BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-2022-academic-track BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-WMUGYQ@talks.osgeo.org DTSTART;TZID=CET:20220824T110000 DTEND;TZID=CET:20220824T113000 DESCRIPTION:This work wants to highlight the results obtained during the BE EMS (Monitoring Bee Diversity in Natural System) project\, which the main goal was to answer the following question: Which biotic and abiotic indica tors of floral and nesting resources best reflect the diversity of bee spe cies and community composition in the Israeli natural environment? To this end\, the research was oriented towards the cost-effectiveness analysis o f new aerial geomatics techniques and classical ground-based methods for c ollecting the indicators described above\, based only on open-source softw are for data analysis.\nThe study involved the Israeli and Italian teams\, focusing the attention on two complementary study systems in central Isra el\, the Alexander Stream National Park\, an area undergoing an ecological restoration project in a sandy ecosystem\, and the Judean foothills area\ , to the South of Tel Aviv. In each study system\, different surveys of be es\, flowers\, nesting substrates and soil\, using classical field measure ment methods have been conducted. Simultaneously\, an integrated aerophoto grammetric survey\, acquiring different spectral responses of the land sur face by means of Uncrewed Aerial Vehicle (UAV) imaging systems have been p erformed. The multispectral sensors have provided surface spectral respons e out of the visible spectrum\, while the photogrammetric reconstruction h as provided three-dimensional information. Thanks to Artificial Intelligen ce (AI) algorithms and the richness of the data acquired\, a methodology f or Land Cover Classification has been developed. The results obtained by g round surveys and advanced geomatics tools have been compared and overlapp ed. The results are promising and show a good fit between the two approach es\, and high performance of the geomatics tools in providing valuable eco logical data. \nThe acquisition of the indicators identified in the plann ing phase took place through several measurement campaigns conducted in th e period between February 2020 and April 2020 located in two areas of inte rest in the Israeli territory. A total of 934 and 543 wild bees were colle cted in the two systems under study\, respectively. From a geomatics point of view\, 8 flights were carried out in the Alexander Stream National Par k on 24 February 2020\, acquiring approximately 65 GB of 8-bit multi-band images in tiff format. In the Judean foothills area\, 11 flights were carr ied out on 26 February 2020\, obtaining approximately 77 GB of tiff images . In addition\, in order to obtain a correctly geo-referenced 3D model\, a total of 54 Ground Control Points (GCPs) were acquired\, of which 27 in A lexander Stream National Park and 27 in the Judean foothills\, with a mult i-frequency\, multi-constellation GNSS geodetic receiver in RTK mode.\nOn the basis of the technical requirements necessary to carry out this projec t\, very high-resolution digital maps (orthophotos\, digital terrain model s - DTM) were produced through the application and optimisation of photogr ammetric and structure from motion (SfM) processes performed on data from different imaging sensors (RGB\, multispectral)\, considering only open-so urce software. Therefore\, considering all the previously defined aspects\ , in order to plan the data acquisition\, the research group defined the f light parameters and instruments\, both in terms of aircraft and sensors t o be installed onboard\, necessary to achieve the project objectives. All the digital cartography generated has been defined in the Israeli referenc e system\, i.e. in WGS84 with UTM 36N cartographic projection. The results are shown in Tables 2 and 3 for the Alexander Stream National Park and th e Judean foothills\, respectively.\nThe production of very high scale digi tal cartography allowed the extraction of the necessary data for training the proposed Artificial Intelligence model. These data were applied to two different approaches for automatic land cover classification. The first a pproach was based on unsupervised classification at the pixel level\, whil e the second approach is based on object classification\, i.e. vector poly gons describing the boundaries of a real object. The algorithms operate di fferently on these two types of data\, in fact in the pixel-based approach they are applied at the level of the single pixel\, while in the object-o riented approach they are applied to groups of homogenous pixels for a giv en feature. The implementation of all the training and validation phases o f the proposed models was based on Python programming language using open libraries for data management (shapely\, raster) and learning (sk-learn). The segmentation of the input data is fundamental in the approach in order to define the objects to be classified\, therefore the Orpheo Toolbox lib rary was applied. The object-oriented approach was applied for the Alexand er Stream National Park site while the pixel-based approach was applied on the Judean Foothills area.\nFor pixel-based classification\, a clusteriza tion algorithm\, KMeans\, was used in an unsupervised manner. The KMeans a lgorithm clusters the data by attempting to separate the samples into n gr oups of equal variances\, minimising a criterion known as within-cluster s um-of-squares. The algorithm was optimised through a trial-and-error proce dure that led to the identification of initialisation parameters. For the object-oriented classification\, we proceeded to apply automatic segmentat ion algorithms based on the analysis of multi-band spectral variability. I n particular\, the algorithm used is the Large-Scale Mean-Shift segmentati on algorithm\, which produces a clustered image in which the pixels around a target pixel that present similar behaviour from both the spatial and s pectral points of view are grouped together. Then\, the procedure vectoriz es these clusters and the operator associates a label to each of them for the generation of the dataset. After subdividing the data into training an d testing elements\, the Random Forest algorithm was used for both approac hes and proved to be the most effective in performing the assigned task. T he classification results were carried out using different validation metr ics such as Precision\, Recall\, F1 score\, etc. that will be presented. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:The use of open source software for monitoring bee diversity in nat ural systems: the BEEMS project - Paolo Dabove URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/WMUGYQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-DD38UJ@talks.osgeo.org DTSTART;TZID=CET:20220824T110000 DTEND;TZID=CET:20220824T113000 DESCRIPTION:Environmental changes can have different causes on local level (e.g. soil sealing) as well as on global level (e.g. climate change). To d etect these changes and to find patterns in the reasons for them it is nec essary to collect broad environmental data\, temporally and spatially. The reto citizens can play an essential role to collect the data (Goodchild\, 2007). In particular\, we developed a system which enables citizens to mon itor the occurrence and distribution of birds and provides the collected d ata to the public in order that both researchers and citizens can derive c onclusions from them. With our automated approach we want to support other citizen science solutions like eBird (Sullivan et al. 2014) where contrib utors manually report their sightings.\n\nTherefore\, we built a prototypi cal bird feeder equipped with several sensors and the infrastructure to pr ocess the data collected by the feeder.\nThe feeder is easy to reproduce a t a reasonable price by following an open available manual. This allows an yone to build the feeder on their own\, enabling a large distribution at m any locations. The feeder automatically detects when a bird is visiting it \, takes an image of the bird\, determines the species and connects the ob servation with environmental data like the temperature or light intensity. All the collected data are published on a developed open access platform. Incorporating other surrounding factors like the proximity of the feeder station to the next forest or a large street allows it to pursue various q uestions regarding the occurrence of birds. One of them might ask\, how do es the immediate environment affect bird abundance? Or do sealed surfaces have a negative effect compared to a flowering garden?\n\nThe developed we atherproof bird feeder is attached with multiple sensors. Thereby the stan dard equipment includes a motion sensor to detect if a bird is currently v isiting the feeder\, a camera to take images of the birds\, a balance to w eigh the birds and a sensor to measure the environment's temperature and a ir pressure. In addition to the standard sensors\, further sensors were te sted with the prototype\, which usefully supplement the monitoring but are not absolutely necessary for the operation of the station. Thus\, a micro phone is suited to record the voice of the birds or generally the surround ing noises. A brightness sensor can be valuable to draw conclusions whethe r birds visit the feeder in relation to light conditions\, or a sensor to measure the air pollution (e.g. PM10) to investigate if the air quality in fluences the bird occurrence. Besides\, the usual camera can be replaced b y an infrared camera to capture animals which visit the feeder at night. T hus\, the station is expandable and customizable depending on the individu al use cases or research questions. \nThe environmental sensor data is con tinuously logged and sent to the open access platform\, whereby the corres ponding interval can be set by the user. Once the motion sensor detects a movement\, the camera recording starts as well as scale and microphone sta rt to store values. As long as the motion sensor detects movement\, camera \, microphone and balance are running. After the movement is finished\, a light-weighted recognition model is used to check whether a bird is depict ed in the images. If this is the case\, all data collected during the move ment\, including the respective environmental data\, will be sent as a pac kage to the open access platform. \n\nIn order to process the data collect ed by the station\, we have developed various methods and software for dat a storage\, analysis and sharing. The data processing is done on a central ized server. Communication with this server is enabled through a RESTful A PI and a website. On the server created entities of the feeders can recei ve environmental data as well as movement packages. When movements are sen t\, the server analyzes the amount of birds and identifies the species wit h artificial intelligence. In addition to the storage\, the server makes t he data available to users in two ways. First\, the data is downloadable a s raw JSON via the API\, which enables others to use it for their own rese arch. Second\, the data is presented nicely on our website\, to make it ea sily inspectable for everyone. However\, not only via our stations a uploa d to the server is possible\, it is also open for the upload of data gathe red by other systems. Further\, it is also possible to upload images of bi rds and receive the represented species. \n\nThe feeder is designed so tha t it can be replicated by anyone. The corresponding instructions will be p ublished shortly. The code to run the station and the server is available via GitHub (https://github.com/CountYourBirds).\n\nMoreover\, different op tions for the validation of the data\, especially the species classificati on\, are implemented. One step is the automatic validation by the sensor v alues or metadata. For instance if a standard camera recognizes a bird but currently it is night (detected by light sensor or time of the day) or th e balance detects nothing\, the observation is discarded. Further validati on can come from actual people. An interface is provided which is used to show people values and especially images recorded with the automatically r ecognized species. The depicted data can be validated to find corrupt sens ors and wipe out mistakes made within the image classification. Additional ly\, the serverside evaluation of the data is supplemented by a validation of the recognized species. It is checked whether it is possible that the species can occur at that geographic region or at that time of the year. \ n\nAs next steps we want to conduct workshops with citizens and experts\, both for putting together the stations as well as evaluating the data and the station itself. In general a strength of our implemented approach is t hat it is easily adaptable to other use cases\, especially to detect other animals. For example with small adaptations to the feeder it could be use d to detect or count different mammals like squirrels or for insects like butterflies and bees. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Multi-Sensor Feeder: Automated and Easy-To-Use Animal Monitoring To ol for Citizens - Jan Stenkamp\, Tom Niers\, Nick Jakuschona URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/DD38UJ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-DNPQLN@talks.osgeo.org DTSTART;TZID=CET:20220824T113000 DTEND;TZID=CET:20220824T120000 DESCRIPTION:Large infrastructure building like the Florence Railway Station designed for high-speed rails requires a proper management of the huge qu antity of waste originating from excavation activities. Such waste amounts require large areas for disposals\, making abandoned areas or exhausted q uarries and mines ideal sites for hosting the excavated wastes. A rectangu lar area of 500x70m delimiting the railway station has been excavated in t wo steps causing the removal of a 10m-thick soil layer per step: the amoun t of construction waste\, as stated in the approved management project by public authorities involved in environmental management plans\, would be u sed for the environmental restoration of an area of 400x350m located near a former exhausted lignite quarry) located in the proximity of the Santa B arbara village near Cavriglia (Arezzo).\nThe Tuscan Regional Environmental Agency (ARPAT) have been involved in monitoring both the terrain transpor tation and disposals’ operations according to the approved management pl an: while the Environmental Evaluation Office (VIA-VAS) was responsible of the waste sampling for further chemical analysis to assess the acceptable waste chemical composition\, the Environmental Regional Information Syste m Office (SIRA) was asked to evaluate volume balancing between all the was te management cycle\, with included: (a) waste extraction from railway sta tion site building\, and (b) waste disposal final destination (exhausted S anta Barbara lignite quarry).\nA phase difference terrestrial LiDAR have b een used in acquiring the 3D point cloud at the railway site at the follow ing stages: (a) initial stage\, before excavation activities’ starting ( b) step 1 stage\, after the first 10m-thick layer excavation (c) step 2 st age\, after completion of excavation works. Various tests have been perfor med to assess the optimal number of scans allowing to obtain the required precision of the final 3D model\, stating from more than 100 scans for the survey for the initial stage to about 50 scans used for (b) and (c) stage surveys. Each survey was referenced by using a local coordinate system ma terialized during the survey\; each target was then referred to the mail l ocal reference system used in the railway station project by the owner’s topographers with a total station.\nScan alignment and 3D cleaning (point clouds and meshes) was made using proprietary licensed software\, while v olume differences evaluation was made in QGIS 3.x environment\; as for the scan alignment phases (3D point clouds’ alignment)\, available open-sou rce platforms have been tested and evaluated. Both scan alignment and 3D c leaning\, while manually executed\, have been proven to be time-consuming operations even using proprietary-licensed sofware.\nAs for Santa Barbara quarry\, an initial RTK RPAS was performed before grass and small vegetati on removal to evaluate the potential of RPAS over the survey area in speed ing survey activities with respect to the terrestrial LiDAR in open areas: the RPAS survey demonstrates that such technology\, compared to terrestri al LiDAR surveys in open areas\, is much less time consuming in both acqui sition and processing time\, making it the best choice for surveys in open areas where extreme precision (sub-centimetric) is not required.\nDue to work progresses in filling activities at Santa Barbara site\, i.e. the par tial cleaning of one of the defined file subareas followed by its filling with excavated wastes\, the initial stage of waste filling was surveyed in five times\, one for each of the defined subareas. Each subarea survey\, due to its limited dimensions (120x50m)\, instrumentation and personnel av ailability at the time of vegetation cleaning\, have been surveyed with th e terrestrial LiDAR\, while for the final survey over the whole quarry are a the RTK RPAS have been used. LiDAR surveys have been processed according to the tested methods in railway station surveys processing\; RPAS RTK su rvey data\, too\, have been processed with the same proprietary software. Terrestrial LiDAR surveys were referenced in a local coordinate system by using a local coordinate system materialized during the survey\; each targ et was then referred to the mail local reference system used in the quarry filling project by the owner’s topographers. The RPAS models\, in geogr aphic coordinates\, were then aligned to the terrestrial LiDAR surveys in order to evaluate the global waste volume disposed onsite.\nComparison ope rations between excavated volume at the railway station site and the exhau sted lignite site showed good agreement\, even by taking into account a st andard transformation coefficient between compact soil and excavated waste . Terrestrial LiDAR scan alignment and point clouds/mesh cleaning activiti es have been very time-consuming\, so that usage of automatic processing p ipelines testing by mean of open source software is in progress: environme ntal monitoring of waste management over large areas\, if properly managed with (semi) automatic processing\, would be less time-consuming stating t o actual testing. National projects of large processing infrastructure ( ‘Mirror Copernicus’) would see a leading role taken by our office in b uilding a fully-operational prototype of a pipeline for scan alignment and point cloud/mesh processing to evaluate waste extraction in large buildin g sites. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Environmental monitoring management of waste from large excavations due to infrastructure buildings - Ing. Cinzia Licciardello URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/DNPQLN/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-BALEWA@talks.osgeo.org DTSTART;TZID=CET:20220824T113000 DTEND;TZID=CET:20220824T120000 DESCRIPTION:Sea water turbidity is a measure of the amount of light scatter ed by particles in water. It is due to the presence of suspended particles \, which it is operationally defined as the fraction in water with less th an 2 µm in diameter. Plankton can also generate turbidity\, but high turb idity events are dominated by high concentrations of inanimate inorganic p articles. High levels of suspended sediments in coastal regions can occur as consequence of high sediment load from rivers\, from bottom sediment re suspension due to wave actions or due to anthropogenic activities\, such a s dredging operations or bottom resuspension from ship propellants. The in crease of turbidity can determine negative environmental effects both on t he biotic and abiotic marine ecosystem. In highly anthropized coastal mari ne systems\, like harbours\, sediments represent a sink for contaminants a nd resuspension can contribute to propagate pollution to unpolluted areas (Lisi et al.\, 2019).\n\nMany marine water quality monitoring programmes m easure turbidity. Traditional methods (e.g.\, in situ monitoring) offer hi gh accuracy but provide sparse information in space and time. Earth Observ ation (EO) techniques\, on the other hand\, have a potential to provide a comprehensive\, fast and inexpensive monitoring system to observe the biop hysical and biochemical conditions of water bodies (Caballero et al.\, 201 8\; Saberioon et al.\, 2020\; Sagan et al.\, 2020). Hence\, some of the au thors are developing a semi-empirical model for predicting water turbidity by combining Sentinel-2A data and machine learning methods using samples collected along the North Tyrrhenian Sea (Italy). Field data collected at the study site from April 2015 to December 2020 were made available by ARP AL\, even though most of these data refer to low turbidity events.\n\nIn t he framework of this research activity\, Sentinel-2A multispectral optical images\, freely available within the EU Copernicus programme\, are elabor ated. It’s well known that such products are provided at Level-1C (L1C) Top of Atmosphere (TOA) and at Level-2A (L2A) Bottom-Of-Atmosphere (BOA). L2A BOA reflectance products are preferred as they are already corrected f or effects of the atmosphere. However\, the official L2A data are availabl e for wider Europe from March 2018 onwards. \n\nThe necessity to use the c omplete on-site dataset to calibrate the predicting model\, and not only d ata after March 2018\, required the identification of the most appropriate algorithm for atmospheric correction of L1C images relative to study area between 2015 and 2018. \n\nHence\, a comparison between the available L2A BOA product ant the corresponding L1C image corrected in different open s ource environment was performed. In particular\, the free and open source QGIS and GRASS GIS\, and the Sentinel Application Platform (SNAP)\, provid ed by ESA/ESRIN free of charge to the Earth Observation Community\, publis hed under the GPL license and with its sources code available on GitHub\, were used.\n\nBoth image-based method\, i.e. the Dark Object Subtraction ( DOS) method in QGIS\, and physically-based methods\, i.e. the Second Simul ation of Satellite Signal in the Solar Spectrum (6S) method in i.atcorr mo dule of GRASS GIS and the Sen2Cor algorithm inside SNAP\, were applied (La ntzanakis et al.\, 2017). The great advantage of the DOS method is that it focuses only on the spectral and radiometric characteristics of the proce ssed image\, hence it doesn’t require remote or in-situ atmospheric meas urements. But the performed correction doesn’t seem so accurate. Instead \, the physically-based approach requires atmospheric measurements and par ameters\, that are difficult to be identified so to be coherent in space a nd time with the processed image. \n\nThe most complex physical parameter to set is Aerosol Optical Depth (AOD)\, which is a dimensionless parameter related to the amount of aerosol in the vertical column of the atmosphere over the target station. It usually range from 0 to 1\, with values less than 0\,1 that corresponds to a clean atmosphere with high visibility\, an d values higher than 0\,4 that corresponds to hazy atmosphere with very le ss visibility. AOD is spatially and temporally very variable. It can be es timated from AERONET (AErosol RObotic NETwork)\, a federation of ground-ba sed remote sensing aerosol networks with more than 25 years of data. A sta tion which measured the Aerosol Optical Depth at 500 nm at Level 2 (qualit y-assured) at the same time as the scene was taken\, is not always availab le nearby the site under study. Hence the evaluation of AOD variability in time and space was analysed for the area and the events of interest\, so to identify the proper values. Expecially i.atcorr seems very sensitive to the set values of AOD.\n\nOnce the proper method for atmospheric correcti on was identify\, it was applied to the L1C images relative to the collect ed field data from April 2015 to March 2018. Then\, the correlation betwee n the in-site dataset and the individual bands known to be most sensitive to water turbidity\, i.e. blue (B2)\, green (B3)\, red (B4) and near infra red (B8 and B8A) bands\, was analysed\, finding good results for the visib le bands\, and a weak correlation with NIR bands. In addition\, indexes de fined by the ratio between the three visible bands were checked to see whi ch combination could best highlight the turbidity of the water from the Se ntinel-2 images. Preliminary results seem to confirm that the identified E O technique could provide a fast and inexpensive monitoring system to obse rve sea water turbidity along the Northern Tyrrhenian Sea (Italy). DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Sea water turbidity analysis from Sentinel-2 images: atmospheric co rrection and bands correlation - Bianca Federici\, Stefania Magrì URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/BALEWA/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-E8BXZJ@talks.osgeo.org DTSTART;TZID=CET:20220824T120000 DTEND;TZID=CET:20220824T123000 DESCRIPTION:In the last decades the European mountain landscape\, and in pa rticular the Alpine landscape\, has dramatically changed due to social and economic factors (Tattoni et al. 2017).\nThe most visible impact has been the reduction of the population for mid and high altitude villages and th e shrinking of part of the land used for agriculture and grazing. The resu lt is a progressive reduction of pastures and meadows and the expansion of the forested areas. Forest plots become also more compact\, with the loss of ecotones.\nThe study of this phenomenon is important not only to asses s its current impact on the ecological functionality of forest ecosystems including biodiversity and natural hazards\, but also to build future scen arios\, taking into account also the climate change issues. The limit of t he mountain treeline is gradually shifting upwards and the monitoring and modeling of these changes will be crucial to plan future interventions and try to implement effective mitigation plans.\nFor these reasons\, a datas et describing the forest\, meadows and pasture coverage for the Trentino r egion\, in the eastern Italian Alps\, has been created.\nA set of heteroge neous sources has been selected so that maps and images cover the longest possible time span on the whole Trentino region with the same quality\, providing the necessary information to create a LULC (Land Use/Land Cover ) map at least for the forest\, meadows and pasture classes.\nThe dataset covers a time span of more than 160 years\, with automatic or semi-automat ic digitization of historical maps and the LULC classification from aerial images.\nThe first set of maps includes historical maps from 1859 to 1936 \, with an additional map from 1992 which was not available in digital for mat and has been digitized for this project: Austrian Cadastral (1859\, 13 297 sheets\, scale 1:1440)\, Cesare Battisti’s map of forest density pub lished in his atlas ”Il Trentino. Economic Statistical Illustration” ( 1915\, single sheet\, 1: 500 000)\, Italian Kingdom Forest Map (IKMF) (193 6\, 47\, 1:100 000) and Map of the potential forest area and treeline (199 2\, 98\, 1:50 000). A new procedure has been developed to automatically ex tract LULC classes from these maps\, combining GRASS and R for the segment ation\, classification and filtering with the Object Based Image Analysis (OBIA) approach. Two new GRASS modules used in this procedure have been cr eated and made available as add-ons on the official repository (Gobbi et a l.\, 2019)..\nThe second set of maps are aerial images\, covering the time span from 1954 to 2015. The four sets which differ for mean scale\, numb er of bands\, resolution and datum: "Volo GAI" (1954\, 130 images\, mean s cale 1:35 000\, B/W\, resolution 2m\, Rome40 datum)\, "Volo Italia" (1994\ , 230\, 1:10 000\, B/W\, 1m\, Rome40)\, "Volo TerraItaly" (2006\, 250\, 1: 5 000\, RGB+IR\, 0.5m\, Rome40) and "Volo AGEA" (2015\, 850\, 1:5 000\, RG B+IR\, 0.2m\, ETRS89). The "Volo GAI" imagery set has been ortho-rectified using GRASS\, images in the other sets were already ortho photos.\nThe ae rial images were classified with OBIA to create LULC maps\, with particula r focus on forest\, meadows and pasture classes. The same training segment s were used across the 4 sets and the custom classification procedure has been scripted. The number of training segments ranges from 1831 for the 20 15 dataset and 2572 for the 1954 imagery set.\nThe evaluation of the resul ts of the classification for all the maps and images has been carried out with a proportional stratified random sampling approach. A procedure has b een scripted in GRASS to select 750 sampling points\, distributed in each stratum (LULC class) proportionally to the area of the class. The resultin g points have been manually labeled and used to assess the classification and filtering (where present) accuracy.[c]\nFor the historical maps\, the application of the custom filtering procedure has increased the accuracy f rom a minimum value of 67% (for the IMF map) to 93% (for the same map)\, w ith a maximum of 98% for the cadaster map.\nFor the imagery datasets the a ccuracy (percentage of points correctly classified) was between 93% and 94 %\, with the latter value corresponding to the higher resolution 2015 imag ery dataset. Higher accuracy\, up to 95% was obtained for the forest class \, which is the main focus of the study.\nThe analysis of selected landsca pe metrics provided preliminary results about the forest distribution and pattern of recolonization during the last 180 years.\nA comparison between the capabilities of FOSS4G available systems for landscape metrics was p erformed to evaluate the best analysis tools (Zatelli et al. 2019).\nFin ally\, these time series of LULC coverage were used to create future scena rios for the forest evolution in a test area of Trentino in the next 85 ye ars\, using both the Markov chain and the Agent Based Modeling approaches with GAMA (Taillandier et al. 2018).\nGiven the large number of maps invol ved\, the great flexibility provided by FOSS for spatial analysis\, such a s GRASS\, R\, QGIS and GAMA and the possibility of scripting all the opera tions have played a pivotal role in the success both in the creation of th e dataset and in the extraction and modeling of land use changes.\nThe dev elopment of new GRASS add-on modules\, based on the scripts created during this study\, is planned. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Modeling of forest landscape evolution at regional level: a FOSS4G approach - Paolo Zatelli URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/E8BXZJ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-3MCN3C@talks.osgeo.org DTSTART;TZID=CET:20220824T120000 DTEND;TZID=CET:20220824T120500 DESCRIPTION:Nowadays\, the need is felt to create a sustainable and inclusi ve urban environment accessible to all\, which requires a people-centered urban planning approach. Along with alleviating environmental problems and minimizing traffic congestion\, the public transit system serves as a mea ns of providing equal access (Rossetti\, et al.\, 2020). This paper attemp ts to re-evaluate the isochrones prepared to access public transport stops particularly transit nodes across Noida city using a GIS-based approach a nd Open Data Kit (ODK) approach. The isochrones predict the time to reach any area from a transport node like a transit station based on the shortes t path model\, however\, not all roads and streets offer equal access to a ll (Lei & Church\, 2010). In this study\, macro-built-environment attribut es responsible to increase pedestrian distance length and time to reach me tro stations were identified using a GIS-based approach by integrating lan d-use and transportation data. However\, the micro-built environment attri butes like pedestrian behaviour\, preference of travel modes\, and purpose and frequency of transit trips made by the transit users were gathered by conducting metro station user surveys using the ODK app linked with its O DK aggregate server. The ongoing transport and urban planning methods hard ly give any importance to the understanding origin and destination to reac h important places with more ease and mobility (Bhatt & Minal\, 2022). Urb an researchers have not investigated any studies to evaluate equal accessi bility to effectively and smoothly use public transit services by easily a ccessing transit stations (Yang\, et al.\, 2019). The objective of this st udy is to map the pedestrian permeability and impermeability by categorizi ng roads and streets around identified transit nodes as public\, private\, and non-accessible by all. The prime function of accessibility is to link people with activities through linkages (Lei & Church\, 2010). In this st udy\, the travel modes particularly considered were on foot\, non-motorize d (cycle and rickshaw)\, shared e-rickshaw\, bus service\, and dropped off by two-wheeler and four-wheeler. A stratified random sampling technique w as adopted to calculate the sample size of 12 existing elevated metro stat ions in Noida on the Blue transit line of DMRC. A self-administered questi onnaire was used to conduct metro station surveys using ODK mobile app at identified 12 metro stations in Noida starting from Noida Sector 15 until the last Noida Electronic city (NEC) station. A sample size of 1% of the a verage transit ridership data\, collected from DMRC for each station was t aken to achieve a 95% confidence level. However\, some stations like the G olf course and stations following Noida city center operational since the year 2019 have low ridership below 5000 persons per day. For these station s\, 2% sample size of the average ridership data was considered to achieve a similar confidence level. Following this step\, the existing land use e ncircling individual metro stations within a radius of 800 meters was dema rcated from Noida Master Plan (NMP) 2031. Based on static master plan land -use distribution\, the stations were categorized as residential\, non-res idential\, mixed-use and transport hubs. The questionnaire contained 31 it ems which included various aspects including the usual purpose of metro tr ips made\, employment status\, availability of the driving license\, house hold size\, current city living in\, number of cars available in the house hold\, car availability during the transit trip made\, building topology o f the transit user\, and number of floors. Most importantly\, the preferre d travel mode to reach the nearest metro station\, frequency of trips made in a week\, metro travel pattern changed in past six months\, particularl y due to COVID-19 restrictions\, and the reason to opt for transit service s. Other allied questions were distance and time to reach the metro statio n using different travel modes. The survey results were downloaded for the ODK aggregate server and converted to an excel sheet from CSV file format for its data analysis. In QGIS\, a buffer distance of 800 meters in both directions was marked along the blue line transit corridor with its 12 met ro stations in Noida. Initially\, typically common walkable pedestrian rou tes terminating at individual metro stations were identified with their tr ip origin location in the nearby sector. Based on the farthest location fo und in all the directions encircling the metro station\, walkable sheds we re developed for all the 12 metro stations in Noida. However\, as the stat ions are quite close in between 1 to 2 kilometers\, the walkable sheds ove rlap for some consecutive metro stations. Thereafter\, using Garmin Etrex 10\, all the common routes\, most commonly followed by e-rickshaws as an a lternative to walking were traced. The photographs of the barriers found t hat typically increase the walking distance and time to reach the metro st ations were clicked. Thereafter\, using My Maps an app by Google\, the Gar min tracked routes were fed along with photos imported through the Google Photos app. In the case of Noida\, there exist both planned and unplanned barriers to equal accessibility for all. While\, the gated communities\, l arge super-blocks\, and many Government housing societies are planned barr iers that impede accessibility for all within TOD station areas. The prese nce of urban villages with organic street layouts and narrow incomplete st reets are hardly accessible and force potential transit riders to shift aw ay from walking or rely on e-rickshaws as short to medium-distance travel modes. Finally\, the study proposes a TOD index based on the Space syntax model and distance measurement to categorize roads within the TOD area as public roads\, private roads - accessible by only a few residents\, non-su itable streets - not fit for use. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Examination of Metro Stations Equal Accessibility for All using Ope n Data Kit (ODK) Applications: A Case Study of Noida City in India - Sahil Singh Kapoor URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/3MCN3C/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-3SYURF@talks.osgeo.org DTSTART;TZID=CET:20220824T120500 DTEND;TZID=CET:20220824T121000 DESCRIPTION:Mobility data\, based on global positioning system (GPS) tracki ng\, have been widely used in many areas.  These include analyzing travel patterns\, investigating transport safety and efficiency\, and evaluating travel impacts. Transport Mode Detection (TMD) is an essential factor in understanding mobility within the transport system.  A TMD model assigns a GPS point or a GPS trajectory to a particular transport mode based on th e user's activity and medium of travel [1].  However\, the complexity of the prediction procedure increases with the number of modes that need to b e predicted. For example\, it is comparatively easy to predict whether a u ser is 'static' or 'slow moving' or 'fast moving' but it's hard to predict detailed transport modes such as walk\, bike\, car\, bus\, train\, boat\, etc.  Therefore\, this study proposes a multi-branch deep learning-based TMD model which can predict multi-class transport modes. \n\n\nTwo major challenges need to be addressed in order to generate a state-of-the-art d eep learning model.\n\n\nThe first is to prepare ground-truth data. There are insufficient open-sourced ground-truth data available for transport mo des in Japan. Hence\, we proposed a transport mode label generation approa ch using snorkel [2]. Snorkel is a weakly supervised labeling function\, a first-of-its-kind system that enables users to train state-of-the-art mod els without hand labeling any training data. Instead\, experts write label ing functions that express arbitrary heuristics based on the logic that ca n be drawn from understanding the data and the physical actions they repre sent. In this study\, we used snorkel for generating the ground truth data for transport mode. Initially\, we considered publicly available road net works\, railway networks\, bus routes\, etc.\, for creating road\, bus\, t rain labels by overlaying GPS points on these transportation networks. How ever\, there are multiple occasions where the road\, bus\, and train class es overlap each other\, especially in a city region. Hence\, we introduced a boolean (True/False) based soft-labeling function\, where the same GPS point might have multiple True values for road or railway. \n\n\nSecond\, we derived mobility-related features from the raw GPS data. Raw GPS raw d ata is typically composed of latitude\, longitude\, and timestamps. The ra w GPS data were used to generate point-level features such as speed\, spee d difference\, acceleration\, acceleration difference\, initial bearing\, and bearing difference. Apart from that\, we also generated trajectory lev el features such as average speed and average acceleration.  \n\n\nTrans portation network-based soft-labeling and other mobility features are used to define labeling functions in the snorkel. These label functions are us ed to create true ground truths using a generative machine learning model with a portion of the GPS data. The generated labels (walk\, cycle\, bus\, car\, train\, boat/ship) were then used to train the proposed deep learni ng model.  To construct the model we opted to use two branches where raw GPS latitude and longitude values were used in one and the derived mobilit y features are used in the other. We used 3 fully-connected hidden layers for raw GPS data (lat/lon) and 4 fully connected hidden layers for mobilit y features.  Features derived from the two branches are concatenated. Fur ther\, 3 fully connected hidden layers and softmax cross-entropy were used as a loss function. The proposed deep learning model has 108\,614 trainab le parameters and Adam is used as an optimizer. This particular two-branch model structure achieves better accuracy as it combines raw data as well as the derived mobility features in the network.  An example of the benef it from this approach benefit can be the network's ability to relate GPS c oordinates with road driving classes\, thus inherently inferring that loca tion as on a road.  Note\, many of these inferences that improve classifi cation accuracy are possible via dramatically more advanced pre-processing to build out additional features.  However\, that approach is more time- consuming and could never catch all the potential inferences that an unbia sed set of deep learning layers can inherently extract.\n \n\n\nWe evalua ted the trained model's effectiveness in two ways.  We compared the resul ts against the popular XGBoost classifier\, with our model producing over 5% higher accuracy for the benchmark Geolife dataset [3].  Moreover\, we collected smartphone-based GPS trajectories for multiple modes of transpor tation collected by testers in Bengaluru\, India\, and Tokyo\, Japan.  Wi th this new absolute ground truth data\, we compared the resulting predict ed classes between operating system-provided activity classifications\, th e above XGBoost model\, and our own.  Our experiments show promising resu lts with improved accuracy and increases in number of labeled data points.   Of key note is that the iOS [4] and android [5]in-built activity recogn ition tools provide the 'automotive' class as a single class\, while our p roposed model efficiently distinguishes automotive classes as car\, bus\, and train with improved accuracy. This work completely depends upon Free a nd Open Source Solutions (FOSS) for data preparation\, mobility feature ge neration\, deep learning model training\, and big data computing. That inc ludes various geospatial libraries such as geopandas\, shapely\, rtree\, w eakly label generation platform snorkel\, deep learning platform tensorflo w\,  keras\, big-data computing platforms such as pyspark\, hadoop\, hive \, etc. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Multi-branch Deep learning Based Transport Mode Detection using Wea kly Supervised Labels - Vinayaraj URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/3SYURF/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-3PT37K@talks.osgeo.org DTSTART;TZID=CET:20220824T121000 DTEND;TZID=CET:20220824T121500 DESCRIPTION:AN OPEN-SOURCE MOBILE GEOSPATIAL PLATFORM FOR AGRICULTURAL LAND SCAPE MAPPING: A CASE STUDY OF WALL-TO-WALL FARM SYSTEMS MAPPING IN TONGA\ n\nPacific Island Countries (PICs) such as Tonga rely on landscape service s to support communities and livelihoods in particular smallholder and com mercial agriculture. However\, PICs are increasingly vulnerable to climati c and environmental shocks and stressors such as increasing cyclone occurr ence and landscape conversion. Spatially explicit\, timely\, and accurate datasets on agricultural and other land use at the community scale are an important source of information for land use policy development\, landscap e management\, disaster response and recovery\, and climate-smart sustaina ble development. However\, such datasets are not available or readily acce ssible to stakeholders engaged in landscape management in PICs. Household surveys\, participatory GIS (PGIS)\, and remote sensing are approaches tha t have previously been used to capture community-scale landscape uses in P ICs\; however\, these approaches are challenged by data collection and man agement burdens\, mismatched scales\, timely integration of databases and data streams\, aligning system requirements with local needs\, and various socio-technical issues associated with developing and deploying applicati ons in new domains. Such data collection approaches only provide single ti me-steps representations of landscape uses and fail to capture the highly dynamic and spatially diverse nature of PIC landscapes.\n \nWe have addres sed these challenges by developing\, integrating\, and deploying a tool fo r agricultural landscape monitoring at a local scale. This tool is compose d of a stack of open-source geospatial applications and was developed thro ugh a collaboration between Tonga’s Ministry of Agriculture\, Food\, and Forests (MAFF) and researchers from Australian and South Pacific universi ties. We used a formal\, iterative ICT for Development (ICT4D) framework t o engage and co-develop the tool with MAFF and other landscape stakeholder s including community leaders. The ICT4D framework is based on agile metho ds and is made up of five components: context analysis\; needs assessment\ ; use-case and requirements analysis\; sustainability assessment\; and dev elopment\, testing and deploying. The five components provide a framework to ensure that project stakeholders (landscape managers\, developers\, and end-users) consider the range of technical and non-technical factors that will determine successful implementation of an ICT system in a new domain . Here\, the goal was to transition from infrequent paper-based and non-sp atial surveying of farms to develop a spatial data infrastructure that sup ports coordinated large-team farm mapping\, data syncing and storage\, and geospatial data analysis and reporting that aligns with MAFFs needs\, and guides landscape management actions. \n \nHere\, we describe our team’s experience in applying the iterative ICT4D framework. We present the deve lopment activities associated with successive phases of the project and re flect on the advantages (and constraints) this framework offers for develo ping open-source geospatial applications for deployment in new domains wit h a low-resource context. Initially\, we introduce the qualitative fact fi nding\, context analysis\, and needs assessment to ascertain and distil MA FF’s needs for geospatial data and applications. Then\, we present sever al stages of application design\, development\, testing\, and refinement i n various MAFF data collection and reporting campaigns\, which enabled ana lysis and the detailed specification of the requirements for the agricultu ral landscape monitoring tool. This includes work on developing initial pr ototype applications\, implementing small-scale vanilla and land utilisati on surveys\, and finally an island-wide wall-to-wall crop survey with a la rge team of field data collectors. \n \nFinally\, we present the system ar chitecture and a case study of the final iteration of the tool deployed fo r Tonga’s country-wide wall-to-wall farm system survey completed by MAFF in 2021. The final iteration of the tool was composed of a stack of open- source geospatial tools including QField for mobile mapping and data colle ction\, QFieldCloud for user authentication and data syncing\, and newly d eveloped\, open-source geospatial data visualisation\, analysis\, and repo rting applications. This case study discusses: (1) how a team of over 40 d ata collectors were able to work collaboratively to build up a database co mprising records from over 11\,000 farms using QField and QFieldCloud\; an d (2) how custom applications developed in this project enable visualisati on of this data on web maps and automated reporting to inform policy devel opment and landscape decision making by MAFF. We also illustrate the crit ical role the tool and the crop survey information collected in 2021 playe d in assisting MAFF’s recovery efforts in the aftermath of the Hunga Ton ga–Hunga Ha'apai submarine volcano explosion and subsequent tsunami whic h impacted heavily on Tonga’s main island of Tongatapu in January 2022. We also discuss the potential challenges in delivering the tool to other l ow-resource jurisdictions in the South Pacific including issues related to data dissemination\, privacy and security\; user management\; technical a nd financial sustainability\; scalability\; training and knowledge transfe r\; and creating and fostering a community of open-source developers and u sers in PICs. The success of our case study demonstrates the importance o f stakeholder engagement in an iterative ICT 4D development framework\, an d the great potential that open-source geospatial tools such as QGIS\, QFi eld\, and QFieldSync can play in agricultural landscape management and dis aster response in PICs. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:An Open-Source Mobile Geospatial Platform for Agricultural Landscap e Mapping: A Case Study Of Wall-to-Wall Farm Systems Mapping in Tonga - Ke vin Davies URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/3PT37K/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-79KBL9@talks.osgeo.org DTSTART;TZID=CET:20220824T121500 DTEND;TZID=CET:20220824T122000 DESCRIPTION:The Architecture\, Engineering and Construction (AEC) cluster i n Portugal is a knowledge and competence aggregator platform in the constr uction sector\, which aims to promote business competitiveness through res earch activities. The AEC sector is characterized by high levels of compet ence\, being able to respond to the rigor and demands of international sta ndards imposed by the world's leading industry leaders. This sector repres ents 2.2% of the Portuguese GDP (Gross Domestic Product) and aims to incre ase exports and qualified employment\, promote international visibility\, strengthen skills\, promote the creation of partnerships and cooperation. It is of central importance for the national economy\, being responsible f or 350 thousand jobs\, 19.9 billion euros in turnover and 4.5 billion euro s in exports.\nCurrently\, the AEC sector lacks tools for digitization\, b eing forced to resort to proprietary software and closed file formats with complex and highly expensive licensing models. Thus\, new opportunities a rise for the creation of tools\, resources and knowledge that promote the renewal and impetus of the entire sector. It is intended to develop a proj ect that can respond to these needs\, through the development of a 3D Digi tal Twin platform\, a digital replica of a physical entity\, for collabora tive representation and editing of Industry Foundation Classes (IFC) docum ents\, a format with a digital description of construction industry assets \, applied to the Building Information Modelling (BIM) methodology\, a met hodology that aims to concentrate project information in a digital way in order to incorporate all its participants.\nPresently\, the AEC sector in Portugal is in need of products\, processes and services that enhance coll aboration\, training and innovation in the sector to compete in the global market. Dependence on proprietary tools for the design of AEC projects\, using closed file formats with complex and highly expensive licensing mode ls\, reveals an opportunity for new tools that can interact collaborativel y based on standards driven by the AEC community itself. Thus\, with the a rising of the IFC interoperable format for the BIM methodology\, it is nec essary to develop universal access tools\, capable of representing and edi ting 3D BIM models through a collaborative\, interactive and real-time pla tform following international standards and specifications. These market o pportunities\, associated with the digital transition of the AEC sector\, attract multiple stakeholders to this new reality\, and produces a set of initiatives that foster competitiveness within the sector. However\, the a pproach to these problems remains faithful to a matrix of classic solution s in this sector\, in which the offer of products is based on highly speci alized desktop software\, and whose use of the IFC standards is only to gu arantee operability with other systems\, through the import/export of IFC files and not their direct handling. Hence\, opportunities emerge for comp anies that seek to develop functionalities that permit handling IFC files through Web tools with universal access that allow responding to the requi rements and needs of the AEC sector. What currently makes it difficult to create these Web applications\, in addition to the various problems alread y mentioned\, is the complexity associated with reading and writing IFC fi les\, and performance issues related to Web applications\, mainly caused b y poor memory management when reading or writing large documents.\nThis ar ticle focuses on the development of an innovative web platform based on th e concept of Digital Twin with 3D digital models\, permanently updated\, e nhanced with information\, which allows the knowledge of the back-office w ork reality\, in real-time\, and facilitates the decision-making process\, including the constant adjustment of resource allocation and development of production processes under optimal conditions\, and the overlap of the virtual domain with the physical domain.\nThe developed solution is a cros s-platform web application that has all base requirements of a standard BI M platform\, plus georeferencing of IFC documents using orthophoto maps an d free access to base cartography. From this innovative web platform\, in addition to the representation of 3D IFC files\, analysis of metadata\, an d measuring elements of the 3D Model\, these also stand out as technologic al breakthroughs of the sector: handling large files\, georeferencing the 3D Model with map support\, performing operations in the model for example adding elements from external catalogues\, removing elements\, updating e lements geometry (rotation\, scaling\, translation) and metadata (properti es and relations).\nThe present work sets itself as a part of a mobilizing project\, called REV@CONSTRUCTION. REV@CONSTRUCTION is a project financed by Portugal 2020 that aims to develop solutions for a digital transformat ion of companies in the AEC sector\, with the aim of promoting its competi tiveness and sustainable growth\, as well as strategic dissemination with the sector at European level.\nThis project established as its main goal\, finding and providing digital solutions to the industry in the AEC sector \, involving architects and designers\, and construction and project manag ement companies. Its R&D activities are directed to the development of dig ital tools linked to key aspects of the sector\, but also to develop and p rovide the methodological bases of standardization\, organization\, and ma nagement of information necessary for the implementation of the BIM and Di gital Twin methodology in Portugal\, applied to the construction and asset management of buildings and infrastructures.\nIn this context\, the focus is on defining bases of standardization across the sector\, such as the e stablishment of construction technical information\, information models an d BIM object libraries\, and a national cost database\, which aim to elimi nate the barrier between methodology and modelling tools and the productio n of standard specifications\, thus leading to increased effectiveness and efficiency of construction processes and reduction of public context cost s\, by standardizing practices and procedures throughout the various stage s of the construction process.\nThrough these integrative\, cross-cutting\ , and structuring initiatives\, the group of 22 entities involved in this project\, from companies to universities or reference research institutes in Portugal\, want to mobilize the AEC sector for the Digital Revolution. This project has an investment of 8.2 million euros being financed by Port ugal 2020. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Digital Twin 3D Platform for collaborative representation and editi ng of IFC documents applied to the BIM methodology - João Pedro Silva URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/79KBL9/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-9CN7WY@talks.osgeo.org DTSTART;TZID=CET:20220824T122000 DTEND;TZID=CET:20220824T122500 DESCRIPTION:Information and communication technology (ICT) is mainly applie d to finance\, telecommunications\, and public sectors. However\, since th e early 2010s\, there have been efforts to apply ICT to various fields suc h as aerospace\, life science\, energy\, and automobiles. Recently\, artif icial intelligence and big data technologies have also been applied in the aerospace field\, among others. In the field of aerospace\, earth observa tion attracts the most interest. \n\nRecently\, the number of satellites f or earth observation is increasing every year. As small satellites can be manufactured at low cost\, the number of small satellite constellation for temporal and spatial resolution is increasing. urban change detection\, d isaster monitoring\, and traffic analysis are typical applications. These applications will be increasing more.\n\nWhen performing earth observation using satellite images\, it must process a large amount of satellite info rmation in real time and analyze satellite images with artificial intellig ence. These studies are being conducted in a variety of ways\, and especia lly\, the area of interest in this study is processing technology for stor ing and retrieving massive satellite images.\n\nAs a technology for handli ng massive satellite information\, a multi-dimensional array database play s an important role. Representative examples based on open source software are Rasdaman and SciDB.\n\nIn 2018\, we started developing KIWI-Sat\, a s ystem for processing and analyzing massive satellite information for espec ially supporting Korean satellite images such as KOMPSAT-2\, KOMPSAT-3\, K OMPSAT-5 etc.\n\nKIWI-Sat supports GeoTiff\, HDF 5\, and JP2 which are mai n data types of representative satellite images. It is mainly being develo ped to support Korean satellite images KOMPSAT-2\, KOMPSAT-3\, KOMPSAT-3A\ , KOMPSAT-5\, GOCI\, etc. also\, overseas satellite images such as Sentine l 1A\, Sentinel 1B\, Sentinel 2B\, SPOT\, and PlanetScope also be supporte d. Because KIWI-Sat supports main data type’s satellite images\, Other s atellite images can be easily supported.\n\nKIWI-Sat is being developed us ing a number of open source software such as Rasdaman\, Pytorch\, Django\, Mapbox etc. First\, it uses Rasdaman to process massive Raster-based sate llite information\, Pytorch to process AI inference module\, and Django an d Mapbox for visualizing Satellite images and overlay etc. KIWI-Sat is mai nly composed of five subsystems: 1) 'K-SDA(KIWI-Sat Data Access)'\, which processes satellite information based on Rasdaman\, an array database\, 2) 'K-SAA(KIWI-Sat AI Analysis)'\, which is in charge of AI-based satellite image analysis\, 3) 'K-SVI(KIWI-Sat Visualization)' that visualizes satell ite image based on map\, 4) 'K-SUT(KIWI-Sat Utility)' that provides utilit y functions such as system resource monitoring and upload/download of orig inal satellite image\, 5) OpenAPI for satellite information access There i s a 'K-SOA(KIWI-Sat OpenApi)' in charge.\n\nIn this paper\, KIWI-Sat will be mainly explained focusing on 'K-SDA'\, which processes massive satellit e images\, and 'K-SAA'\, which analyzes satellite images with artificial i ntelligence.\n\nFirst\, K-SDA was developed using Rasdaman. In the early o f development K-SDA\, SciDB was used as database system\, but as SciDB fin ished its open source policy. We adopted Rasdaman\, a representative open source\, as alternative database for processing satellite images. For this \, function and performance comparison was performed\, and Rasdaman has a good performance in the uploading original satellite to storing in Databas e. The search performance for the selected area was excellent in SciDB. Co nsidering the real-time performance of recent satellite image analysis\, i t was confirmed that Rasdaman has an advantage. In processing satellite im ages\, the multidimensional array database can handle storage and retrieva l of array units with theSQL standard\, and has many advantages as it supp orts massive raster data.\n\nSecond\, it is 'K-SAA' that is linked to the satellite image-based AI module. KIWI-Sat has a structure that can be easi ly linked with the inference code and parameters of the previously develop ed artificial intelligence module. It works with the 'K-SVI' stage by rece iving the satellite image obtained from 'K-SDA' and transmitting the obtai ned result to the DJango web framework by executing the inference code.\n\ nWe installed a demo system at KARI to test and improve Kiwi-sat. KIWI-Sat was installed in Ubunt 20.04 LTS\, and it stores KOMPSAT-2\, KOMPSAT-3\, KOMPSAT-3A\, KOMPSAT-5\, Planetscope satellite images to search for ROI fo r optical images and SAR images\, AI interlocking\, etc. Several tests are being carried out.\n\nIn this paper\, we demonstrate the result of interw orking of the KIWI-Sat and two AI technologies. First\, for the KOMPSAT-3 satellite image\, which is an optical image\, the object detection result was compared to the object detection result to which the satellite image s uper-resolution technology was not applied and the object detection result to which the super-resolution technology was applied. This result shows t hat it is confirmed that the super-resolution technology can improve the o bject detection performance of satellite images. The test satellite image is KOMPSAT-3\, and the area is Hong Kong.\n\nThe second shows the results of detecting the water system in the KOMPSAT-5 satellite image\, which is a SAR satellite image.\n\nSo far\, we have looked at KIWI-Sat\, a large-ca pacity satellite image processing and analysis system. KIWI-Sat is current ly under development. It plans to develop technologies for searching betwe en multiple satellite images and extending satellite images to multiple no des. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Development of Multi-dimensional Array Database Based Massive Satel lite Information Processing and Analysis System: KIWI-Sat - HAN SANG HYUCK URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/9CN7WY/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-BJNURB@talks.osgeo.org DTSTART;TZID=CET:20220824T141500 DTEND;TZID=CET:20220824T144500 DESCRIPTION:Synthetic Aperture Radar (SAR) backscatter is adept in differen tiating standing water\, due to its low signal\, compared to most non-wate r surface cover types. However\, the temporal transition from non-water to water is critical to identifying floods. Hence objects with permanent or seasonally low backscatter become ambiguous and difficult to classify. TU Wien's flood mapping algorithm utilizes a pixel-wise harmonic model derive d from SAR datacube (DC) (Bauer-Marschallinger et al.\, in review) to acco unt for these patterns. Designed to be applied globally in near real-time\ , our method applies Bayes inference on SAR data in VV polarization. In th is method\, the harmonic model generates the non-flooded reference distrib ution\, which we then compare against flooded distribution to delineate fl oods within incoming Sentinel-1 IW GRDH scenes. \n\n\n\nIn the harmonic m odeling\, we estimate each location's expected temporal backscatter variat ion\, explained by a set of Fourier coefficients. Following recommendation s in the literature\, a seven coefficient formulation was adopted (Schlaff er et al.\,2015) and is here on referred to as our harmonic parameters (HP ARs). The HPARs include the backscatter mean and three iterations of two s inusoidal coefficients. This model acts as a smoothened proxy for the meas urements in the time series\, thus allowing for a seasonally varying backs catter reference to be estimated for any given day-of-year \n\n\n\nHoweve r\, generating the harmonic model at a global scale and with high resoluti on presents significant logistical and technical challenges. Therefore\, h armonic modeling of remotely sensed time series is often performed on spec ialized infrastructures (Liu et al.\, 2020)\, such as Google Earth Engine (GEE) (Gorelick et al.\, 2017) or other highly customized setups (Zhou et al.\, 2021)\, where the pixel-wise analysis of multi-year data requires we ll-defined I/O\, data chunking\, and parallelization strategies to generat e the HPARs in reasonable time and cost. While harmonic analysis is not ne w\, to our knowledge\, production and application at a global scale using dense SAR time series have yet to be implemented\, let alone operationally utilized.\n\n\n\nTo prepare for global near real-time flood mapping effor t\, HPARs were systematically computed using a global DC organizing Sentin el-1 IW GRDH datasets. In the DC structure\, individual images are stacked \, allowing for data abstraction in the spatial and temporal dimensions\, making it ideal for time-series analysis. However\, for this abstraction t o be realized\, a rich set of software solutions is needed to implement th e 3-dimensional data model.\n\n\n\nIn this contribution\, we present our S AR DC software stack and its utilization to compute the aforementioned glo bal harmonic parameters. We show a set of portable and loosely coupled Pyt hon packages developed by the TU Wien GEO Microwave Remote Sensing (MRS) g roup capable of forming a global data cube with minimal overhead from indi vidual satellite images. The stack includes\, among others\, open-source p ackages for:\n\n high-level data cube abstraction - yeoda\,\n spatial refe rence and hierarchical tiling system - Equi7Grid\,\n lower-level data acce ss and I/O – veranda\,\n spatial file and folder-based naming and handli ng – geopathfinder\, and\n product tagging and metadata management – m edali.\n\n\n\n\nThe detailed description of the preprocessing and storage infrastructure used for this global DC is outlined by Wagner et al.\, 2021 . Here\, we focus on the software interfaces. Moreover\, given the preproc essed datasets\, the logical entry point is through yeoda\, which abstract s well-structured Earth observation data collections as a DC\, making high -level operations such as filtering and data loading possible. This level of abstraction is supported by the other components of the software stack\ , which addresses the organization and lower-level access to the individua l files.\n\n\n\nIn a nutshell\, the DC is simply a collection of raster da tasets in GeoTIFF file format co-registered in the same reference grid. To deploy for large-scale operations\, a well-defined grid system is require d to deal with high-resolution raster data. A tiling system fulfilling thi s requirement is the Equi7grid\, based on seven equidistant continental pr ojections found to minimize raster image oversampling. Interacting with th is tiling system on an abstract level is possible via our in-house develop ed Equi7Grid package. The tiling system follows a hierarchy of directories to manage the datasets on disk. Moreover\, for individual files\, a prede fined naming convention is applied to indicate spatial\, temporal\, and an cillary information from product metadata that becomes transparent to yeod a. This setup of customizable file naming schemes is easily managed throug h the geopathfinder package.\n\n\n\nThe actual HPARs processing task was s ubdivided into multiple High-Performance Computing (HPC) jobs on the Vienn a Scientific Cluster (VSC) based on this tiling hierarchy. For the tempora l domain modeling to work\, data is further split into manageable chunks. Thus only one tile per HPC node was allocated. Hence\, yeoda was used to f ilter the DC down to the tile level\, which was further reduced to a two-y ear period. From there\, a three-dimensional array formatted backscatter m easurements were generated by veranda from the image stack on disk.\n\n\n\ nDue to the depth of the DC\, further segmentation and parallelization wer e required at this level. Thus\, pixel-based parallelization was done usin g Numba to handle the core least squares estimation of the measurements ve rsus a day-of-year array derived from image timestamps. Veranda is again u sed for the output operation to encode and write the HPARs to individual f iles. Data quality checks\, and metadata encoding\, done via medali\, cap the processing. In this manner\, the HPAR product themselves can be abstra cted similarly as a DC and simplify subsequent flood mapping computations. \n\n\n\nWith the HPAR product\, the Sentinel-1 time series is seasonally m odeled and condensed to a fraction of the size of the original global DC. While for now\, it is exclusively used to allow our flood monitoring workf low to work globally in near-real-time\, other potential applications incl ude seasonal water and vegetation analysis. Moreover\, with the software s tack used to compute and subsequently access\, this product can easily be deployed on different platforms with little to no overhead\, allowing repr oducible DC analysis. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Computing Global Harmonic Parameters for Flood Mapping using TU Wie n’s SAR Datacube Software Stack. - Mark Edwin Tupas URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/BJNURB/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-EFPDRZ@talks.osgeo.org DTSTART;TZID=CET:20220824T141500 DTEND;TZID=CET:20220824T144500 DESCRIPTION:Remote sensing via orbiting satellite sensors is today a common tool to monitor numerous aspects related to the Earth surface and the atm osphere. The amount of data from imagery have increased tremendously since the past years\, due to the increase in space missions and public and pri vate agencies involved in this activity. A lot of these data are open-data \, and academics and stakeholders in general can freely download and use i t for any type of application. The bottle-neck is often not data availabil ity anymore\, but the processing resources and tools to analyse it. In par ticular multi-temporal analysis requires stacks of images thus digital spa ce for storage and processing workflows that are tested and validated. Pro cessing image by image is often not a viable approach anymore. Several sol utions have been created to support centralized and automated processing o f multiple images. Software as a service (SaaS) is becoming more common am ong users. The most popular to this day is probably Google Earth Engine (G EE)\, which gives users Petabytes of data at their fingertips\, access to processing resources and an interface that provides a large number of tool s for data processing via Javascript or Python programming environments (G orelick et al.\, 2017). What took before days if not months can now be run in a few minutes or hours. GEE is available and free for academics as of today\, but it must be noted that it is not to be taken for granted in the future. Other initiatives such as Copernicus RUS project that has closed at the end of 2021 also provided access to data (Copernicus data) and comp uting resources\, to promote uptake of Copernicus data via educational and research activities. \n\nMoving towards SaaS solutions usually requires a provider that puts software on the cloud and a channel\, usually a web po rtal\, for accessing data and tools. The R CRAN programming environment ha s all the “ingredients” that are needed to create such SaaS in a local machine or on a server. We propose and discuss here a solution\, called I nforSAT\, that was created ad hoc for centralizing satellite imagery proce ssing\, taking advantage of a remote server with multiple processors and t hus also parallel processing solutions. The R Shiny package was used for c onnecting online widgets for user interaction with R tools for specific pr ocessing of imagery that is done via other specific packages. To this date only Sentinel-2 Level 2C data are considered\, but the system is scalable to other sensors and processing levels. The tools that are available to t his day are focused on multi-temporal analysis\, to support the academic c ommunity involved in particular in vegetation analysis\, whose phenology h as notable changes inter- and intra-annually. The tools are available via a web portal to reach research teams that are not so familiar to satellite image analysis\, to allow simplified extraction of multi-temporal data fr om Sentinel-2 images. Figure 1 shows the interface and figure 2 the result of extracting a boxplot of vegetation index values over a specific time w indow. \n\nAll image data are stored in a user-defined folder on the serve r\, and a script checks weekly (or at other user-defined intervals) for ne w Sentinel-2 images and automatically downloads them and stores metadata i n an R list structure. The metadata stores image paths\, bands and also hi stograms of values for each band\, to use for defining color-stretching pa rameters during image rendering on the browser. Regarding visualization\, users can render real-color and false-color composites defining their own band combinations\, and can also create and raster layer with the values o f common vegetation indices or define their own index by providing an equa tion on the interface (see Figure 1). The images to be rendered on the use r browser are processed on-the-fly from the original JPEG2000 format\, als o for calculating the index raster and the color-composites. Each index ra ster is calculated every time the user re-draws actively the raster\, by s ampling the original image with points that correspond to the screen pixel s\, reprojected from screen coordinates to image coordinates. Depending on the screen size and on the area\, these are around one million points\, t hat are then converted to an image and rendered on screen with a fixed sca le that depends on the expected minimum and maximum values of the index (e .g. for the normalized vegetation index that would be between -1 and 1) or a scale that automatically stretches between the 10th and 90th percentile of the frequency distribution of the real values. The color-composites ar e automatically drawn at any scale using the intrinsic overviews for each Sentinel-2 band that are present from the JPEG2000 format. Regarding multi -temporal analysis\, users can define one ore more polygons over the area and for each polygon extract single pixel values (digital numbers – DN) and aggregated zonal statistics for each and all available images in a few seconds\, with or without using parallel processing mode. Users can downl oad the multi-temporal data\, i.e. the DN values\, in table format for fur ther analysis. The table is in long format and has a column with a timesta mp\, one with polygon ID and one column for each band with values. In both visualization and multi-temporal analysis\, users can decide a threshold for masking according to cloud and snow probability\, which are available products from the sen2cor processing of Sentinel-2 to level 2C. In the nea r future this solution will be integrated in an R package\, allowing users to easily download\, install and replicate their own portal locally or in their own server. Code is available on Github at https://github.com/fpir otti/inforsat DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:InforSAT: an online Sentinel-2 multi-temporal analysis toolset usin g R CRAN - Francesco Pirotti URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/EFPDRZ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-YXFEWL@talks.osgeo.org DTSTART;TZID=CET:20220824T144500 DTEND;TZID=CET:20220824T151500 DESCRIPTION:Approach and concepts\n3D-georeferenced historical pictures hav e a high potential for the analysis of different landscape features such a s melting glaciers\, the effects of urbanization or natural hazards. Moreo ver\, historical pictures have a higher temporal and spatial resolution th an satellite imagery and therefore allow for analyses that go farther back in time. A 3D georeferenced picture can for instance be combined with a d igital terrain model (DTM) and other reference data to calculate the exact footprint of the picture and to generate a list of visible toponyms that can be used to find pictures of a specific place or region.\n\nThe utiliza tion of historical pictures is unfortunately still difficult: 1. historica l pictures need to be digitized 2. collections are often spread across sev eral places in different archives and collections 3. metadata is often not available. In the ongoing open-source project Smapshot (Produit et al. (2 018)\, https://smapshot.heig-vd.ch/) over 150’000 digitized historical p ictures have been georeferenced in 3D by more than 700 participants. In th e web-platform Smapshot a participant can georeference a picture using mon oplotting (Bozzini et al\, 2012): ground-control-points (GCP) are digitize d both in the historical picture and in a virtual globe that displays rece ntly updated data. These GCP allow for the calculation of the exact positi on from where the picture has been taken (3D point) and the three angles t hat define the direction of view: roll\, pitch and yaw. Once the position and the direction of view has been calculated a footprint of the picture i s generated using a DTM.\n\nResults\nIn order to make the pictures and the metadata from Smapshot available to the public\, an open API for 3D-geore ferenced historical pictures has been created. The goal was to offer free access to all the data in the Smapshot database and to allow for different types of queries such as retrieving the footprints of the photos\, fetchi ng metadata for a picture (e.g. owner\, title\, date\, x/y/z position and roll\, pitch\, yaw angles) or retrieving photos that are within a certain range from a specific point. \n\nThis API was built in NodeJS (https://nod ejs.org/) with a PostgreSQL/PostGIS (https://www.postgresql.org/\, https:/ /postgis.net/) database and python code for the georeferencing algorithm. The API is a REST API fully documented using the OpenAPI specification. Th e API project has been open-sourced and specific test-suites have been put in place to ensure quality and to allow community contribution with confi dence.\n\nOne challenge for the establishment of this API was standardizat ion: Today there are several standards for the definition of metadata in p ictures such as the IIIF (https://iiif.io/) or the Dublin-Core (https://ww w.dublincore.org/) standards. These standards however have limited support for geospatial data. On the other hand\, spatial standards poorly support pictures that are oriented in 3D. The glTF standard (https://www.khronos. org/gltf/) is one example and there is also a recent initiative from the O GC called GeoPose (https://www.ogc.org/projects/groups/geoposeswg) which f ormalizes a standard to define a 6DoF pose everywhere on Earth including a position and orientation in 3D.\n\n\nReasons why it should be considered\ n3D georeferenced images are increasingly used by several projects that do cument change over time\; e.g. within the field of digital humanities even paintings can be considered for 3D georeferencing and differences between the real world and the painted world can give room for analysis and inter pretation. Another use-case is the creation of geovisualization-applicatio ns that show the contents of historical pictures in 3D and that enable a u ser to compare its contents to the real world (e.g. augmented or virtual r eality applications)\n\nFurthermore in the context of climate change\, pic tures and paintings document change and deliver evidence. Image processing techniques can be used to automatically detect features (machine learning ) and if several pictures are available for one region (but taken from sli ghtly different viewpoints) 3D features can be generated.\n\nThe open API for 3D georeferenced historical pictures makes these types of analyses eas ier and opens up the data for a larger public. It also becomes possible to implement other solutions that utilize the data directly - e.g. for displ aying historical pictures in a 3rd party web page or for implementing mach ine-learning processes that automatically download pictures and metadata i n order to recognize features and places.\n\nThe results of the project ar e also an important input for standardization activities that aim at estab lishing standards in the context of georeferenced pictures and their metad ata.\n\nAn important perspective of the project is the establishment of an infrastructure for 3D-georeferenced pictures that can be deployed on a na tional or international level and that also offers the possibility to push new data (e.g. pictures) in the database.\n\nThis work is of interest for researchers who want to utilize and analyze 3D georeferenced historical i magery and for people who want to establish open API’s to give access to data that is relevant for research.\n\nBibliography\nBozzini\, C.\, Coned era\, M.\, Krebs\, P.\, 2012. A new monoplotting tool to extract georefere nced vector data and orthorectified raster data from oblique non-metric ph otographs. International Journal of Heritage in the Digital Era 1 (3)\, 49 9–518.\n\nProduit\, T.\, Blanc\, N.\, Composto\, S.\, Ingensand\, J.\, F urhoff\, L\, 2018. Crowdsourcing the georeferencing of historical pictures . Proceedings of the Free and Open Source Software for Geospatial (FOSS4G ) Conference. Guimarães\, Portugal. 2018-07\n\nSource code : https://gith ub.com/MediaComem/smapshot-api DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:An open API for 3D-georeferenced historical pictures - Jens Ingensa nd\, Nicolas Blanc URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/YXFEWL/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-CNHGNH@talks.osgeo.org DTSTART;TZID=CET:20220824T144500 DTEND;TZID=CET:20220824T151500 DESCRIPTION:The collection of georeferenced information on the field has be come an established and popular practice allowing professionals\, voluntee rs and citizens to contribute to mapping objects or reporting events. Fiel d data collection is essential to a variety of domains [1] including many scientific and humanistic disciplines\, humanitarian and rescues operation s\, locations reviews and professional engineering surveys\, to mention a few. \n\nThe spread of mobile devices that can record location coordinates \, media and features while on the go (and share them through the web) is primarily accountable for such diffusion. As a result\, a number of mobile apps and software frameworks (both proprietary as well as free and open-s ource) have been developed and released to perform data collection on the field. Most of these frameworks allow developers or data collection promot ers to customize collection forms according to the characteristics of each collection task and manage both users and records through web dashboards or database management systems. From the user perspective\, mobile client apps are available to access selectively the collection forms and contribu te to the data collection on the field using mainly smartphones or tables. Focusing on general-purpose data collection software frameworks\, some of the most popular free and open-source solutions are the Open Data Kit (OD K\, https://opendatakit.org)\, the KoBoToolbox (https://www.kobotoolbox.or g) and Epicollect (https://five.epicollect.net). Other relevant examples o f free and open-source frameworks implementing a more technical approach t o field data collection are e.g. QField (https://docs.qfield.org)\, Geopap arazzi and SMASH (https://www.geopaparazzi.org). Proprietary or pay-per-us e solutions developed by major GIS firms are also available on the market but they were not considered in the benchmark analysis carried out in this work.\n\nThe outlined free and open-source software frameworks provide cl ient and server modules and both web and mobile apps to support the full d evelopment of field data collection projects [2]. From the developer (or d ata collection promoter) perspective\, the adoption of such frameworks is facilitated by the availability of open APIs\, interfaces and dashboards t o generate\, deploy and manage collection forms\, users and records. Neve rtheless\, limitations connected to the final user experience are common t o most of them. On one hand\, mobile client apps are not always available or optimized for all mobile OS\, therefore preventing their use on the fie ld by a significant number of potential contributors [3]. This is the case e.g. of the ODK on iOS devices. On the other hand\, each of these framewo rks requires the installation of a specific mobile app on the user's devic e. This operation may not represent a significant obstacle to the contribu tion to specific data collection projects by very active or committed user s. However\, it might inhibit the contribution of occasional users who may not be willing to install additional software on their device for sporadi c mapping of objects or event reporting [4].\n\nIn view of the above\, thi s work presents the Geo Collector Bot\, an alternative free and open-sourc e software toolkit to empower field data collection projects avoiding the development and/or the installation of a specific mobile app on contributo rs' devices. The Geo Collector Bot is a configurable Telegram-based chatbo t enabling the dispatching of data collection forms that can be activated and filled through Telegram chats. It consists of a backend application wr itten in Typescript and running on Node.js. As the supporting mobile clien t\, the Telegram app is exploited thus enabling a large number of users to contribute\, even sporadically\, to data collection projects (potentially every Telegram user\; 550 million monthly active users as of July 2021). The Geo Collector Bot is released under MIT License and source code\, docu mentation and a demo are available on GitLab (https://gitlab.com/geolab.co mo/geocollectorbot).\n\nThe Geo Collector Bot works as a standard Telegram Bot. To collect the data\, the Bot asks a series of questions to the user including location coordinates\, media\, textual annotations\, multiple-c hoice checkboxes\, etc. and persists the answers to a spatial database. Th e questions flow can be customized by editing a JSON configuration file. L ocal deployments of the system are facilitated by the provision of a Docke r container (https://hub.docker.com/r/geolabpolimi/geo-collector-bot). \n\ nThe Geo Collector Bot has been developed in the framework of the INSUBRI .PARKS project (https://insubriparksturismo.eu)\, funded by the Interreg C o-operation Programme 2014-2020. This project aims at increasing the touri sm attractiveness of the Insubria region (between Northern Italy and South ern Switzerland) through the provision of infrastructure as well as integr ated marketing and management strategies for the Insubria natural parks. T he Bot represents a component of the virtual infrastructure supporting the project. It was originally designed to allow both parks visitors and mana gers to easily collect and share geolocated records on parks status and fe edback on points of interest. However\, the ultimate goal of the presented work is to provide an open and general-purpose data collection software f ramework suitable for multi-purpose applications.\n\nThe current version o f the Geo Collector Bot still does not provide dedicated backend supportin g modules for both collection tasks and records management. To that end\, the development of a web control dashboard is planned and it will be inclu ded in the stack of the Geo Collector Bot Docker container as an auxiliary component. The Bot has been tested using a PostgreSQL-PostGIS database. A dditional configuration options to plug other spatial database systems is planned as well in the future development of this work. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Geo Collector Bot: A Telegram-based open toolkit to support field d ata collection - Daniele Oxoli URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/CNHGNH/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-EHNYXK@talks.osgeo.org DTSTART;TZID=CET:20220824T151500 DTEND;TZID=CET:20220824T154500 DESCRIPTION:As geospatial data continuously grows in complexity and size\, the application of Machine Learning and Data Mining techniques to geospati al analysis is increasingly more essential to solve real-world problems. A lthough\, in the last two decades\, the research in this field produced in novative methodologies\, they are usually applied to specific situations a nd not automatized for general use. Therefore\, both generalization and in tegration of these methods with Geographic Information Systems (GIS) are n ecessary to support researchers and organizations in data exploration\, pa ttern recognition\, and prediction in the various applications of geospati al data. The lack of machine learning tools in GIS is especially clear for what concerns unsupervised learning and clustering. The most used cluster ing plugins in QGIS [1] contain few functionalities beyond the basic appli cation of a clustering algorithm. \n\nIn this work we present Cluster Anal ysis\, a Python plugin that we developed for the open-source software QGIS and offers functionalities for the entire clustering process: from (i) pr e-processing\, to (ii) feature selection and clustering\, and finally (iii ) cluster evaluation. Our tool provides different improvements from the cu rrent solutions available in QGIS\, but also in other widespread GIS softw are. The expanded features provided by the plugin allow the users to deal with some of the most challenging problems of geospatial data\, such as hi gh dimensional space\, poor quality of data\, and large size of data.\n\nI n particular\, the plugin is composed of three main sections:\n\n- feature cleaning: This part aims to provide some options to reduce the dimensiona lity of the dataset by removing the attributes that are most likely bad fo r the clustering process. This is important to achieve better results and faster execution time\, avoiding the problems of clustering in high dimens ionality. The first filter removes the features that are correlated above a user-defined threshold\, since highly correlated features usually provid e redundant information and can lead to overweight of some characteristics . The other two filters identify the attributes with constant values for a ll the data points or with few outliers differentiating from them. These t ypes of features don’t provide any valuable information and can worsen t he performance of clustering. To identify quasi-constant features\, we use two different parameters introduced in the function NearZeroVar() from th e Caret package developed for R [2]: the ratio between the two most freque nt values and the number of unique values relative to the number of sample s.\n\n\n- clustering: This section is used to perform clustering on the ch osen vector layer. First of all\, the user needs to select the features to use in the process. It is possible to select the features both manually a nd automatically. The automatic feature selection is done using an entropy -based algorithm [3] presented in two versions with different computationa l complexities. The currently available algorithms for clustering are K-Me ans and Agglomerative Hierarchical\, and the users can select the one that best suits their needs. Before performing clustering\, the plugin offers the possibility to scale the datasets with standardization or normalizatio n\, and to plot two different graphs to facilitate the choice of the numbe r of clusters.\n\n\n- evaluation: In this section we show all the experime nts carried out in the current session\, with a recap of the settings and performances of the experiments and the possibility to save and load them with text files. To evaluate the quality of the experiments we calculate t wo indexes and the comparisons among experiments on the same dataset. The indexes are the internal metrics Silhouette coefficient and Davies-Bouldin index. To directly compare the clusters formed by two or more experiments we compute the score [4]\, which evaluates how many couples of data point s are grouped together in all of the experiments or in none of them. Every experiment completed in the current session can be stored in a text file\ , and the experiments saved in previous sessions can be loaded in the plug in and are shown in the evaluation section along with the other ones.\n\nO ne of the major challenges during development has been allowing most of th e functionalities on large datasets as well\, both from the point of view of the number of samples and the number of dimensions. To achieve this\, w e also implemented algorithm options with good time complexities\, as in t he case of entropy with sampling and K-Means. Moreover\, for all the data storage and manipulation done in the system\, we use the data structures a nd functions provided by the libraries pandas and NumPy to guarantee high performance.\n\nAnother important objective of the research is the accessi bility and ease of use of the plugin since the general user of GIS is ofte n lacking a machine learning and computer science background. To guarantee this\, the User Interface is simple and self-explanatory\, and each secti on contains a brief guide to explain all the functionalities. Furthermore\ , some algorithm parameters that cannot be modified via the interface are stored in an external configuration file\, and can be modified via this. T his is done to avoid confusing the less experienced users.\n\nAlong with t he implementation\, the research is integrated with a considerable experim ental phase\, both during and after the development phase. This phase is e ssential to highlight both the potential of the plugin and its limitations in real-world scenarios. The great volume of experiments is conducted on data about the city of Milan\, describing social-demographics\, urban and climatic characteristics and with different granularities (ranging from le ss than 100 data points to almost 70000\, and with a large number of numer ical attributes\, up to 109). Overall\, the experimental phase shows good and adequate flexibility of the plugin\, and outlines the possibilities fo r future developments that can be provided also by the QGIS community\, gi ven the open-source nature of the project.\n\nThe stable version of the pl ugin is available on the QGIS Python Plugins Repository (https://plugins.q gis.org/plugins/Cluster-Analysis-plugin-main/) while the development versi on as well as documentation are available on GitHub (https://github.com/fo lini96/Cluster-Analysis-plugin). DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Cluster Analysis: a comprehensive and versatile QGIS plugin for pat tern recognition in geospatial data - Andrea Folini URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/EHNYXK/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-7LNNUU@talks.osgeo.org DTSTART;TZID=CET:20220824T151500 DTEND;TZID=CET:20220824T152000 DESCRIPTION:The area of agricultural land for food production is limited an d is constantly decreasing both in the world and in Bosnia and Herzegovina . According to the National Action Plan in Bosnia and Herzegovina (B&H)\, up to 1\,600 ha of land are lost annually (NEAP BiH 2003). The prevention of degradation and sustainably controlled land use should be the most impo rtant parts of the land protection policy of every country and the local c ommunity. In order for this policy to be implemented properly\, relevant i ndicators of the state of land resources are necessary (Predic et al. 2021 ). According to the Law on Agricultural Land of the Republic of Srpska\, m unicipalities and cities are obliged to prepare a planning document “Gro undwork for Agricultural Land Protection\, Use and Restructuring (The grou ndwork)”. The Groundwork is made according to the FAO (Food and Agricult ure Organization) model which consists of an inventory of land and climate resources\, agro-ecological zoning\, and economic-ecological zoning. With GIS modeling of existing data (pedology\, digital elevation model\, clima te data\,...) new relevant data were created (bonity\, agro-ecological zon ing\, suitability of cultivation…). It is intended for municipal authori ties in decisions making in the process of land use and protection. GIS la yer of the current condition of land cover and land use (hereinafter LC/LU ) is one of the most important GIS layers for creating Groundwork. It is necessary to make a precise GIS layer on a large scale in order to obtain relevant data on agricultural land and land use. The most precise method o f making LC/LU is manual mapping of LC/LU classes with orthophotos and hig h-resolution satellite images combined with field verification. The critic al point of this method is that it is time consuming. On the other hand\, "free" land cover data is available\, such as Corine Land Cover (hereinaf ter CLC)\, OpenStreetMap\,... In this paper\, using free open source progr ams\, a comparison of two sets of data representing land cover was perform ed: manually vectorized data with an orthophoto image of LC/LU and CLC. Th e aim of this paper is to determine the relevance of CLC data for the need s of land use planning at the level of administrative units in B&H. The st udy area is the municipality of Laktaši with an area of 38807 ha for whic h the LC/LU was created in 2018 at the same time as the CLC for B&H. The f irst phase of the comparison is the synchronization of LC/LU-CLC classific ations. LC/LU classification is The Land Cover Classification System\, (FA O LCCS\, 2000) which is modifiable for the conditions of B&H. Both the LC/ LU and the CLC classifications consist of classes divided into three level s. The main difference between LC/LU and CLC is that the LC/LU classificat ion is primarily intended for the detailed identification of agricultural land. The LC/LU nomenclature is dominated by classes that represent agricu ltural land both in terms of land cover and in terms of use (18 out of a t otal of 36 classes). The smallest mapped area in LC/LU depends on the sign ificance of a LC/LU class. For example\, for the arable land class\, it is 0.5 ha\, and for the permanent crops class\, it is 0.1 ha. The main reaso n is the fragmentation of properties in B&H (85% is dominated by less than 0.5 ha plots). Unlike the CLC classification\, which discusses artificial surfaces in great detail and has 11 classes in the third level (111 Conti nuous urban fabric\,..\, 121 Industrial or commercial units\,…\,142 Spor t and leisure facilities)\, LC/LU classification has only 2 classes for ar tificial surfaces: Built up and Built up dominates. In this class\, the mi nimum mapped area is 0.025 ha because it is necessary to accurately separa te land areas that are temporarily or permanently lost to agriculture. Reg ardless of the above differences\, it is possible to synchronize LC/LU and CLC classifications through third level classes. In the study area (Lakta ši municipality) LC/LU GIS layer contains 23 out of 36 LC/LU classes (107 07 polygons)\, and CLC layer 16 out of 44 classes (177 polygons). In the study area\, the CLC classification did not recognize 11 classes of LC/LU\ , of which 8 classes are precisely characterized by agricultural areas (gr eenhouses\, vineyards\, nurseries\, meadows…). The entire process of com paring and analyzing data was performed using QGIS with the support of the Python programming language. Using QGIS\, the union of LC/LU and CLC poly gons (14044 polygons) was performed. Using the Python programming language \, an error matrix was created and the parameters of the quality of land c over maps were recalculated (Bratic et al.\, 2020). The obtained results s how the accuracy of CLC with respect to LC/LU reference. Although the over all accuracy is 70%\, the class-level results are showing that during the creation of CLC layers\, a significant part of non-agricultural areas was marked as agricultural classes. For example\, 19.4% LC/LU forest class and 42.4% LC/LU class built up\, in CLC were mapped as arable dominated class . From the above example\, in the studied area\, a significalntly larger a rea of agricultural land was present in relataion to the actual state. Aft er analyzing the results\, it was concluded that the CLC in the studied ar ea is not a sufficiently precise GIS basis for agricultural land use plann ing at the local level. However\, it can be a good starting point for maki ng of LC/LU\, which would significantly shorten the time of its creating. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Analysis of Free and open Land Cover maps for agricultural land use planning at the local level - StefanJovanovic URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/7LNNUU/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-9KGNRA@talks.osgeo.org DTSTART;TZID=CET:20220824T152000 DTEND;TZID=CET:20220824T152500 DESCRIPTION:Japan's open infrastructure map development using OpenStreetMap was triggered by the Great East Japan Earthquake in 2011\, which led to a widespread understanding of the activity\, and by the end of September 20 19\, more than 35\,000 unique users had made some kind of contribution\, a nd the data is still being updated daily. The data is still being updated daily. In addition\, the Mapillary project (Juhász and Hochmair\, 2016\; Mahabir et al.\, 2020) which started in April 2014\, is a location-based l andscape photo-sharing service that\, like OSM\, is crowdsourced and allow s users to post photos of places around the world\, not just on roads.\n\n This activity has started to spread in Asia\, especially in Japan\, where the number of contributors and the number of photos taken is rapidly incr easing (Ma et al.\, 2020). These voluntary crowdsourcing activities are a great incentive to work on the creation of micro-scale road data\, especia lly those that cannot be maintained or updated by public agencies. On the other hand\, most of the research on Mapillary to date has been concerned with technical methodologies\, such as the study of ground object extracti on based on deep learning of images using Mapillary data\, and approaches such as local comparison of data generated by contributors\, as is commonl y done in OSM research\, have not made much progress. This study was condu cted in September 2014.\n\nIn this study\, we obtained about 41.7 million log data through the Search Images API of Mapillary API ver3 taken in Japa n from September 2014 to September 2019. Then\, together with the line dat a of OSM roads at the same point in time\, the maintenance status of Mapil lary and OSM road data in municipal units in Japan was spatially analyzed mainly with QGIS\, considering the time series and user trends. The data f or the entire country of Japan to be analyzed is so huge that it is diffic ult to perform spatial analysis with the basic database (PostGIS)\, so we tried to add various attributes that can be analyzed spatially in QGIS by converting the data to FlatGeobuf format\, which has been attracting atten tion recently. We also tried to add various attributes that can be analyze d spatially in QGIS. The added attributes include the administrative name of the local government in Japan\, and the type\, version\, last editor\, and date of data update of the road in the nearest vicinity of the taking photo point (maximum search radius set to 50m) from the OSM dump file obt ained separately.\n\nSome of the results of the analysis are as follows. T he number of unique contributors who participated in the maintenance of Ma pillary data for five years across Japan was about 1500\, and it was found that the top 20 users generated about 90% of the data. The top three cont ributors each shared more than 5 million images. The number of contributor s involved in the OSM road data as a comparison of user participation is a bout 4\,800\, suggesting that Mapillary data is generated by about 1/3 of the users compared to OSM.\n\nWe extracted the major contributors for each of the 1\,700 municipalities in Japan and found that about 50 users were involved. Although the Mapillary data in Japan is supported by a smaller n umber of contributors than the OSM data\, we succeeded in bringing to ligh t the image of contributors in each region by analyzing the data on a micr o-regional basis.\n\nIn terms of the number of Mapillary images taken and their spatial characteristics\, the number of images taken on major roads (equivalent to OSM's highway = trunk or primary) in non-urban areas in the Tohoku region (especially Fukushima Prefecture: approximately 6 million i mages\, Iwate Prefecture: about 4 million images) and Kansai region (Kyoto Prefecture: about 3 million images) is outstanding\, while the number of images taken on sidewalks (highway = sidewalk) in the metropolitan areas o f Tokyo and Osaka is low. In the metropolitan areas of Tokyo and Osaka\, t he data developed to supplement the OSM data for sidewalks (highway=path\, footway\, unclassified) and other road types that exist in reality but ar e not well-developed in the OSM data. In the paper\, we plan to describe t he local activities in Fukushima Prefecture and Kyoto City\, where Mapilla ry activities are particularly active\, in addition to comparisons at the national and municipal levels. In this paper\, we also focus on the tempor al transition of data maintenance. In this paper\, we also focus on the te mporal transition of data maintenance. Specifically\, we analyzed the rela tionship between OSM data and the points where Mapillary images were taken using time series clustering.\n\n　This study is a multifaceted spatial analysis of long-term photography logs through Mapillary and the first stu dy to reveal macro trends across Japan as well as more local trends in com bination with attributes of road data from municipalities and OSM. In addi tion\, by using distributed processing methods such as tiling technology a nd FlatGeobuf to obtain a large dataset of more than 41 million POIs (Poin ts of Interests) from APIs and analyze the data spatially in QGIS\, we wer e able to process the data without requiring a large-scale server. This is also a significant achievement. Finally\, since the Mapillary log data us ed for the analysis is large-scale\, we are planning to provide both archi ved data and spatially aggregated GIS data.\n\nSpatially aggregated from M apillary POI data (41 765 634) for all of Japan used in the analysis\, we are providing both FlatGeobuf format data per municipal-level (232.2 M B\; 32 attribute values) and per 1-km grid-level (126.5 MB\; 20 attribute values)\, via a Github repository: \nhttps://github.com/tossetolab/mapilla ry-analysis-japan. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Analysis of the spatiotemporal accumulation process of Mapillary da ta and its relationship with OSM road data: A case study in Japan - Toshik azu Seto URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/9KGNRA/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-CA3TGA@talks.osgeo.org DTSTART;TZID=CET:20220824T152500 DTEND;TZID=CET:20220824T153000 DESCRIPTION:Soil erosion is a major global land degradation threat. Improvi ng knowledge of the probable future rates of soil erosion\, accelerated by human activity and climate change\, is one of the most decisive factors w hen it comes to making decisions about conservation policies and for earth -system modelers seeking to reduce uncertainty on global predictions [1].\ n\nIn this context\, the use of remote-sensing based methods for soil eros ion assessment has been increasing in recent years thanks to the availabil ity of free access satellite data\, and it has repeatedly proven to be suc cessful [2\, 3]. Accurate information about it is\, however\, usually know n only at the local scale and based on limited field campaigns. Its applic ation to the Arctic presents a number of challenges\, due to peculiar soil s with short growing periods\, winter storms\, wind\, and frequent cloud a nd snow cover. However\, the benefits of applying these techniques would b e especially valuable in arctic areas\, where ground local information can be hard to obtain due to hardly accessible roads and lands.\n\nHere we pr opose a hybrid solution\, which uses ground truth samples to calibrate the processed remote images over a specific area\, to then automate the analy sis for larger\, less accessible areas. This solution is being developed f or soil erosion studies of Iceland specifically\, using Sentinel 2 satelli te data combined with local assessment data from Iceland’s Soil Conserva tion Services department\, Landgræðslan. Their historical data is more e xtensive than usual\, since they are the oldest soil erosion department in the world.\n\nAvailable data includes parameters of bare ground cover\, w hich can be calculated from satellite images alone\, after using informati on from observationally correct areas without vegetation for calibration\; Icelandic soil profiles\, to be analyzed to find how the profile relates to soil erosion intensity\; as well as the parameters of agriculture use a nd arable land data including plant species in cultivated lands.\n\nFor th e training phase we employ a dataset composed of 550 cropped georeferenced and atmospherically corrected Sentinel 2A images [4]\, combined with a Di gital Elevation Model (DEM) of Iceland that allows us to detect slopes whi ch can produce landslides or help erosion to occur. The dataset is labelle d by six degrees of erosion severity\, using measurement points furnished by Landgræðslan. We split it into 2/3 for model training and 1/3 for mod el testing.\n\nThese images are in tiles of 10980x10980 pixels (about 600 MB) and cover an area of approximately 100x100 km2. We can crop the images down to preferred size. They contain multispectral data\, divided up into 12 bands of varying wavelengths\, and a resolution from 10 to 20m. We cou ld add as well some of the 60m bands if necessary. Different band data are combined to create indices which represent or highlight certain features\ , such as vegetation\, soil crusting\, bare soil\, and red edge indices.\n \nElevation data from the Arctic (north of 60°N\, including Iceland) star ted to be openly available since 2015 through the ArcticDEM project. The D EMs are derived from satellite sub-meter stereo imagery\, particularly fro m WorldView 1-3 and GeoEye-1. This information can be used to detect to wh at extent plant growth is reduced at higher heights because of longer snow cover\, shorter growing period and stronger winds on one side. By using t he variation of DEM and building a slope map\, we can see that soil erodes more on steep slopes which leads to a higher likelihood of erosion the st eeper they are.\n\nThe tools for geometric and topographic correction incl ude SNAP (Sentinel application platform)\, Sen2Core\, FLAASH (Fast line-of -sight atmospheric analysis of hypercubes)\, DOS (Dark Object Subtraction) and ATCOR software. This correction reduces effects due to shadows and su rface irregularities and corrects the single-date Sentinel-2 Level-1C Top Of Atmosphere (TOA) products from atmospheric effects in order to deliver a Level-2A Bottom-Of-Atmosphere (BOA) reflectance product.\n\nAfter a prep rocessing technique based on dimensionality reduction in order to avoid ad ding too much noise to the algorithm\, this labelled data is then used to train a Support Vector Machine (SVM) model for classifying each coordinate . We choose the SVM algorithm as a starting point because it is a fast and reliable algorithm that performs well for classification problems with hi gh-dimensional feature spaces such as ours\, and does not require large tr aining sets to achieve high accuracy as other algorithms do (e.g. deep neu ral networks). The output of the model is a set of coordinates\, each with a numeric classification representing soil erosion severity\, and used fo r creating a map of soil erosion severity in a selected area.\n\nThis meth odology has been proven to provide good results\, achieving an overall lan d cover classification accuracy of 94% [5]\, a performance that can be att ributed to the spectral complexity of Sentinel-2 data\, particularly the r ed-edge bands which give room for separability of erosion classes. Low sep arability is a common limitation to the applicability of classification me thods. We address this by using ISODATA and minimum distance methods. Two factors that could affect the accuracy of the delineation of eroded soils using spectral images are the intensity of the soil erosion processes and changes in the spectral characteristics of disturbed soils.\n\nThe researc h described here aims at producing a reliable\, widely applicable and cost -effective method to classify Icelandic soils into different categories of erosion risk\, a proof of concept which\, once engineered\, could be stra ightforwardly expanded and applied to other Arctic areas\, such as Greenla nd and Canada. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Remote mapping of soil erosion risk in Iceland - Daniel Fernández URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/CA3TGA/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-CS9MGW@talks.osgeo.org DTSTART;TZID=CET:20220824T153000 DTEND;TZID=CET:20220824T153500 DESCRIPTION:## Motivation & Contribution\n\nPart of the development of an a nalysis pipeline for mobility studies using GPS data is benchmarking its p erformance on both the raw data accuracy and the analysis pipeline itself. When we started to develop our algorithm for stop and trip classification \, it became clear that we needed a precisely annotated dataset containing accurate stop and trip labels as a ground truth. Apart from validating ou r development\, we wanted to have a reference point for comparing our anal ysis methods with existing libraries.\n\nFor the study\, we planned to equ ip participants with a smartphone to collect movement data in form of GPS and acceleration data for several days in a row. To prolong battery time\, we chose a lower sample frequency. Our special focus was to create ground truth for stop and trip detection algorithms\, hence the annotation focus ed on this.\n\nThrough this manuscript\, we contribute a comprehensive dat aset providing accurate start and end timestamps for stops over 126 days. The STAGA dataset is an unprocessed table of GPS coordinates\, annotated w ith a timestamp\, altitude\, GPS accuracy\, and class label ("stop" or "tr ip"). Each sample labeled as a "stop" further contains the GPS coordinates of the location it's attributed to. The acceleration data is provided as a separate file\, but covers the same time frame and contains a triple (x\ , y\, z) of acceleration sensor readings for each given timestamp\, sample d at 1 Hz. The STAGA~dataset is provided publicly and free to use. We furt her provide the iOS app used to create the diary data for simple stop/trip annotation while on the go. All this is made available under CC BY 4.0.\n \n\n## Method\n\n#### Diary\n\nTo create the dataset\, we first tried a tr aditional diary approach: four researchers were taking notes\, writing dow n addresses and times whenever they stopped. While this provided some firs t samples\, it was a tedious and error-prone process\, since taking notes is impractical in everyday life. Furthermore\, it required looking up the coordinates belonging to each noted address\, which works for clearly defi ned\, urban spaces but can become problematic otherwise\, e.g. in a park o r a rural\, outdoor environment as addresses aren't precise enough here. B ecause of that\, we developed a simple iOS app that helped us annotate our movements. The app contains a map to validate the identified position\, o ne button to start or end a stop\, and a list overview of previously recor ded stops. It captures the GPS position whenever a new stop is started and stores the current time as the start timestamp. When the button is presse d again\, the stop is completed and the current time is stored as the end timestamp. Trips are derived from the intervals between two stops. Even mo re\, the app allows exporting the captured annotations as a CSV file which can be directly used for benchmarking purposes. This way\, we were able t o create a GPS dataset containing precise stop/trip annotations\, together with a reference position of the actual stop location. The diary was reco rded using an Apple iPhone XR.\n\n#### Data Collection\n\nThe device we us ed for the recordings was a ZTE Blade A5 (2019). It was configured to reco rd GPS samples at a minimum accuracy of 25m\, so if the device was unable to obtain a position reading within this radius\, the data point was omitt ed. We sampled data with a frequency of 0.1 Hz and used both network and G PS as sources for determining the position (the smartphone supports A-GPS and GLONASS). It runs Android 9 and is equipped with a 2.600 mAh battery\; during the recording of the dataset\, the battery was always charged befo re the phone shut down.\n\nWhile the dataset contains mostly everyday life \, it also holds small periods of vacation\, travel\, and hiking. Most tri ps were carried out by bike. However\, the dataset contains long periods o f walking\, car traffic\, and train rides as well. While the data was reco rded in two different European countries (mostly urban environments)\, eve rything was rotated and projected into the North Atlantic for privacy prot ection. In the same vein\, all timestamps have been shifted to start on Ja nuary first in the year 2000. However\, none of these changes should affec t the performance of stop and trip detection algorithms\, as the relative temporal and spatial accumulation of GPS records are not changed.\n\n\n## Dataset Statistics\n\nThe dataset contains 122\,808~GPS and 7\,813\,740~ac celerometer records. The recording time spans over 126.65~days. \nThe diar y contains 692~stops and 691~trips.\nThe average (mean) duration of a stop is $240.8min$\; the average trip duration is $22.7min$.\nOn average\, a s top contains $114.0$ GPS samples\; a trip contains $63.5$ GPS samples (mea n).\n\n\n## Discussion & Use-Cases\n\nThis dataset enables researchers to validate the performance of their algorithms that are used to predict stop s and trips from GPS data. It provides a ground truth through careful anno tations over a long period. In particular\, the development of algorithms for stop and trip classification should profit from this dataset as it ena bles accuracy tests in the temporal and spatial domain. Due to free access \, researchers can use it in various projects\, enabling them to make data -driven decisions in the development of mobility research frameworks.\n\n\ n## Data-Availability\n\nThe described dataset\, containing GPS & accelera tion records and stop/trip annotations\, are publicly available at the Ope n Science Framework under a CC-By Attribution 4.0 International license: h ttps://osf.io/34sft/\n\nThe annotation companion app we used to annotate t he dataset is free software under a BSD 3-Clause license: https://github.c om/RGreinacher/GPS-Diary DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:The STAGA-Dataset: Stop and Trip Annotated GPS and Accelerometer Da ta of Everyday Life - Robert Spang URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/CS9MGW/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-HVSF38@talks.osgeo.org DTSTART;TZID=CET:20220824T153500 DTEND;TZID=CET:20220824T154000 DESCRIPTION:Digitalization is being adopted in many public services to incr ease the efficiencies of the required operations. Regarding this\, there i s an important interest in digitalizing the current building permit proced ures since most of the buildings are designed digitally and as three-dimen sional (3D). In addition\, several countries are making an effort to reali ze the transition from two-dimensional (2D) cadastre to 3D cadastre. This is because 2D delineation of the legal rights may remain incapable to refl ect the reality with respect to property ownership in multipartite buildin gs. The 3D city models should also be kept updated to effectively manage t he occasions (e.g.\, natural disasters) and services (e.g.\, waterworks) i n the living areas. In this sense\, the open data standards have a vital r ole to enable interoperability between different domains such as AEC and L and Administration. In this sense\, this paper first aims to show the curr ent situation and opportunities on how to efficaciously benefit from open data standards for three significant issues. The issues can be listed as\, 1) digitalizing the building permit procedures\, 2) registering the condo minium as 3D\, and 3) updating the 3D city models. It then presents an app roach for integrating open standards for 3D registration of condominium ri ghts in Turkey context. The integration of GIS and BIM\, GeoBIM\, has gain ed importance in terms of digital building permitting since there are rule s to be checked with respect to the built environment\; for example\, the availability of bicycle parks. Besides\, zoning plans that are essential f or building permitting are generally formatted with GIS-based data. There are studies in the literature that aim to carry out the building permittin g by benefiting from the integrated GIS and BIM approach. This approach is also connected with the update of the 3D city model database because the as-built models of the buildings can be integrated into this database afte r the necessary conversions (Guler & Yomralioglu\, 2021). 3D registration of condominium rights\, which is part of the 3D cadastre\, is often resear ched in the literature since 2D-based delineation of ownership rights migh t be insufficient in detecting who owns or responsible for which parts of the multipartite buildings. The availability of 3D representation of owner ship rights will be efficient for various land administration applications \, for example\, property valuation. Open standards are\, of course\, pivo tal for realizing the 3D registration of condominium rights as they not on ly provide the integration between different organizations but also enable the interoperability for other processes that are needed the same data. I n this connection\, Land Administration Domain Model (LADM) is the first s tandard that comes to mind because it aims to provide a common language fo r land administration systems and supports 3D representation through bound ary face and boundary face strings. Since standards like CityGML focus on 3D modeling of buildings more deeply\, there are attempts that integrate t he CityGML and LADM by exploiting advantageous features of each of the sta ndards for better depiction of ownership rights as 3D (Li et al.\, 2016). The “Building” and “Cadastre” themes that are produced within the context of Turkey National GIS (TNGIS) describe the relationship between r elated features\, namely parcel\, building\, building blocks\, and condomi niums. These features are modeled such that they allow for integration wit h other standards such as CityGML and IndoorGML so as to enable the effici ent reuse of spatial data in different applications. To enable better inte roperability and 3D depiction of condominium rights\, an integrated model is developed. The proposed features that are adapted from LADM permits the 3D representation of ownership rights. The proposed features are linked w ith the IFC entities\, namely “IfcZone”\, “IfcRelAssignsToGroup”\, and “IfcSpace” to provide integration with IFC. “BuildingUnit” is linked with the “BuildingCondominium3D” feature\, and hence the integ ration with CityGML data is provided. It can be mentioned that “Building Condominium3D” corresponds to the “LegalSpaceBuildingUnit” feature o f LADM. The proposed model incorporates the integration with IndoorGML by means of the link between “BuildingCondominium3D” and “CellSpace”. Due to fact of the inevitable proliferation of digitalization\, the proce sses related to land and city management need to be accomplished more digi tally and fast. There is an important potential to be practiced building p ermit issuing\, as one of the important public services\, in the sense of improvement and automation of the process (Noardo et al.\, 2022). Open dat a standards have a quite crucial role in realizing this potential. This is because these standards enable the standardization of information flow be tween designers and organizations that are responsible for compliance chec king. In other words\, applicants can prepare their submissions according to required information for building permit issuing. There is a strong int errelation between digital building permitting and the update of 3D city m odels because if the as-built IFC data of buildings are available\, these data can be converted to CityGML\, and thus the 3D city model database can be kept up-to-date. In addition to this\, an up-to-date 3D city model dat abase can be used for digital building permitting as there is a need for b uilt environment data for integrated and comprehensive compliance checking . For example\, rules with respect to infrastructure facilities can be che cked using 3D city models. Open standards are effective to be successful i n practicing the 3D cadastre. With the increasing trend in BIM\, there are proposed approaches that use the IFC schema for 3D delineation of apartme nt rights in multipartite buildings. In parallel\, this paper concentrates on a model that provides the integration with IFC data in the 3D represen tation of condominium rights in Turkey. The misinterpretations regarding w ho is responsible or owns of which parts of the buildings can be prevented using the IFC-based depiction of ownership rights (Shin et al.\, 2020). T he semantic information pertaining to independent sections can be queried using the produced IFC-based models. These models will also be quite helpf ul for property valuation applications in Turkey that exploit 3D variables such as size\, volume\, position\, and material quantities since they pro vide detailed information on indoor parts of the buildings (El Yamani et a l.\, 2021). DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:The Role of Open Standards in Digital Building Permitting\, 3D Regi stration of Condominium\, and Update of 3D City Models - Dogus Guler URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/HVSF38/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-XDD9JK@talks.osgeo.org DTSTART;TZID=CET:20220824T161500 DTEND;TZID=CET:20220824T164500 DESCRIPTION:Decentralized applications are a fundamental element for intern et development\, not only because they are safer but also because they mak e data accessible to more people than centralized applications. One of the most important architectures of decentralized applications is blockchain\ , a computing infrastructure capable of sharing data obeying consensus and in an immutable way. The most popular blockchain applications belong to t he financial sector\, and developments are still missing in other areas th at can take advantage of \nthis technology. An area that can benefit from blockchain characteristics is citizen science\, which\, as its name specif ies\, is the research activity performed by a community of citizens. Due t o the requirements to this extent\, this work studies the feasibility to u se a blockchain architecture in citizen science\, specifically for ecosyst em monitoring. Additional to this\, this work helped to understand the adv antages and disadvantages of using this technology in this area.\n\nCurren t state-of-the-art applications that propose partially a solution to citiz en science are FOAM and CryptoSpatial Coordinates. FOAM [1] is a geospatia l web application that builds a consensus-driven globe map using the block chain Ethereum protocol. To achieve network verification\, it employs a cr yptographic software utility token\, where cartographers verify if points added to the network are false or correct. This removes the need for a cen tral authority to regulate and verify the points. The voting mechanism use s FOAM tokens to avoid spamming from the participants. The system works by mapping a blockchain address to a physical location\, which can be regist ered with a spatial resolution of 1m by 1m. CryptoSpatial Coordinates (CSC ) [2] is an Ethereum smart-contract library that can be used for developin g geospatially enabled decentralized apps. It uses Blockchain technology t o store\, retrieve\, and process vector geographic data.\n\nIn our approac h\, we were only “inspired” by the previous solutions\, but we decided to develop something new and original. The system is developed in Solidit y programming language. This allows usage on every blockchain that support s the Ethereum Virtual Machine and guarantees extended flexibility. Moreov er\, this choice is justified by the expanded ecosystem that Ethereum offe rs. The architecture of Smart Contracts is completely open-source and deve loped with a focus on the reusability of the components for other applicat ions in the same field. The two main parts of the architecture are the Cel l Smart Contracts and the Registry Smart Contracts. This is based on the m apping of a Discrete Global Grid System (DGGS) [3] with Smart Contracts. A s a DGGS we choose S2 [4]\, which is an open-source library developed by G oogle that offers good processing functionalities and a grid with a fine-g rained resolution. Each Smart Contract representing a Cell is used to keep track of the hash of the observations collected in the application. The h ashes are used to locate and retrieve the stored files in the decentralize d storage InterPlanetary File System (IPFS). This structure also allows to store metadata about the observations\, for example\, their quality decid ed through a peer voting mechanism or with some other system. The Registry Contracts are linked to a resolution of the DGGS and have the duty to kee p track of the mapping between the DGGS cells of that resolution and their respective Smart Contract. \n\nThe prototype platform is developed in Vel as\, a blockchain architecture with a strong focus on fast transaction spe ed and low costs of fees compared to other blockchains (e.g. Ethereum\, Ca rdano\, Solana). The use-case for this work was the Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems (SIMILE) project. SIMILE is a cross-border Italian-Swiss project with the aim to improve the collaboration between public administrations and stakeholders for the management of the Insubric lakes (Lugano\, Como and Maggiore) and their ecosystems\, as well as monitoring water resources quality [5]. One of the main sources of data in SIMILE is collected with a Citizen Science approach\, meaning that the data is collected from normal citizens throug h their smartphones. The observations of this type include data about wate r quality\, climatic parameters\, and multimedia files such as images can be included. The collected data can be currently validated by the public a uthorities managing the platform but this requires time which is not alway s available to technicians. In our system\, the observations are instead v alidated through a mixed rating system that allows both users and admins t o evaluate each entry. Furthermore\, the use of the proposed blockchain ar chitecture allows access to the collected data without relying on the curr ently existing Web Application. \n\nThe practical importance of this work is to fill the gaps currently present in citizen science applications\, by proposing an innovative system that works with the blockchain infrastruct ure. The result of this work and the technological development performed\, demonstrate that citizen science applications can be\, as a matter of fac t\, developed as a decentralized infrastructure. The main advantages with respect to other systems are data immutability\, security and no single po int of failure. Future work can include the implementation of a system to further incentivize the collection of data. This will work with a reward s ystem in the form of a Utility Token. This token could be accepted by the public administrations benefitting from the data\, in exchange for some fo rm of compensation such as discounts on public services. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Collaborative validation of user-contributed data using a geospatia l blockchain approach: the SIMILE case study - Andrea Folini\, Jesus Rodri go Cedeno Jimenez URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/XDD9JK/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-CS9HJ3@talks.osgeo.org DTSTART;TZID=CET:20220824T161500 DTEND;TZID=CET:20220824T164500 DESCRIPTION:# Developing a privacy-aware map-based cross-platform social me dia dashboard for municipal decision-making\n\n## Introduction\n\nUsers o f location-based social media networks (LBSN)\, such as Instagram\,\nFlick r\, or Twitter\, have produced an unprecedented base of data over the\npas t decade. According to ILIEVA & MCPHEARSON (2018: 553)\, "the\nenormous sc ale and timely observation are unique advantages of [social\nmedia data]" and therefore hold enormous potential for various\napplication purposes su ch as urban planning\, among others.\n\nMost notably for Instagram\, as on e of the largest LBSN\, encouraging the\nsharing of locations when creatin g content\, offers completely new and\npromising application purposes\, th rough the combination of the spatial\ncomponent with timestamps and the ac tual content (image & text).\n\nPublic social media (SM) data have shown t heir potential examining the\nincreasingly relevant social problems of Spa tial (In-) Justice\, spatial\n(in-) equality and spatial (in-) equity (Cf. SOJA 2013: 47). However\,\nfew research attempts were made to make these results available broader\nin practice and accessible to laypersons in an understandable way.\n\nLBSN data could contribute significantly to creatin g a better\ninformation base for municipal decision-making processes\, rea ching\nespecially younger target groups. Until now\, specifically these gr oups\nwere difficult to reach in common participation processes (Cf. SELLE \n2004)\, while bearing consequences of municipal policies for the longest \nperiod of time.\n\nOur stated research goal is therefore to provide citi zens\, laypersons\nand municipal decision-makers with an unprecedented LBS N Dashboard\, as a\nsimple open-source platform for spatial multi-purpose LBSN analysis.\n\n## Problem Statement\n\nSuch an undertaking raises certa in ethical and legal questions\, since\nthe user data belong to the users themselves\, including the right to\nself-determination over their data\, on the one hand\, and the right to\nprivacy on the other. The far too shor t-sighted (but frequently used)\nargument that posts have been deliberatel y published\, with all the\nconsequences of their public nature in mind (e .g.\, BURTON et al. 2012:\n2)\, is simply not sufficient for an in-depth d iscussion of privacy. This\nfurther violates the most important aspects of privacy (Cf. BOYD &\nCRAWFORD 2012: 672). In fact\, most users are not or only partially aware\nof what can actually be inferred from what they sha re or disclose about\nthemselves (KESSLER & MCKENZIE 2018: 6f).\n\nYet\, p rivacy is rarely addressed in LBSN research and\, worse\, often\nnegligent ly ignored. In this context\, many negative examples can be\nfound where d ata was analyzed and high-resolution results were\npublished\, clearly vio lating users' privacy\, for example\, in scientific\npublications (Cf. KOU NADI & LEITNER 2014: 140).\n\n## Research Interest\n\nGiven the increasing socio-spatial inequality\, the rapid growth of SM\,\nand the growing inte rest of municipalities in SM knowledge\, we see a\nsignificant need for su ch a privacy-aware LBSN dashboard\, which is\nentirely new to the geospati al community.\n\nWe develop a privacy-aware LBSN dashboard prototype and p ropose a data\nprocessing pipeline based on the HyperLogLog (HLL) algorith m by FLAJOLET\net al. (2007). The dashboard is geared towards easy informa tion\nretrieval and making use of the data richness of LBSN -- without\nco mpromising user privacy and the need for extensive data retention.\nInstea d\, we provide a unique\, customizable\, GDPR-compliant privacy\napproach. The combination of different open-source tools for structuring\nmulti-pla tform LBSN data\, leveraging the capabilities of HyperLogLog and\nsimple P ython integration ensure easy reproducibility and active\ncommunity develo pment (Cf. DUNKEL et al. 2021\; DUNKEL & LÖCHNER 2021a &\nb).\n\nThe dash board prototype is tailored for use in municipalities and its\ncitizens\, but offers high scalability for other purposes or other\nspatial levels. A limited interactive demo and its GitHub repository are\npermanently publi cly available as a result of a Master's thesis and an\nIoT Design Thinking Workshop (Cf. WECKMÜLLER 2021\; BUNDESSTADT BONN\n2022).\n\nWe plan on f inishing and automatizing the data processing pipeline\,\nenabling more so phisticated queries and adding further visualization\nmethods. In the long run\, the dashboard is thought to serve as a\nparticipation and open data hub for all citizens and for any city in the\nworld. So far\, the city of Bonn and Chemnitz (Germany) are pilot\npartners of this research project. \n\n## References\n\n### Literature\n\nBOYD\, D.\, & CRAWFORD\, K. (2012). CRITICAL QUESTIONS FOR BIG DATA.\nInformation\, Communication & Society\, 15(5)\, 662--679.\n\nBURTON\, S. H.\, TANNER\, K. W.\, GIRAUD-CARRIER\, C .G.\, WEST\,J. H.\, &\nBARNES\, M. D. (2012). "Right Time\, Right Place" H ealth Communication\non Twitter: Value and Accuracy of Location Informatio n. Journal of\nmedical Internet research\, 14(6)\, e156.\n\nFISCHER\, F. ( 2008). Location Based Social Media -- Considering the\nImpact of Sharing G eographic Information on Individual Spatial\nExperience. In A. Car\, G. Gr iesebner\, & J. Strobl (Eds.) Geospatial\nCrossroads @ GI_Forum '08. Proce edings of the Geoinformatics Forum\nSalzburg (pp. 1-7). Wichmann.\n\nFLAJO LET\, P.\, FUSY\, É.\, GANDOUET\, O.\, & MEUNIER\, F. (2007). Hyperloglog :\nthe analysis of a nearoptimal cardinality estimation algorithm. Analysi s\nof Algorithms 2007 (AofA07)\, 127--146.\n\nILIEVA\, R. T.\, & MCPHEARSO N\, T. (2018). Social-media data for urban\nsustainability. Nature Sustain ability\, 1(10)\, 553-565.\n\nKESSLER\, C.\, & McKenzie\, G. (2018). A geo privacy manifesto. Transactions\nin GIS\, 22(1)\, 3-19.\n\nKOUNADI\, O.\, & LEITNER\, M. (2014). Why does geoprivacy matter? The\nscientific publica tion of confidential data presented on maps. Journal\nof Empirical Researc h on Human Research Ethics\, 9(4)\, 34-45.\n\nSelle\, K. (2004). Kommunika tion in der Kritik? In: Müller B.\, Löb S.\,\nZimmermann K. (Ed.) Steuer ung und Planung im Wandel\, VS Verlag für\nSozialwissenschaften.\n\nSOJA\ , E. W. (2013). Seeking spatial justice (Vol. 16). University of\nMinnesot a Press.\n\n### List of Web References\n\nAll links last accessed on Febru ary 20\, 2022.\n\nBUNDESSTADT BONN (2022). Studierende entwickeln neue Ide en für die\ndigitale Stadt von morgen.\n\n\nDUNKEL\, A.\, LÖCHNER\, M.\, KRUMPE\, F. & Contribu tors (2021). LBSN\nStructure. .\n\nDUNKEL\, A. & LÖCHNER M. (2021a). LBSN HLL Database - Docker Container.\n\n\n\nDUNKEL\, A. & LÖCHNER M. (2021b). Lbsntransform.\n.\n\nWECKMÜLLER\, D. (2021). LBSN-Dashboard Prototype for Bonn.\n DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Developing a privacy-aware map-based cross-platform social media da shboard for municipal decision-making - Dominik Weckmüller\, Dr.-Ing. Ale xander Dunkel URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/CS9HJ3/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-ELBYL7@talks.osgeo.org DTSTART;TZID=CET:20220824T164500 DTEND;TZID=CET:20220824T171500 DESCRIPTION:## Introduction\n\nNoiseCapture is an Android application devel oped by the Gustave Eiffel University \nand the CNRS as part of a particip atory approach to environmental noise mapping. \nThe application is open-s ource and all its data are free.\n\nThe study presented here is a first an alysis of the first three years of data \ncollection\, through the prism o f noise sources. The analysis only focused on the \nlabels filled in by th e users and not on the sound spectrum of the measurement\, \nwhich will be studied later.\n\nThe aim was to determine whether known dynamics in envi ronmental acoustics could\nbe recovered using collaborative data.\n\nThis preparatory work having to be consolidated and extended thereafter\, and w ith \nthe will to include this study within the framework of the Open Scie nce\, an \nattention was brought on the reproducibility aspect of the anal ysis. \nThis one was entirely realized with free software and literate pro gramming techniques.\n\nThe context of the study\, the tools and technique s used and the first results \nobtained will be presented as well as the b enefits of using literate programming \nin this type of preparatory work.\ n\n## Data\nAn article presenting this dataset was published in 2021 (Pica ut et al. 2021). \nIt details the structure of the database and the data\, the profile of the \ncontributors and the contributions but does not anal yze the content of the data. \nThis is what this article proposes to begin .\n\nThe data used in this study correspond to contributions made between August 29\, 2017 \nand August 28\, 2020. During this period\, nearly 70\,0 00 unique contributors allowed \nthe collection of more than 260\,000 trac ks for a total of about 60 million seconds \nof measurement. A trace is a collected recording\, it contains the sound spectrum \n(1 second\, third o ctave) recorded by the phone coupled with its GPS positioning \n(1 second) . This information can be enriched by the contributor with labels. \nThere are 18 labels and the user can select one or more of them for each of the \ntraces made. They are detailed in (Picaut et al. 2021). \nThe prelimina ry work presented here focuses on the analysis of the proportion of \ncert ain labels in the global sample at certain temporalities.\n\nIn addition t o data from the collaborative collection\, some additional data were \nuse d to limit the study area. We chose to limit the geographical scope of thi s \npreliminary study to metropolitan France because this area contains th e largest \nnumber of recordings. \nThe climate and sound dynamics are kno wn and documented there.\n\nTo facilitate the reproducibility of spatial f iltering\, it was decided to use \nopen data sets from recognized sources: the Natural Earth database \n(Patterson and Kelso 2021) and the Admin Ex press database from the \nNational Institute of Geographic and Forest Info rmation (Institut Géographique National 2021).\n\n## The study\n### Tools \n#### PostGIS\nThe data are provided as a dump from a PostGreSQL/PostGIS database (Ramsey and Blasby 2001).\nSeveral scripts perform much of the at tribute and spatial filtering. \nThese filterings are saved in a materiali zed view whose data will be analyzed \nwith the R language.\n\n#### R\nThe R language (R Core Team 2021)\nis a programming language for data process ing and statistics with many libraries \ndedicated to geospatial data. \nR markdown allows to mix code and text in markdown for the dynamic productio n of \ngraphs\, tables and documents. \nIt is one of the recommended means for literate programming.\n\n#### Git\nGit is a Distributed Version Contr ol System (DVCS) (Chacon and Straub 2014). \nIt enables collaborative and decentralized work. \nThe choice of Git was natural as different collabora tors are present on several\nsites (Nantes\, Lyon\, Paris) and Git is alre ady used within the UMRAE laboratory.\n\n### Implementation\nThe data are provided in the form of a PostGreSQL/PostGIS dump. \nA server has been set up and the data loaded. \nA materialized view was created in order to pro vide a stable access to the data \ncorresponding to the defined criteria. \nThese criteria are both attributive (filtering of certain tags\, minimum and maximum \ndurations\, etc.) and spatial (located in France\, reduced trace area\, etc.). \nA Rmarkdown document establishes the connection with the view and then performs \nthe operations allowing to analyze the data. \n\nA document mixing narrative\, figures and code allowed the resumption and \ncontinuation of the analyses shown here. \n\n## Results\nThe study c oncerns tracks bearing a tag\, registered in metropolitan France. \nIt foc uses on the proportion of a certain tag in relation to all the tags for a \ngiven period (time of day\, season\, etc.). \nIn the sample studied\, it is possible to note a prevalence of the tags *roads*\, \n*chatting*\, *an imals* and *wind*. The tags *air_traffic* and *works* are also well repres ented.\n\nA first axis of analysis concerns the time distribution of the t ags. \nAnimal noises (tag *animals*) are more frequent in the morning and especially \none hour before sunrise. \nThis is a common dynamic for bird song. \nWe also observed peaks in human activity\, especially commuting.\n \nThe next temporal axis was the seasonality\, especially those of animal noises\, \nwith a more intense activity in European spring and summer. \nT his phenomenon could also be observed in the recordings.\nWe also noticed that music was less present in autumn than in other seasons and \nthat it is mostly present at late hours.\n\n## Conclusion\nThe first results are e ncouraging because road dynamics related to commuting or \nanimal activity can be observed.\nThe main question was to determine if these known dynam ics in environmental acoustics \ncan be observed in a crowdsourced dataset .\nThe first elements seem to answer positively to this question.\n\nSome questions still need to be explored\, notably those concerning the \nrepre sentativeness of samples that are sometimes weak for certain time periods. \n\nThe systematic use of open source software\, the provision of document ed code files \nand a document mixing narrative\, figures and code have al lowed the resumption and\ncontinuation of the analyses shown here. \nThis work in progress will complete the final article. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Crowdsourced acoustic open data analysis with FOSS4G tools - Nicola s Roelandt URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/ELBYL7/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-CY9BMD@talks.osgeo.org DTSTART;TZID=CET:20220824T164500 DTEND;TZID=CET:20220824T171500 DESCRIPTION:Complex quarry districts like Apuan Alps’ marble quarries req uire remotely sensed high resolution data for soil consumption monitoring over the years: extractive activities lead to environmental challenges tha t require accurate environmental controls issued by the Tuscan Regional En vironmental Agency (ARPAT). The Regional Environmental Information System office (SIRA) over the last 5 and 10 years has developed methods and techn iques suitable for both 2D and 3D soil consumption monitoring by using fre e aerial and satellite images and Open Source Geo-spatial Software for dat a processing and data dissemination useful in controls’ planning and man agement. Aerial images and LiDAR acquisition\, satellite data\, RPAS acqui sitions have been tested in order to evaluate their suitability in derivin g both 2D and 3D indicators with proper resolution to address required spa tial-temporal constraints\, i.e. yearly monitoring of high resolution chan ges (spatial resolution between 50cm and 1m).\nDue to the size of the Area of Interest (AOI) of the Carrara basins\, up to 2.5km x 2.5km\, stereo sa tellite and aerial images can be used to obtain precise terrain models by photogrammetric reconstruction useful in 3D soil consumption monitoring\, while middle-resolution (10m) multi-spectral satellite images and high-res olution aerial images (50cm-1m) can be used in 2D soil consumption monitor ing and quarries’ area regulations by public bodies (natural soil loss\, exhausted areas restorations\, debris removals and new disposals).\nOpen- access Sentinel-2 multi-spectral satellite images with 10m of spatial reso lution have been used to assess coverage changes\; the results have been s ubsequently refined by manual interpretation over 5 years (2016-2021). Bot h semi-automatic methods based on spectral distances and machine learning techniques have been used to identify areas affected by extraction activit ies in QGIS 3.x environment over Sentinel-2 images. Free OGC Web Map Servi ces (WMS) made available by the Tuscan Regional Information System have be en used to assess changes highlighted by semi-automatic methods: aerial hi gh-resolution images between 2010 and 2019 have been evaluated by visual p hotointerpretation\, allowing to extend to 10 years the 2D soil consumptio n assessment over the whole area.\nComparison of highlighted 2D changes to regulated areas like mapped debris disposals and quarries’ property lim its have been used to check proper developments of extraction activities a nd proper environmental debris management.\nIn turn\, 3D changes have been tracked by comparison of 2009 and 2017 free aerial LiDAR data made availa ble for download by the Tuscan Regional Information System\, integrated wi th two stereo models obtained from 2020 and 2022 Pléiades satellite high resolution images (new acquisitions) freely granted by ESA following Proje ct Proposal id 61779 (“Quarry activity monitoring in Apuan Alps”). Ste reo satellite B/W images with 50cm of spatial resolution have been process ed by using Open Source stereo processing pipelines in Docker virtual envi ronments\, obtaining high precision digital surface models (height precisi on around 1m) after vegetation filtering. 3D changes detected over the yea rs by elevation algebraic comparison\, performed in QGIS 3.x environment\, highlight quarries characterized by intense extraction activities (extrac ted marble blocks\, characterized by positive quotas differences) and quar ry area management (debris disposing and service infrastructure building\, characterized by negative quotas differences).\nThe combined usage of bot h 2D and 3D changes’ indicators can be challenging in term of proper rep resentation of soil consumption dynamics over the years: while decision ma kers need a quick and easy access to both 2D and 3D data\, web technologie s suitable for a proper representation have been developed in very differe nt contexts\, making their integration quite complex. While a ‘classical ’ 2D webgis client Openlayers or Leaflet-based can be enough to highligh t 2D changes and – with some limitations – 3D changes as elevation dif ferences\, a ‘true’ 3D visualization environment must be set to track ongoing extraction activities aiming to assess both (a) compliance to auth orized extraction plans by public bodies and (b) proper debris management in quarry areas. In addition\, 3D web viewers are mainly targeted to repre sents point clouds or CAD drawings\, making very difficult the integration of 2D\, 2.5D (Terrain Models) and 3D (extracted volumes) data.\nA dual 2D /3D webgis client have been developed for proper representation of 2D/3D s patial indicators of ongoing extraction activities in the Carrara marble b asin: high resolution images have been served as tiled data\, while 2D/3D spatial indicators are served as static and/or tiled vector data. Open-Sou rce libraries have used in data processing\, serving and representation in side a map interface.\nFor each quarry included in the Carrara basing\, bo th area limitations and authorized areas for extraction activities have be en superimposed over the spatial indicator layers\, thus allowing users to easily locate areas subjected to intense extraction activities and to eva luate compliance to sustainability plans and environmental management pres criptions issued by public bodies.\n2D and 3D indicators are in progress t o be used in prioritizing environmental controls’ planning: this novel a pplication would require a proper scoring system based on the degree of co mpliance to both environmental management prescriptions and performances m ainly in the field of quarry and marble slurry waste management. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:2D/3D soil consumption tracking in a marble quarry district - Ing. Cinzia Licciardello URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/CY9BMD/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-FLDTX8@talks.osgeo.org DTSTART;TZID=CET:20220824T171500 DTEND;TZID=CET:20220824T174500 DESCRIPTION:In northern Italian mountainous regions\, forests are invading pastures and abandoned cultivated surfaces leading to an important land-us e change phenomenon and reducing those open areas that are fundamental for ecological purposes [1]. \nThe research here presented\, focuses on a mul tiobjective and contemporary assessment methodology of two or more multicr iteria analyses applied in the identification of the most suitable areas f or agricultural purposes between those surfaces that have been invaded by forests carried out using Free and Open Source Software for Geospatial (FO SS4G) software. The analysis of the areas was determined by taking into ac count their intrinsic characteristics and their spatial location in relati on to the territory and started from previous studies on land use in the A utonomous Province of Trento (Italy). The pilot areas are three municipali ties that are part of Trento’s Province: the municipality of Trento - th e Province’s capital\, the municipality of Pergine Valsugana and seven m unicipalities that are part of the Piana Rotaliana region. Almost 88% of t he Municipalities are located at an altitude of more than 600 m above sea level reflecting the peculiar topography of the province made up of valley s and high mountains with high percentages of steep slopes [2]. In Trento \, the overall density is 742 inhabitants per square kilometers and the pr essure on urban and peri-urban areas is nine times higher than the rest of the province [51]. 20% of Trento’s territory is classified as agricultu ral and 50% as forest or pasture land. About 70% of the territory is cover ed by silvopastoral -agricultural areas\, the remaining 30% is categorized as urban. The repartition of the province’s surface is similar to the o ne of the city of Trento: 61% of the territory is covered by forests\, 33. 6% by agricultural areas\, and only 5% by other types of land use. Collect ive bodies and public actors manage most of these silvopastoral -agro-fore stal areas whose ownership is collective and is managed following the “u so civico” rights\, a customary right embedded within the properties of communities and villages [52]. Therefore\, profit is not their main aim.\n This study has been part of the SATURN European project [3] funded by EIT Climate-KIC (November 2018-December 2021). Three city-regions have been in volved: the Trentino region in Italy\, Birmingham in the United Kingdom\, and Gotheborg in Sweden. The project aimed to reintegrate natural resource s into cities' climate change adaptation strategies and to expand and nurt ure its model by creating a broader initiative involving an increasing num ber of stakeholders. Geospatial data set was georeferenced and managed wit h GRASS and QGIS and the files were collected combining data freely availa ble at the Autonomous Province of Trento as well as self produced during t he project. \nThe comparative analysis and methodology were carried out by means of QGIS 3.8 Geographic Information System that has been used to com plete the analysis in order to develop a methodology that can be widely us ed by territorial operators and Public Administrations.\nThrough a series of multi-criteria analyses [4] of the agricultural and ecological vocation of a given region\, and more specifically of abandoned agricultural areas \, it was possible to create initial maps assigning values according to sp ecific considered aspects. To lead these analyses\, it was necessary to co llect and select a significant amount of georeferenced data and then stand ardise them. Synthesis analyses have been useful to compare the ecological and agricultural aspects and to integrate them in synthesis maps\, which can be used in the future for land management and planning. \nIn order to validate the model and to verify the results\, on-site inspections were ca rried out both in Valsugana and in Val d'Adige.\nTechnicians and experts h ave been involved in the research through focus groups\, organised within the SATURN project\, which allowed some general criticalities of the terri tory to emerge\, and through the completion of a questionnaire proposed wi thin the thesis work. Through these questionnaires\, it has been possible to identify the most important criteria for assessing a plot of land from an ecological and agricultural point of view.\nThe obtained results showed how the classical approach\, based on single criteria analysis\, differ f rom the multicriteria approach for its potential to produce a more precise and clearer classification output of the aspects considered\, showing the two multi-criteria analyses and their dependence on a single final map. S ignificant advantages have been taken from the use of this method in terms of data and information exchange between the stakeholders and in terms of a deepen understanding of the characteristics of the areas that have been analysed.\nThe proposed methodology and the script that has been develope d can be used in order to better plan forest management and as a basis for future territorial plans. \nMoreover\, the multicriteria approach\, which initially provides for a separate analysis of the research layers and the n integrates them into a single final output\, may represent a starting po int for ecosystem evaluations. Preserving the ecosystem of an area\, or ra ther the mosaic of ecosystems that make it up\, is in fact of fundamental importance\, as is succeeding in creating an eco-sustainable environment. In order to achieve this\, it is necessary to have a spatial planning proc ess that is as accurate as possible and that evaluates all the ecological criteria in a diversified manner with respect to the criteria of the objec t of research\, so as to be able to identify key elements. \nThe model pre sented can be replicated by changing the current research object\, i.e. ag ricultural assessment\, and keeping the ecological assessment instead.\nFu ture development will foresee the transformation of the Python script into a plug-in for Qgis\, guaranteeing greater functionality for those who wis h to use it. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Multiobjective analysis of open areas invaded by forest with open s ource software: the case of the SATURN project - Angelica Pianegonda URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/FLDTX8/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-ETS7XY@talks.osgeo.org DTSTART;TZID=CET:20220824T171500 DTEND;TZID=CET:20220824T172000 DESCRIPTION:Mt. Ushba is situated in the Greater Caucasus in Georgia\, next to the Russian border. With its nearly symmetrical double peak appearance \, it is iconic and a symbol of the historic Svaneti region in Georgia\, f amous for its mountains\, botany\, and century-old defense towers. Svaneti is becoming an increasingly popular tourist destination in summer and win ter. Therefore\, the German Alpine Club is interested in providing a new m ap for this region\, which will be produced by the Institute of Cartograph y of the TU Dresden. In the age of open data\, it is consequential that Op enStreetMap will be an essential source of the new map. It should make the project more sustainable and inspire people to use free and open-source s oftware for map production.\n\nOne basis of each topographic or touristic map is fieldwork\, which means organized mapping and editing with OpenStre etMap aiming to verify and to complement map content and coverage[1]\, car ried out by the Institute of Cartography in Mestia (Georgia) in the summer of 2021. Preparing for this work\, a comparison with older maps was condu cted to identify possible shortcomings and errors in the data. A draft was created using OpenStreetMap and the SRTM elevation model\, preparing for the fieldwork. It helped to evaluate the current state of the data\, gave a first impression of the mapping area\, and was an ostensive basis for da ta capturing in field. A field book was produced for each participant\, co ntaining the map draft as an atlas and information on which data should be collected and which the specific attributes were required. Finally\, the data was contributed to OpenStreetMap\, and from there\, the draft was upd ated again.\n\nIn the case of land cover\, creating an own classification seemed beneficial in distinguishing between typical vegetation classes in a high mountain area. Showing the vegetation in detail is a feature of Alp ine Club map\, but using OpenStreetMap data would not detailed enough. In addition\, a land cover classification based on remote sensing data is mor e reliable and ensures better consistent results compared to individual co ntributions from users with different previous knowledge. Open remote sens ing data from the Landsat and Sentinel programs offer good sources for suc h a task and are also used to monitor the glaciers in this area[II]. R is used as an analysis platform. It is possible to classify rock\, glaciers\, and specific vegetation types such as alpine rose or open birch stands. F or identifying the vegetation\, representative examples were collected dur ing the fieldwork by entering them in the atlas and taking sample photogra phs.\n\nAnother essential part of a topographic map for a high mountain ar ea map is a good terrain visualization. The SRTM model is beneficial but n ot detailed enough to create rock depictions\, which will be automatically derived by the Piotr tool[iii]. Planet Labs Inc provided high-resolution Rapid Eye and their Dove satellites imagery\, suitable for creating a digi tal elevation model with a spatial resolution of approximately ten meters by applying stereo photogrammetry methods using the AMES Stereo Pipeline[i v]. The result enables a much more precise and understandable representati on of the terrain. The terrain points were recorded with special standard GPS devices\, the Garmin GPSMAP 66sr\, which stores the raw observations f or two frequencies. Accuracies in the range of around 0.1 meters[v] can be achieved using professional GNSS software. \n\nIn order to produce the fi nal topographic map\, it is necessary to combine all data components to re present the area around Mt. Ushba. In a first step\, the updated OpenStree tMap data is imported into a PostgreSQL database with PostGIS extension. I n a second step\, an automated generalization is carried out for the selec ted target scale of 1:33\,000\, particularly schema transformation\, aggre gation\, and simplification. For the visualization\, QGIS is utilized: one project containing all layers with their visualizations served as WMS. It enables team members to view the current map and access all the data with out storing it individually locally on their computer. Additional web mapp ing services were set up to provide georeferenced scans of other available maps of the region to enable a comparison and evaluation of the new deriv ed topographic map product.\n\nBecause of the wide range of tasks\, the wo rk is split into several work packages and ongoing subprojects. Students' master theses within the International Cartography Master program – a co operate offer of TU Dresden\, TU München\, TU Wien\, and University Twent e contributed significantly to the project by implementing and evaluating selected methods required for the map derivation. \n\n\n[i] Grinberger\, A . Yair\, Moritz Schott\, Martin Raifer\, and Alexander Zipf. “An Analysi s of the Spatial and Temporal Distribution of Large‐scale Data Productio n Events in OpenStreetMap.” Transactions in GIS 25\, no. 2 (April 2021): 622–41. https://doi.org/10.1111/tgis.12746. \n\n[ii] Holobâcă\, Iulia n-Horia\, Levan G. Tielidze\, Kinga Ivan\, Mariam Elizbarashvili\, Mircea Alexe\, Daniel Germain\, Sorin Hadrian Petrescu\, Olimpiu Traian Pop\, and George Gaprindashvili. “Multi-Sensor Remote Sensing to Map Glacier Debr is Cover in the Greater Caucasus\, Georgia.” Journal of Glaciology 67\, no. 264 (August 2021): 685–96. https://doi.org/10.1017/jog.2021.47. \n\n [iii] Geisthövel\, Roman\, and Lorenz Hurni. “Automated Swiss-Style Rel ief Shading and Rock Hachuring.” The Cartographic Journal 55\, no. 4 (Oc tober 2\, 2018): 341–61. https://doi.org/10.1080/00087041.2018.1551955. \n\n[iv] Shean\, David E.\, Oleg Alexandrov\, Zachary M. Moratto\, Benjami n E. Smith\, Ian R. Joughin\, Claire Porter\, and Paul Morin. “An Automa ted\, Open-Source Pipeline for Mass Production of Digital Elevation Models (DEMs) from Very-High-Resolution Commercial Stereo Satellite Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 116 (June 2016): 101– 17. https://doi.org/10.1016/j.isprsjprs.2016.03.012. \n\n[v] Lachapelle\, Gérard\, Paul Gratton\, Jamie Horrelt\, Erica Lemieux\, and Ali Broumanda n. “Evaluation of a Low Cost Hand Held Unit with GNSS Raw Data Capabilit y and Comparison with an Android Smartphone.” Sensors 18\, no. 12 (Novem ber 29\, 2018): 4185. https://doi.org/10.3390/s18124185. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Mapping Mt. Ushba – How to create a high-quality topographic map from open data using free software - Mathias Gröbe URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/ETS7XY/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-GURC7K@talks.osgeo.org DTSTART;TZID=CET:20220824T172000 DTEND;TZID=CET:20220824T172500 DESCRIPTION:Manual digitization of 3D information from aerial stereo images has been one of the major tasks in national mapping agencies. However\, i t is labor-intensive. There is an enormous need in developing an automatic method for extracting 3D information from stereo images. Recent advanceme nt in hardware and software provides the possibility of realizing full aut omation in stereo-image tasks. Stereo-image tasks require a large capabili ty of computational power. The emergence of the GPU gave great support to such technique development. With recent advances in AI\, machines are gain ing the ability to learn\, improve\, and execute repetitive tasks precisel y\, especially with deep learning techniques: the capacity of combining an d adjusting millions or even billions of parameters from a neural network. Therefore\, it becomes possible to automize many complex tasks.\nOpenCV w as built in 2008. It is an open-source library that includes several hundr eds of computer vision algorithms. OpenCV supports functions of epipolar g eometry estimation and constraint as well as depth calculation from stereo image. Before 2016\, many researchers have employed OpenCV (Open Source Co mputer Vision Library) for depth estimation from stereoimage. In recent ye ars\, using deep learning methods for obtaining depth maps from stereoimag e has been highlighted. In deep learning applications\, left and right ima ges usually need to be rectified before they can be fed to the network. GC -Net\, HRS Net\, MVSNet\, PMS Net\, and PLUMENet are examples of convoluti onal neural networks (CNNs) that can be used for this purpose. GC-Net was introduced in 2017 by Kendall et al. [1]\, PMSNet in 2018 by Chang et al. [2]\, MVSNet in 2018 by Yao et al. [3]\, and HRS Net in 2019 by Yang et al . [4]\, PLUMENet in 2021 by Wang et al.. Disparity images are typically us ed as labels\, but some networks work with unsupervised learning\, meaning no labels are used for training them. Some experiment was based on open-s ource datasets\, such as KITTI stereo and Middlebury stereo being good exa mples. Ready remote sensing stereo image datasets still seem to be quite s carce\, but at least some can be found\, for example\, stereo image datase t of Vaihingen: Aerial Stereo Dense Matching Benchmark introduced in 2021 [5].\nOur experiment was focused on obtaining disparity maps i) from aeria l stereo images with known orientation parameters using openCV\; ii) from rectified aerial stereo images with deep neural networks: GC-Net\, MVSNet\ , and PSM net. The results based on OpenCV and neural networks were compar ed and evaluated. \nTwo datasets were used in the experiment. One dataset was the aerial stereo images with known orientation parameters from Nation al Land Survey of Finland. The aerial images were acquired in 2020\, using the UltraCam Eagle Mark3 (Vexcel\, Austria)\, with a forward overlap of 8 0% and a side overlap of 30% between flight stripes. The flight height was 7657.9 m. The image has a spatial resolution of 30 cm. Another set was fr om the ISPRS Aerial Stereo Dense Matching Benchmark 2021[5]: the Vaihingen dataset. The Vaihingen dataset from the ISPRS 3D reconstruction benchmark provides a good registration of oriented images and LiDAR point clouds. T he dataset is composed of 20 images with a depth of 11 bits and a ground s ample distance (GSD) of 8 cm. The reference depth maps were produced by Li dar point clouds for evaluation.\nIn the experiment of using the OpenCV li brary\, known orientation parameters were used for image rectification. OR B (Oriented FAST and rotated BRIEF) features were used to find image match ing points. ORB is open source\, which is an efficient alternative to SIFT or SURF. The algorithm uses FAST in pyramids to detect stable keypoints\ , selects the strongest features using FAST or Harris response\, finds the ir orientation using first-order moments and computes the descriptors usin g BRIEF (where the coordinates of random point pairs (or k-tuples) are rot ated according to the measured orientation).\nIn the experiment of using d eep learning methods\, GC-Net\, MVSNet\, and PSM net\, were tested. GC-Net is an end-to-end deep stereo regression architecture [1]. It estimates pe r-pixel disparity from a single rectified image pair by employing a cost v olume to reason the geometry and utilizing a deep convolutional network fo rmulation for reasoning the semantics. MVSNet is an end-to-end deep learni ng architecture for depth map inference from multi-view images [3]. It com putes one depth map at each time by extracting deep visual image features\ , building the 3D cost volume upon the reference camera frustum\, and appl ying 3D convolutions to regularize and regress the initial depth map to ge nerate the final output. PSMNet is a pyramid stereo matching network consi sting of two main modules: spatial pyramid pooling and 3D CNN [2]. It expl oits global context information in stereo matching. PSMNet extends pixel-l evel features to region-level features with different scales of receptive fields by pyramid pooling module. The cost volume was formed by combining global and local feature clues. A stacked hourglass 3D CNN was designed to repeatedly process the context information for estimating cost volume in a top-down/bottom-up manner to improve the utilization of global context i nformation. \nThe results from two datasets with three networks and OpenCV were presented. The experiments exhibited that selecting proper loss func tion and learning rate is important in using neural networks. It affects t he performances and results of different networks. The results were evalua ted by comparing with the reference depth maps. The advantages and disadva ntages of using networks and OpenCV library were analyzed and discussed. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Experiment on producing disparity map from aerial stereo-images usi ng openCV and deep learning - Lingli Zhu URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/GURC7K/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-JW3X9G@talks.osgeo.org DTSTART;TZID=CET:20220824T172500 DTEND;TZID=CET:20220824T173000 DESCRIPTION:According to the goals of the European Communications "2030 Dig ital Compass: the European way for the Digital Decade" and “Open Source Software Strategy 2020 – 2023” regarding the digitalization and the us e of the Open-Source solution inside the Public Administrations\, this pap er presents the approach followed for the realization of an Open-Source We b-GIS to transfer all the information assets related to the public works\, that must be judged by the Regional Technical Administrative Committee (C .R.T.A). The developed Web-GIS consists of a platform to support the “Ci vil Engineering” authority of the Abruzzo Region in the management of th e public works during their whole administrative process. \nIn particular \, the main aims of the Web-GIS are:\n- to manage in a unique shared geosp atial database the public works\, that must be judged by the C.R.T.A. of t he Abruzzo Region\; \n- to monitor the activities and the life-cycle of th e public works\;\n- to share information related to the public works both with other regional authority offices and with citizens. \nIn general\, th e creation of a WebGIS starts from a project created on the client side wh ich\, in a subsequent phase\, will be loaded on a server to allow the visu alization\, interaction and distribution of the information among multiple users at the same time. \nIn this case\, the creation of GIS project for the management of geo-referenced territorial and alphanumeric information for their description required a careful study of the needs of the “Civ il Engineering” authority of the Abruzzo Region and a definition of the contents of the GIS platform\, passing through the documentation and the a rchives to consult and implement in the GIS. Finally\, the choice of the o utput to be presented was made\, also in relation to the type of end-users that will have to manage (regional authority employees) and view (citizen s) the published information. \nIn order to properly design the requested Web-GIS application\, as a first step the structure of the geodatabase has been designed locally into the Qgis software\, one of the most famous ope n-source GIS software. Among the main geodatabase formats\, the geopackage \, an open\, OGC (Open Geospatial Consortium) standards-based\, platform-i ndependent\, portable\, self-describing\, compact format for transferring geospatial information\, has been selected. The GeoPackage standard descri bes a set of conventions for storing it within a SQLite database. The geop ackage format has been selected considering the geometric entities of the public works that must be stored within the database\, together with their attributes\, that consist of points\, multi-lines\, and multi-polygons el ements. In fact\, the public works that must be judged by the C.R.T.A. of the Abruzzo Region can be buildings (strategic or scholar buildings or hea lthcare constructions)\, road works\, hydraulic works\, or land defense. T hese public works\, as required by the Abruzzo Region\, do not have an exa ct type of geometry but\, depending on its type and the type of project ca n be represented in the most appropriate way to understand the interventio n itself. The geopackage format allows storing all the information related to the public work in a single file\, simplifying their management. \nThe use of QGIS solution was made keeping in mind the idea of using LizMap so ftware to publish directly the contents of the geodatabase designed locall y in a simple way. Lizmap is an open source software designed by 3Liz\, a service company revolving around QGIS software\, which facilitates the pub lishing of web mapping applications from QGIS Desktop using QGIS Server as Map Server. Another important aspect for this choice consists in the fact that QGIS environment is well-known among the public authorities employee s and this simplified the interaction during the design phase of the datab ase. This allows verifying if the structure of the designed database satis fies all the requirements of the “Civil Engineering” authority of the Abruzzo Region. In addition\, in the future\, the “Civil Engineering” authority of the Abruzzo Region will be able to modify or update autonomou sly the public works that will be subject to the judgment of the Regional Technical Administrative Committee (C.R.T.A)\, directly in QGIS Desktop. \ nAfter the realization of the project in Qgis Desktop\, in order to share the map online\, the Lizmap plugin inside Qgis Desktop was used to configu re the publishing options\, i.e. scales\, base layers\, metadata\, etc. . Once the file configuration is compiled\, it is possible to synchronize th e working folder with the Qgis Server. When synchronisation is complete\, the QGIS project can also be accessed on the Internet\, through the Lizmap Web Client application using a web browser. Lizmap Web Client is installe d on QGIS Server in order to insert projects and it allows to configure th e project and the displayed web page. All this step can be performed local ly\, (intranet network) and finally\, the project and the created settings files have to be transferred to the region geoportal of Abruzzo Region. I n conclusion\, the use of Lizmap to transfer Qgis Desktop projects on the web represents a good solution to move Public Administration towards the u se of Open-source solutions and towards the digitalization procedures requ ired by the European Commission. In addition\, this tool had the purpose o f ensuring maximum transparency to citizens who\, although not insiders\, can access the geoportal to see how the funds allocated by the Region\, th e Italian Nation\, and the European Community are distributed and spent. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Design and implementation of an Open-Source Web-GIS to manage the p ublic works of Abruzzo Region: an example towards the digitalization of th e management process of Public Administrations - Alicandro Maria\, Sara Zo llini URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/JW3X9G/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-33PMHD@talks.osgeo.org DTSTART;TZID=CET:20220824T173000 DTEND;TZID=CET:20220824T173500 DESCRIPTION:Overview:\n \nThis presentation will discuss the ongoing effort to map\, in unprecedented detail\, a forested area in Central Bali\, Indo nesia\, the use and ownership of which is currently a contested question. The presentation will outline the historical and political reasons for the contested nature of the land area under investigation\, and then discuss participatory field mapping methods and a collaborative analysis pipeline developed to represent via formal GIS methodologies the land and its use w ith the needs of different and differing stakeholders in mind.\n \nBackgro und:\n \nOur research approach is informed by current approaches to commun ity mapping in general [Cochrane2020] and specific to emerging economies\, with a particular focus on the conditions in Indonesia [Sulistyawan2018]. In particular\, we are studying an area is Central Bali in the vicinity o f the Taman Wisata Alam (TWA) Buyan -Tamblingan comprising 1\,491 hectare of forest area including Alas Merta Jati [Suryawan2021]\, part of the Batu karu nature reserve which is estimated to contain sufficient springs to me et Bali’s water needs [Zen\, 2019] (Fig. 1). The Alas Merta Jati is cont ested as it is currently claimed as ancestral lands (or “customary fore st”) by the Tamblingan people and at the same time claimed as a state fo rest by the Indonesian government. While both entities claim to want to pr otect the forest along fashionable “sustainable” principles [Strauss20 15]\, each entity interprets the responsibilities and benefits of sustaina ble actions in different ways. Subjecting the area to GIS compliant analys is approaches is one way by which differences and commonalities across sta keholders can become tractable.\n \nCollaboration framework :\n \nOur work is coordinated and overseen by a local NGO\, the WISNU foundation (https: //www.wisnu.or.id/) with which we have a memorandum of understanding outli ning work methods\, data collection and data ownership as well as ownershi p of intellectual property\, creating formal boundary conditions for an eq uitable long-term outcome of the project. Moreover\, our research team inc ludes GIS professionals from the Indonesia National Research and Innovatio n Agency with expertise in remote sensing of tropical forests.\n \nData so urces and field work:\n \nOur data collection relies on a combination of h igh-resolution satellite imagery from PlanetScope (PS) provided by Planet Labs (integration of Sentinel-2 data is in progress as well) and field lev el data collection through inhabitants of the area. PS with a resolution o f 3.7 m/pixel containing four channels: Blue (455 - 515 nm)\, Green (500 - 590 nm)\, Red (590 - 670 nm)\, and Near-Infrared (780 - 860 nm) [Raza et al. 2020]. Our first step follows standard practices. We study the compos ite’s PS satellite data in comparison with Google Earth (GE) images to i dentify a first round of land cover features. However\, we then also check questionable areas with local informants who collect short video recordin gs of the actual situation on the ground (Fig. 2) and upload these verific ation datasets to a shared server. Moreover\, our system is set up to supp ort low-tech input data collected with old-fashioned paper and pencil. A h andwritten set of longitude\, latitude and identified land cover class is sent (via email) to the evaluation team where custom python scripts conver t the information to an entry into a vector data set suitable for classifi cation purposes.\n \nComplex land cover classes:\n \nThe single most signi ficant issue we encounter in this project is the fact that local knowledge and local interests are not represented in GIS maps nor in the land cover categories that routinely constitute formal categories in GIS representat ion. The existing GIS knowledge production pipeline\, with its reliance on visual evidence\, is not sufficient to address these needs.\n \nFor examp le\, how might one monitor and detect the outcome of efforts of the "jaga teleng" (traditional forest guards) as opposed to modern forest regrowth a pproaches? Even some quotidian and concrete “use” classes in the study area are resistant to visual-only inspection. Coffee plant farms typicall y grow together with and often under clove tree gardens and cannot be dist inguished even with high-resolution (3.0m/pixel) satellite imagery without additional field level data collection. In general\, the land use conditi ons in Bali are characterized by a variety of mixed uses and mixed conditi ons\, with untouched areas mingling with secondary forests and overgrown l ight use agricultural areas creating a complex assemblage of “quasi-natu ral” conditions. And the tropical conditions on the island ensure that a n agricultural area that has been harvested or abandoned\, regrows to a se mi-wild area in months. While this project contains many elements\, the im age interpretation and metadata creation that can be ingested into a GIS f ramework to represent some of the convention challenging categories listed above\, is by far the most challenging aspect of the effort.\n \nA GIS an alysis framework for experimentation and collaboration:\n \nIn order to su pport the challenging data interpretation work and enable a collaborative testing environment\, we have developed a cloud-based GIS environment (COC KTAIL) that combines elements of established QGIS\, GDAL\, OTB and SAGA en vironments such that we can create processing pipelines across these vario us widely used GIS systems and run this software cocktail remotely in the cloud. This allows our research partners to work in their respective time zones and explore different approaches to the data analysis and classifica tion approaches within a shared analysis framework. Importantly\, our pipe line records the large collection of local setting and internal evaluation parameters to a file such that each member can easily recreate the output of the other team member experiments. Results are transferred to a shared remote server such that results can easily be visually inspected together during remote meetings.\n \nAt the time of this writing\, Cocktail is use d in our research group to combine satellite imagery with texture maps\, t o create change maps (from the start of the datasets to this year) and to perform land cover classification (Fig. 3). Cocktail includes Support Vect or Machine\, Random Forest and Neural Network classifiers\, the suitabilit y of which we are now analyzing in an iterative manner\, collecting more d ata as the need arises (see resources). DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Who speaks for the forest? Participatory mapping and contested land cover classification in Central Bali\, Indonesia - marc böhlen\, Rajif I ryadi URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/33PMHD/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-JAERFJ@talks.osgeo.org DTSTART;TZID=CET:20220824T173500 DTEND;TZID=CET:20220824T174000 DESCRIPTION:In the recent years\, point cloud technologies\, such as Unmann ed Aerial Vehicles (UAV)\, Terrestrial Laser Scanners (TLS)\, Aerial Laser Scanners (ALS)\, let alone Mobile Mapping Systems (MMS) have come into th e focus of attention and have been a subject of considerable public concer n in mapping.  Thanks to these new techniques\, experts can survey large areas with sufficient and homogenous accuracy\, with high resolution. \n \nIt comes from this that there are several areas where the point clouds c an be used. One of the applications is updating land registry maps. Many c ountries all over the world face the issue that a significant part of thei r large-scale land registry maps are based on old analogue maps from the l ate 19th or the early 20th century. One of these countries is Hungary\, wh ere more than eighty percent of digital cadastre maps were digitised using analogous maps in a scale range of 1:1000 – 1:4000\, not to mention the maps with fathom as base unit and with the scale of 1:2880. It is quite c ommon to have a few meters offset in the features depicted in the land reg istry maps\, which yields a wide variety of problems in applying maps\, su ch as in public utility registration and engineering practice. The final s olution to the problem would be to carry out new surveys for the critical areas\, but that has been often postponed due to the lack of time and exce ssive costs. \n\nThanks to the new technologies updating the old and not r elevant maps are feasible and there are several examples\, where point clo uds were used to update old land registry maps with manual processing. As it has been investigated by many researchers\, an optimal solution is to g enerate point clouds from the combination of nadir and oblique images take n by UAVs\, typically having 1-3 cm Ground Sample Distance (GSD). Our aim is to find the building footprints with not more than 10 cm accuracy from the point clouds. Oblique images play an important role in having sufficie nt number of points on the walls of the buildings in the point cloud\, so we can find not only the outline of the roofs but the walls of the buildin gs\, too. \n\nThere is another crucial factor that needs to be considered when processing point clouds\, namely that of automation. It is beyond dou bt that automation definitely improves the efficiency of the whole procedu re. There is already a wide range of open-source software available\, such as OpenDroneMap (ODM)/WebODM\, CloudCompare\, QGIS\, not to mention many open-source libraries\, like Open3D\, PDAL\, Point Cloud Library (PCL)\, S ciPy and Scikit-learn to support automatic data processing of point clouds . During our research\, the different combination of these libraries was i nvestigated paying attention to be accessible and freely developable for e veryone. Therefore\, the source code (mostly written in Python) of our pr ograms\, created in the frame of this project\, is also open-source and av ailable on our Geo4All Lab’s GitHub page. \n\nIn addition\, our study fo cuses on segmenting point clouds in an almost fully automated way. The pro cessing starts off by a cloured point cloud which is generated by ODM from nadir and oblique images. Then\, a Normalized Digital Surface Model (nDSM ) is generated. The Cloth Simulation Filter (CSF) algorithm is used to sep arate points on the ground and a Digital Elevation Model is generated from those points. From the nDSM the ground and the near ground points are rem oved\, this way the low vegetationare also filtered out. \n\nSubsequently the filtered point cloud is voxelized. Voxels are essential to divide the complex task into small processes which can be parallelized. With the help of a sequential RANdom SAmple Consensus (RANSAC) method in each voxel\, o ne or more significant planes are detected. Those points in a voxel that f it on a found plane are substituted by a single point on that plane and th e normal of the plane\, thus a spare point could be used later. This way n oise and vegetation is filtered out in a robust and efficient way. \n\nIn the next step of processing the spare point cloud is segmented by the nor mal directions into three categories: walls\, roofs and others. The wall a nd roof points are further segmented separately by region enlargement meth od. Finally\, the continuous wall and roof segments are combined to define the footprints of the buildings. \n\nTest areas and traditional land surv eying methods were used to validate the aforementioned algorithms. As our intention is to apply the technology mainly for smaller settlements\, we are about to focus on detecting detached houses. According to our prelimin ary results\, land registry maps with homogenous accuracy is achievable. A ccuracy can be characterized by less than 10 cm\, which meets the requirem ents in general. With the contribution of open-source solutions\, the tech nology offers an economical way of updating old and heterogenous land regi stry maps. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Voxel-based point cloud segmentation and building detection - Bence Péter Hrutka\, Bence Takács URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/JAERFJ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-GBPPCM@talks.osgeo.org DTSTART;TZID=CET:20220825T090000 DTEND;TZID=CET:20220825T093000 DESCRIPTION:# Context and purpose\nOGC standards shape a backbone within th e OSGeo community in defining a pathway to software implementation toward the standardization of geospatial information and related services ensurin g interoperability between FOSS4G software. Since 2016\, the OGC has initi ated the specification of a new generation of standards based on the OpenA PI so as to facilitate integration in modern web applications and systems. \n\nUnderpinning the OGC API roadmap\, the development of all these standa rds represents a significant amount of activities carried out by various O GC working groups\, testbeds and pilots from the OGC Innovation Program. S ome standards have been approved\, many are still under development and it is therefore not always easy to follow the progress. Indeed\, while some geodata infrastructures involving national entities are already deploying this new generation (e.g. Canada MSC GeoMet)\, some initiatives run a phas e of experimentation (e.g. Geonovum Testbed Platform for the Dutch geoport al).\n\nFrom a practical perspective\, how can organizations and instituti ons anticipate to leverage this new generation of standards to deploy a ge ospatial data infrastructure? This issue is what this article is about\, i ntroducing a project that seeks to address it by running an OGC API testbe d platform with a special focus to the Swiss context. This project is embe dded in the Resources for the NSDI Program (related to the Swiss Geoinform ation Strategy) with the purpose to contribute to the upcoming revision of e-government standards regarding geoinformation (e.g. eCH-0056 Geoservice s application profile). The project is about a study jointly carried out b y swisstopo and complementary academic partners (HEIG-VD\, SUPSI\, UNIGE). \n\n# Approach\nAs a result of the above mentioned complexity and overlapp ing of existing standards\, the project team has applied a benchmark study approach\, where different standards are tested in experimental cases and evaluated in comparison of other existing solutions. The outcomes include both quantitative and qualitative results that will be condensed in pract ical recommendations for implementation and adoption of the OGC API family .\n\nThis research aims at evaluating a selection of different OGC specifi cations as well as different server and client implementations in order to define e-government recommendations to promote collaboration between auth orities\, companies and individuals.\n\nThe selected mainstream topic for the experimental cases is about climate change. While not yet connected in a complex pilot study\, each case represents one of the required componen ts: from sensing (remote/in-situ) to data visualization and exploration\, through data offering and elaboration. The study is organized in three par ts:\n\n* The hydro-meteorological monitoring network of the Canton Ticino\ , which is currently managed using the SOS standard\, has been selected as representative of a practical implementation of basic data required for t he climate change impact assessment pipeline. The network\, which has a 40 years long time-series\, is currently composed of 60 stations and 140 sen sors observing precipitation\, air temperature and humidity\, water temper ature\, river height. Collected information is operationally used by the l ocal administration to design and actuate water resources protection and a llocation to guarantee a sustainable management of the resource and the na tural environment while protecting from the impacts of extreme events like floods and droughts. The Sensor Things API operational applicability is e valuated by testing this standard to fulfil all the major in place daily p ractical operations like for example data quality management\, data sharin g with third parties\, data collection from vendor specific sensors and da ta analyses and visualization.\n\n* Switzerland was the second country in the World after Australia to have an operational satellite Earth Observati ons (EO) Data Cube. The Swiss Data Cube (SDC) is a tera-scale analytical c loud-computing platform allowing users the access\, analysis and visualize up to 38 years (1984-2022) of consistent calibrated and spatially co-regi stered optical and radar Analysis Ready Data. The SDC leverages the inform ation power of Big Earth Data for monitoring the environment by minimizing time and knowledge required for analysing large volumes of raster data. T he derived analytical products provide an effective means to build sociall y robust\, replicable\, and reusable knowledge\, to generate ready-to-use products supporting evidence-based decisions. Currently\, all the data pro ducts and their related description (i.e. metadata) are accessible through “traditional” OGC services such as WMS\, WCS\, CSW. For example\, the Normalized Difference Water Index (NDWI) time-series can be used to estim ate and monitor the evolution of vegetation water content over the entire country. The aim of the experimental case will be to use a set of new OGC APIs implemented on top of the Swiss Data Cube to track the evolution of N DWI. To reach this objective we will implement the OGC API Coverages\, Pro cesses\, EDR\, Records\, STAC APIs to access NDWI raster data time-series and compute zonal statistics using different administrative units/levels ( e.g. national\, canton).\n\n* Given past activities of the team project re lated to portrayal interoperability with OGC standards like WMS\, WMTS\, S LD/SE\, this part aims to challenge a set of specifications of the OGC API \, especially Features\, Tiles\, Maps\, Styles and to provide insights abo ut OGC SymCore. At one side\, the experimental case will consider outputs from running Geoclimate\, an open source geospatial toolbox to compute a s et of urban climate related parameters describing a given area of study us ing OpenStreetMap data as a base. The intent is to make these indicators d iscoverable and to serve them as data and maps through the OGC API. At the other side\, the aim is to address national needs for geodata visualizati on using the Minimum Geodata Models (MGDM) in conjunction with their styli ng models\, testing how the symbol description may be encoded in a standar d way with modern formats and techniques to build styles and symbology (i. e. from SLD/SE to GeoCSS with or without cascading\, etc).\n\nFor all thes e experimental cases\, FOSS4G are deployed\, especially at the server leve l with FROST\, pygeoapi\, Geoserver\, QGIS Server. The results are useful for developers\, government agencies and organizations who want to impleme nt and use the new family of OGC standards. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:OGC API State of Play - A practical testbed for the National Spatia l Data Infrastructure in Switzerland - Ertz Olivier\, Collombin Maxime URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/GBPPCM/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-R7S8UY@talks.osgeo.org DTSTART;TZID=CET:20220825T090000 DTEND;TZID=CET:20220825T093000 DESCRIPTION:Urban planning and design play an important role in amplifying or diminishing built environmental threats to health promotion and disease prevention (Keedwell 2017\; Hackman\, et al. 2019). However\, there is st ill a lack of good evidence and objective measures on how environmental as pects impact individual behavior. The eMOTIONAL Cities project (eMOTIONAL Cities - Mapping the cities through the senses of those who make them 2021 ) sets out to understand how the natural and built environment can shape t he feelings and emotions of those who experience it. It does so with a cro ss-disciplinary approach which includes urban planners\, doctors\, psychol ogists\, neuroscientists and engineers. \nAt the core of this research pro ject\, lies a Spatial Data Infrastructure (SDI) which assembles disparate datasets that characterise the emotional landscape and built environment\, in different cities across Europe and the US. The SDI is a key tool\, not only to make the research data available within the project consortium\, but also to allow cross-fertilisation with other ongoing projects from the Urban Health Cluster and later on\, to reach a wider public audience.\n\n The notion of SDIs emerged more than 20 years ago and has been constantly evolving\, in response to both technological and organisational developmen ts. Traditionally\, SDIs adopt the OGC W*s service interfaces (e.g.: WMS\, WFS\, WCS)\, which are based on SOAP\, the Simple Object Access Protocol. However\, in recent times\, we have seen the rise of new architectural ap proaches\, which can be characterised by their data-centrism (Simoes and C erciello 2021). Web-based APIs have numerous advantages\, which speak for their efficiency and simplicity. They provide a simple approach to data pr ocessing and management functionalities\, offer different encodings of the payload (e.g.: JSON\, HTML\, JSON-LD)\, can easily be integrated into dif ferent tools\, and can facilitate the discovery of data through mainstream search engines such as Google and Bing (Kotsev et al. 2020). These APIs o ften follow a RESTful architecture\, which simplifies its usage\, while mi nimising the bandwidth usage. Moreover\, the OpenAPI specification (OpenAP I Initiative 2011) allows to document APIs in a vendor-independent\, porta ble and open manner\, which provides an interactive testing client within the API documentation.\nOGC has embraced this new approach in its new fami ly of standards called OGC APIs (OGC 2020a). Although still under active d evelopment\, it already produced several approved standards: the ‘OGC AP I - Features’’ (OGC 2022b\, the ‘OGC API - EDR’ (OGC 2022c)\, the ‘OGC API Common’ (OGC 2022d) and the ‘OGC API - Processes’ (OGC 20 22e) which provide standardised APIs for ensuring modern access to spatial data and processes using those data.\nThere are many similarities in the process of designing and implementing open source and open standards. OSGe o encourages the use of open standards\, like those from OGC and there is even a Memorandum of Understanding between the two organisations (OSGeo 20 12). In practice\, many long-standing OSGeo projects implement OGC standar ds and they often contribute to the standards development (e.g.: GDAL\, Ge oserver\, QGIS\, OpenLayers\, Leaflet). However\, in the majority of cases they still implement the legacy W*s standards\, rather than the new OGC A PIs.\n\nIn the eMOTIONAL Cities project we have set out to create an SDI b ased on OGC APIs\, but realised that we needed to support some legacy stan dards\, because an OGC API equivalent was not widely supported yet. This h as led us to create two stacks: one OGC APIs (e.g.: modern) and another on e using W*s services (e.g.: legacy). Both stacks rely on FOSS/OSGeo softwa re\, and whenever relevant we have contributed to some of those projects. The modern stack includes Elasticsearch and Kibana (Elastic)\, which add e xtra capabilities in terms of searching\, analytics and visualisation.\nFo r the sake of reproducibility\, all software components were virtualized i nto docker (Wikipedia 2022) containers and they are orchestrated using doc ker-compose. The results are published in the eMOTIONAL Cities public gith ub repository (eMOTIONAL Cities H2020 Project 2021).\n\nDespite its numero us advantages\, we still see a lack of adoption of the OGC APIs within mos t SDIs. In part this could be due to the standards not being well known\, but it could also be due to a lack of knowledge about which implementation s are available out there\, specially as FOSS. In this paper we would like to share our modern SDI architecture\, and the reasons for choosing pygeo api (Kralidis 2019) for publishing data as OGC API Features\, Vector Tiles and Records. Although the standards we selected target the Urban Health u se case\, we believe they are generic enough to be useful for sharing data in other contexts (e.g.: climate change\, cross-border datasets).\nWe are confident about a transition to OGC APIs\, but we are also conscious that this may take time\, and for a period of time many solutions will have to offer both modern and legacy standards. \n\nPlease find the complete list of references on this page: https://github.com/emotional-cities/foss4g_re f/blob/master/references.md DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Serving Geospatial Data using Modern and Legacy Standards: a Case S tudy from the Urban Health Domain - Antonio Cerciello\, Joana Simoes URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/R7S8UY/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-GDD8XU@talks.osgeo.org DTSTART;TZID=CET:20220825T093000 DTEND;TZID=CET:20220825T100000 DESCRIPTION:Lake Maggiore and the Ticino River are water bodies shared by\n Italy and Switzerland: they are important resources for drinking\nwater\, irrigation and hydroelectricity generation as well as for\ntourism and bio diversity. The cross-border character and the\noften conflicting needs of the different users make the shared\nmanagement of this resource very comp lex\, but of great importance.\nThe `‘Parchi Verbano Ticino´’ project \, funded by Regione\nLombardia / EU – INTERREG Italia Svizzera 2014/202 0\, aims\nto study the effects of water levels of the lake on various envi ronmental\ncomponents with a particular focus on protected\nnatural areas. The level of the lake is regulated by a dam located\nat the southern shor e of the lake. In this framework\, this\nstudy aims to analyse the effect of water level on bird migration\nby: 1) Calculate the inundated bird habi tat using a simulation\nbased on measured water level\; 2) calculate the i nundated habitat\nfrom Sentinel-1 remote sensing imagery 3) Use the floode d\narea derived from S1 as ground truth to validate the previous\nsimulati on 1).\nThe study area is centerend around Bolle di Magadino (Switzerland\ ,\n8°51’56.90”E\, 46°9’42.17”N\, a protected wetland located\non the north shore of lake Maggiore at the confluence with the\nTicino river . The area is a recognized nesting and stopover\nsite for birds\, listed a s a Ramsar Wetland of International Importance\nand as Important Bird and Biodiversity Area (IBA).\nWe defined the habitats of interest using\na ve getation map provided by Fondazione Bolle di Magadino.\nThe vegetation typ es collected from a phytosociological field\nstudy were aggregated into te n land cover classes that described\nthe habitat types and land use. The f inal habitat map covers\nan extent of 6.7 km²\, including the 1500 ha of wetland called\nBolle di Magadino. Daily passage of migrant birds have bee n\nrecorded at Magadino ringing station and since 2019\, traditional\nnet captures were coupled with an Avian Vertical-looking Radar\nIn this study we focus on the following periods\, during which\nbird monitoring systems were both deployed: P1: 2019-05-01–\n2019-06-20\; P2 2019-10-01–2020-0 2-20 and P3 2021-02-01–\n2021-07-20.\nThe lake level measured at the hyd rological station of Locarno\n(CH) was used to determine the inundated are a in GRASS GIS\nusing ther.fill module (GRASS Development Team\, 2022) and \na DTM that included the lake bathymetry (cell size 0.5 m). The\nlake lev el fluctuated between 192.3 and 194.9 m.a.s.l. over the\nstudy period\, wi th a minimum in April and May\, when the waters\nare used to irrigate the rice fields downstream\, and a maximum\nin late autumn.\n\nWe used the Goo gle Earth Engine Platform (GEE) (Gorelick et\nal.\, 2017) to extract Senti nel-1 Synthetic Aperture Radar (SAR)\nimages (ESA\, 2021) as they are suit ed for surface water mapping\nand not affected by cloud coverage (Ovakoglo u et al.\, 2021).\nWe used Edge Otsu Algorithm with terrain correction (Ma rkert\net al.\, 2020) to estimate the inundated areas of the collection of \na total of 236 images for the three time periods when bird migration\nwa s also monitored. The calculation was implemented\nGEE with the approach d escribed by Gorelick et al. (2017) calibrating\nthe threshold for our stud y area\, adapting the code provided\nby Open Geo Blog (2021). The inundate d areas were then\noverlapped with the land use map in order to estimate t he extent\nof the submerged vegetation over the three time period defined. \nThe resolution of all maps was 10 m\, except the DTM that has\n0.5 m\, t he CRS used in this work were WGS84 in GEE and the\nlocal CRS GCS CH1903 f or all the other analysis.\nThe area covered with water\, according to S1\ , varied between\n103.9 and 471.7 ha (210+- 83.6)\, some of the surface is a permanent\nwetland so it is never completely dry. Each habitat was\naff ected differently by the flooding: when the water was at its\nhighest\, cr oplands were completely inundated\, grasslands and\nreeds were submerged f or 80% of their extent whereas urban\nareas and infrastructures were not a ffected (less than 1% underwater).\nThe flooded area calculated by filling the terrain model\nat the level of the ranged from 130.4 to 248.90 ha (20 8+- 48.6).\nThe correlation between inundated areas obtained using r.fill in\nGRASS and S1 on the same dates was fair for P1 and P2\, but\nnot for P 3\, when the water of the lake were taken for irrigation\,\nbut the habita ts were flooded by rainfall. An interpolation of the\nflooded from S1 is a more efficient way to obtain an estimate of\nthe flooded habitat on a dai ly basis\, that is necessary to study\nits effect on migratory birds.\nThe results presented here will contribute to the definition sustainable\nman agement tools of water management of lake Maggiore\ntaking into account th e effect of lake level on biodiversity\nin general and on bird habitat in particular. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Effect of water level on bird habitat at lake Maggiore - Clara Tatt oni URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/GDD8XU/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-REQLGD@talks.osgeo.org DTSTART;TZID=CET:20220825T093000 DTEND;TZID=CET:20220825T100000 DESCRIPTION:Context\n\nIt is said that data visualization is as important a s the data itself. As the amount of data generated from Earth observation (EO) satellites – i.e. Copernicus program (Jutz and Milagro-Pérez\, 202 0) – is getting bigger and bigger\, we need more efficient tools to dea l with this onslaught of data. To help data scientists better extract rele vant information from datacubes\, we noticed that an under-exploited compu ter graphics tools could bring new perspectives to specialists. Datacubes are known to be the reference format to handle EO data\; several technique s such as Web WorldWind developed by NASA exist to process and interact wi th them. Recent works have shown focus on the preparation of largescale ge ospatial data (Mazroob Semnani et al.\, 2020)\, a highly technical subject \, could benefit from optimizations. QGIS is another tool frequently used in the field\, that can be enhanced by plugins and can retrieve data from Web platforms. A modern approach to process efficiency is the use of GPUs . Still\, when reviewing the use of GPUs to process geospatial data\, the emphasis is often put on the parallel processing of geospatial datasets ra ther than focusing on their visualization (Saupi Teri et al.\, 2022).\n\nO bjectives\n\nOne of the main contributions of this paper is to consider ge ospatial data using GPU resources for intermediate computation and visuali zation. Considering the increasing interest to interact with this data dir ectly using Web pages or Notebooks\, this article presents tools allowing a program to run on the GPU and display the desired datacubes using the We bGL API. This can result in high performances thanks to its low-level cont rol and possibility to use GPGPU algorithms. WebGL running natively on mos t web browsers\, another benefit will be the end-user ease of use. The en d goal is to display even large (i.e. 1024^3) datacubes rendered on the fly in real time on a PC\, still well-equipped.\n\nMethodology\n\nTo keep our applied research efforts focused\, we have set up an independent inte rnational expert advisory group. Indeed\, we wanted above all to provide s omething useful and concrete for the actors in the field. The represented institutes are ESA (EC)\, EURAC (Italy)\, GISAT (Czech Republic)\, Terrasi gna (Romany)\, TU Wien (Austria)\, VITO (Belgium)\, and even a former NASA (USA) analyst. They have been regularly interviewed to get constant feedb ack on the suitability of our developed application\, the final goal of o ur project being to build a toolbox of models to efficiently visualize di fferent EO datacubes formats.\n\nModels\n\nThis paper presents three main models applicable to datacubes from an EO context\, some relatively standa rd and others innovative\, still all revisited via the GPGPU architecture. \nImplicit curves model – This model has two main approaches: discrete a nd math-based sub-models. Especially adequate to process (x\, y) or (x\, y \, t) datacubes in a 2D or 3D visualization we developed and compared both sub-models with their dependencies. Sets of given iso and δ values are e xtracted from the data and stored as sets of curves. They can be displayed in a 2D environment or in 3D with additional information such as: (1) the simulation of the data as a 3D surface\; (2) different colormaps for the surface representing yet other external data\; (3) surface render in steps to emphasize the given iso and δ values\; (4) user customizable colormap s\; and (5)\, a water level simulation rendering.\n\nDerivative 3D renderi ng model – This model is specialized in analyzing (x\, y\, t) datacubes as a volume where the time t is part of the visualization. Indeed\, the ai m is to visualize the evolution in time of a geographical area by highligh ting the temporal differences within a volume. After selecting the (x\, y) region of interest the user selects a reference layer representing the st ate of an area at a defined time t and a time interval Δt. The cumulated differences between the two are visible in a colored sub-volume defined by the time interval. In order to add more contextual information in the v isualized geographical area\, we have added the possibility to display an additional map (such as topographic data) at the reference layer level wit hin the volume. \n\nJupyter Notebook massive rendering model – To make t he toolset even easier to use\, we have developed a visualization model de ployable in Jupyter. This model allows rendering of (x\,y\,z) and (x\,y\,z \,t) data volumes. Two rendering algorithms are already available: (1) the implicit surface simulation for any iso intensity -- but only via the dis crete approach -- and (2)\, an XRay-cast simulation.\n\nDiscussion\n\nResu lts show our models can process large amounts of data and render them in r eal-time. Where large 3D datasets would normally become problematic to han dle for any GPU\, we developed specialized tools to overcome software and hardware limitations. For instance\, a 3D datacube can be sorted into a 2D texture to be directly loaded into GPU memory\, thus improving performanc e. When the textures become too big to work with WebGL\, their information can be split in the RGBA channels of standard 2D textures for a four-fold decrease in memory use. Furthermore\, when displaying our rendering model s\, and in the case of machines without sufficiently powerful graphics car ds\, we propose to display only the fraction of the data that interests th e user. All of these highly efficient rendering models are assembled toge ther in a toolbox dedicated to datacube visualization.\n\nConclusion\n\nIn this paper we demonstrate an example of application retrieving raw data f rom a server\, formatting it for local use with GPGPU\, and rendering it w ith several innovative models. We developed these tools for a Web applicat ion and Jupyter Notebooks to better fit with the needs of data scientists. To better understand the scope of the possibilities of this work\, severa l illustrations are available here: https://bit.ly/3th8JBF. Finally\, we w ould also like to point out that this work has been granted by national fu nds\, therefore our development is open and does not have any external sof tware dependencies. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Earth Observation DataCubes Multi-visualization Toolbox - Christoph e Muller\, Stéphane Gobron URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/REQLGD/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-JFWQQM@talks.osgeo.org DTSTART;TZID=CET:20220825T100000 DTEND;TZID=CET:20220825T103000 DESCRIPTION:Introduction: Legally defined appellation areas are used by gov ernments throughout the world to demarcate geographic areas that produce a gricultural products\, such as wine\, cheese\, or preserved meats\, with a specific quality or set of characteristics. In the United States\, the A merican Viticultural Areas (AVAs) define wine growing areas that are disti nctly different from others. These boundaries are created by the US Alcoh ol and Tobacco Tax and Trade Bureau (TTB) through a legal process and the definitions are published in the United States Federal Register in narrati ve form defined using United States Geological Survey (USGS) topographic m aps for their landmarks. Despite their geographic definition\, a full spat ial dataset of these boundaries following the legal definitions did not ex ist until they were created by a team of researchers led by the University of California Davis’ (UC Davis) library. The purpose of the dataset is to produce open data suitable for use in research and cartography followi ng a well-documented set of methods that represents the official boundary descriptions with as high fidelity as possible. Using the UC Davis AVA dat aset alongside datasets defining environmental characteristics such as soi ls\, climate\, and elevation\, we seek to understand how the characteristi cs present within the AVA boundaries are similar to each other using a hie rarchical clustering process. Through this case study\, we will describe t he UC Davis AVA boundary dataset and demonstrate a use case for the data.\ nData: The UC Davis AVA dataset was created by digitizing the boundary nar rative onto the USGS topographic maps described in the legal documents (of ficially known as the “approved maps”) for each AVA by a team of colla borators at UC Davis\, UC Santa Barbara\, and Virginia Tech University\, a s well as community volunteers. For each boundary\, we recorded attributes including an identifier\, the official name of the AVA\, any synonyms for the name\, the dates the AVA officially was recognized\, the start and en d date for the given polygon\, who petitioned to define the AVA\, which TT B staff member wrote the official documents\, the list of approved maps\, the list of maps used to digitize the boundary (to record any necessary su bstitutions)\, and the official boundary description. In addition to the currently defined boundaries\, we also created a boundary polygon for the previous iterations of any boundaries that have undergone revisions. The dataset is stored in geojson format in a publically available GitHub repos itory and updated as AVAs are created or amended.\nFor each AVA\, we summa rized the environmental data over the area of the polygon. The PRISM datas et (from Oregon State University) provided the climate data (30-year clima te normals for precipitation and temperature) and elevation data in raster format with an 800m cell size. For each variable\, we calculated the mean and the range within the AVA boundaries.\nWe also plan to expand this ana lysis over the coming weeks to include additional environmental characteri stics available from PRISM\, such as vapor pressure and solar radiation th at would be important considerations for grape growth\, as well as soil da ta from the United States Department of Agriculture’s (USDA) SSURGO (Soi l Survey Geographic) soil dataset. SSURGO is a spatially-enabled dataset of soil characteristics for the United States. It includes geologic soil s eries names as well as the soil’s chemical attributes.\nAnalysis: For ea ch attribute\, the value at each AVA was assigned a z-score\, calculated a s the mean of the attribute field subtracted from the value and divided by the standard deviation of the field. This was done to normalize the data and reduce the effect of differing scales of measurements (for example\, d epth of precipitation compared with temperature in degrees Celsius). To as sess how similar any given AVA is to other AVAs\, we performed a hierarchi cal clustering analysis using R’s hclust() hierarchical clustering funct ion. This tool uses a dissimilarity matrix to assign each polygon to a hi erarchical series of groups based on how similar (or dissimilar) each poly gon is to each other. The results can be displayed in a dendrogram to visu alize the structure of the classes. The classes can also be used to create a map of the AVAs to help interpret the groups.\nResults: Preliminary res ults group AVAs into clusters that appear to be somewhat based on geograph ic regions\, but not entirely. When the dendrogram is cut into 6 groups\, the AVAs in the eastern half of the country primarily fall into one group\ , however\, the western AVAs comprise the remaining 5 groups. This could b e driven by the higher degree of variation in elevations\, precipitation\, and temperature in the west. In the southwest\, the AVAs appear to corre spond to one group\, however\, the west coast states have many groups\, in cluding some AVAs that correspond with the eastern group. Expanding the an alysis to include additional environmental factors will likely clarify som e of these groups\, perhaps defining more variation in the east. This pape r will include maps and diagrams that clearly show the relationships betwe en the groups.\nDiscussion: Investigating the relationship between the AVA boundaries is an important exercise. With the availability of the AVA bou ndaries as a geographic dataset\, we are now able to combine this data wit h other existing open datasets to better understand the relationship and d ifferences between these areas. All of the datasets used in this analysis are freely available and demonstrates not only the usefulness of the UC Da vis AVA dataset but also the depth of the work possible with open data. Th is particular exploration builds on work I have published with colleagues investigating the Sierra Foothills AVAs in the state of California and the emerging wine growing region in the state of Arizona. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Classifying American Viticultural Areas Based on Environmental Data - Michele Tobias URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/JFWQQM/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-KQ7NSA@talks.osgeo.org DTSTART;TZID=CET:20220825T100000 DTEND;TZID=CET:20220825T100500 DESCRIPTION:Land surface temperature (LST) in urban areas is an important e nvironmental variable considered a reliable indicator of the urban heat is land (UHI) phenomenon. LST is affected by various factors such as solar ir radiance\, cloudiness\, wind or urban morphology. Traditionally\, LST is o bserved and recorded by thermal remote sensors. For example\, thermal sate llite sensors are very popular for assessing the UHI effect on a global sc ale such as MODIS\, Sentinel 3\, ASTER\, Landsat 7 ETM+\, or Landsat 8 TIR S. However\, these sensors provide rather low spatial (60 m to 1000 m) and temporal resolutions (several hours to days) of satellite observations th at limit the accurate estimation of LST in urban areas for local studies a nd specific time periods (Mushore et al.\, 2017)\, (Hu and Wendel\, 2019). Airborne or terrestrial remote sensing can be viewed as another option fo r capturing higher spatial resolution of thermal data but it is not feasib le to be used for large urban areas with increased periodicity. However\, the increasing availability of the high-resolution geospatial data and ade quate modeling techniques provide an alternative approach to high-resoluti on estimation of LST in urban areas. \n\nSeveral studies showed the potent ial of geographic information system (GIS) tools\, digital surface models (DSM) and 3-D city models for the estimation of solar radiation in urban a reas (e.g.\, Hofierka and Kaňuk\, 2009\; Hofierka and Zlocha\, 2012\; Fre itas et al.\, 2015\; Biljecki et al.\, 2015). Solar irradiance is a key fa ctor affecting LST during daylight periods\, especially under clear sky si tuations. Nevertheless\, LST assessment requires a physical model combinin g surface-atmosphere interactions and energy fluxes between the atmosphere and the ground. Properties of urban materials\, in particular\, solar ref lectance\, thermal emissivity\, and heat capacity influence the LST and su bsequently the development of UHI\, as they determine how the Sun’s radi ation energy is reflected\, emitted\, and absorbed (Hofierka et al.\, 2020 b\; Kolečanský et al.\, 2021). It is clear\, that the problem complexity requires a comprehensive GIS-based approach. \n\nOur solution is based on open-source solar radiation tools available in GRASS GIS\, a 3D city mode ling and spatially distributed data representing thermal properties of urb an surfaces and meteorological conditions (Hofierka et al.\, 2020a\, 2020b \; Kolečanský et al.\, 2021) . The proposed LST model is calculated usin g the methodology implemented in GRASS GIS as a LST module written using a script (shellscripts\, Python). In these scripts\, the r.sun and v.sun so lar radiation models in GRASS GIS were used to calculate the effective sol ar irradiance for selected time horizons during the day . The solar irradi ance calculation accounts for attenuation of beam solar irradiance by clou ds estimated by field measurements. The proposed LST model also accounts f or a heat storage in urban structures depending on their thermal propertie s and geometric configuration. The 2D LST model uses the output of the r.s un solar radiation model and a DSM representing urban surfaces and the 3D LST model uses the output of the v.sun solar radiation model and a vector- based 3D city model. Computed LST values for selected urban surfaces were validated using field measurements of LST in 10 locations within the study area with acceptable accuracy. The proposed approach has the advantage o f providing high spatial detail coupled with the flexibility of GIS to eva luate various geometrical and land surface properties for any daytime hori zon. The methodology can be used for evaluation of proposed UHI mitigation measures such as increasing albedo of urban surfaces or expanding green a reas including green roofs and trees.\n\nReferences:\n\nBiljecki\, F.\, St oter\, J.\, Ledoux\, H.\, Zlatanova\, S.\, Çöltekin A.\, 2015. Applicati ons of 3-D city models: State of the art review. ISPRS International Journ al of Geo-Information\, 4\, 2842–2889. https://doi.org/10.3390/ijgi40428 42.\n\nFreitas\, S.\, Catita\, C.\, Redweik\, P.\, Brito\, M. C.\, 2015. M odelling solar potential in the urban environment: State-of-the-art review . Renewable and Sustainable Energy Reviews\, 41\, 915–931. http://dx.doi .org/10.1016/j.rser.2014.08.060.\n\nHofierka\, J.\, Bogľarský\, J.\, Kol ečanský\, Š.\, Enderova\, A.\, 2020a. Modeling Diurnal Changes in Land Surface Temperature in Urban Areas under Cloudy Conditions. ISPRS Int. J. Geo-Inf.\, 9\, 534.\n\nHofierka\, J.\, Gallay\, M.\, Onačillová\, K.\, H ofierka\, J. Jr.\, 2020b. Physically-based land surface temperature modeli ng in urban areas using a 3-D city model and multispectral satellite data. Urban Climate\, 31\, 100566.\n\nHofierka\, J.\, Kaňuk\, J.\, 2009. Asses sment of photovoltaic potential in urban areas using open-source solar rad iation tools. Renewable Energy\, 34\, 2206–2214. https://doi.org/10.1016 /j.renene.2009.02.021.\n\nHofierka\, J.\, Zlocha\, M.\, 2012. A New 3-D S olar Radiation Model for 3-D City Models. Transactions in GIS\, 16\, 681 –690. https://doi.org/10.1111/j.1467-9671.2012.01337.x.\n\nHu\, L.\, Wen del\, J.\, 2019. Analysis of urban surface morphologic effects on diurnal thermal directional anisotropy. ISPRS Journal of Photogrammetry and Remote Sensing\, 148\, 1–12. https://doi.org/10.1016/j.isprsjprs.2018.12.004.\ n\nKolečanský\, Š.\, Hofierka\, J.\, Bogľarský\, J.\, Šupinský\, J. \, 2021. Comparing 2D and 3D Solar Radiation Modeling in Urban Areas. Ener gies\, 14\, 8364.\n\nMushore\, T.D.\, Odindi\, J.\, Dube\, T.\, Matongera\ , T.N.\, Mutanga\, O.\, 2017. Remote sensing applications in monitoring ur ban growth impacts on in-and-out door thermal conditions: A review. Remote Sensing Applications: Society and Environment\, 8\, 83–93. https://doi. org/10.1016/j.rsase.2017.08.001. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Assessing land surface temperature in urban areas using open-source geospatial tools - Jaroslav Hofierka URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/KQ7NSA/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-LHVPAD@talks.osgeo.org DTSTART;TZID=CET:20220825T100500 DTEND;TZID=CET:20220825T101000 DESCRIPTION:In the last years we have witnessed a huge increase in the avai lability of free and open multispectral\, multitemporal and global coverag e satellite imagery. At the same time\, new open software tools for exploi ting these images have arisen. Given the availability of short-revisiting time open satellite images\, this study focuses on the analysis of satelli te imagery using free and open source GIS software to identify displacemen ts of single landslides.\nIn particular\, the Ruinon landslide was selecte d as the subject for this analysis. It is situated in Northern Lombardy\, Italy\, and it is one of the most active landslides of the Alps. The lands lide is situated at the base of a Deep-seated Gravitational Slope Deformat ion\, that affects the entire slope up to the summit at 3000 m a.s.l. Two major scarps can be identified: the upper one is a sub-vertical rock cliff of about 30 m in height\, while the lower one is characterized by a more widespread debris cover.\nThe general strategy employed in this work for o btaining landslide displacements in terms of direction and magnitude is to apply a local maximum cross-correlation on a multitemporal images stack. This was achieved using GRASS GIS and custom Python scripts.\nThe images w ere selected from both the Sentinel-2 catalogue\, which is free\, and the Planet catalogue\, available for free for research purposes.\nThe main pre processing steps are: creation of a suitable multi-temporal stack\, clippi ng the satellite images to the selected AOI and applying cloud masking and an atmospheric correction\; image co-registration to ensure that the imag es become spatially aligned so that any feature in one image overlaps as w ell as possible its footprint in all other images in the stack\; histogram matching to transform one image so that the cumulative distribution funct ion (CDF) of values in each band matches the CDF of bands in another image .\nThe main processing is based on the Maximum Cross-Correlation method im plemented on couples of images. The first image of the couple will be refe rerred to as reference image\, and the second one as secondary image. This algorithm was previously applied to land cover changes (You et al.\, 2017 ) and to the movement of desert sand dunes (Oxoli et al.\, 2020). In the d eveloped procedure\, the processing phase starts by placing a window in th e same position of both images. The window on the secondary image is then shifted in all directions\, and a cross-correlation coefficient is compute d for each of the shifts. The shifted window with the highest cross-correl ation coefficient is selected\, and a displacement vector is computed betw een the center pixel of the reference image window and the center pixel of the new shifted window of the secondary image.\nThe outputs are shifts (i n pixels) in X and Y directions which are actually the distances required to register the window of the secondary image with the one of the referenc e image. \nIt is important to note that the smallest displacement that can be identified by this procedure is a displacement of 1 pixel\, i.e. a dis placement of 10 m if considering Sentinel-2 data. Therefore\, smaller move ments cannot be sensed by because of the native resolution of input satell ite data. Secondly\, errors can arise from the images having differences i n terms of co-registration and histogram distribution\, since this process highly relies on the images being as aligned and similar as possible.\nFo r monitoring the activity of the Ruinon landslide\, two different sets of images were considered. The first one consists of one image per year in th e period 2015-2020\, with the idea to track the evolution of the landslide throughout the last few years. Since the landslide is situated in a mount ainous region\, it is often covered by clouds\, and in the winter months b y snow. Because of this\, only the best image for each year was selected f or the analysis. The other set is composed of three images\, one per month \, in the period July 2019 - September 2019\, aiming at highlighting a lar ge movement that took place in the summer of 2019.\nTo compare and evaluat e the performances of the cross-correlation approach\, data coming from UA V surveys (provided by the local environmental agency ARPA Lombardia) of t he landslide were used. At first\, the results obtained with the procedure were compared with the output given by the procedure when applied to RGB images obtained from the surveys\, which have a resolution of 1m. The two outputs were found to be very similar\, both for the displacement magnitud es and directions. Secondly\, photogrammetric point cloud comparisons crea ted from the UAV observations in periods close to the considered ones for satellite monitoring were investigated. In particular\, the displacement a long the vertical axis was inspected\, and accumulation zones were found i n correspondence to the largest movements of the landslide detected from t he algorithm. Because of this\, the results were considered consistent wit h the data of the surveys.\nThe increased availability of high-resolution multitemporal satellite imagery promotes the use of these images for monit oring purposes. While on the field monitoring can produce very accurate re sults\, a procedure like the one applied in this work has the advantage to be more flexible\, scalable and cost-effective than an analysis on the fi eld. The experimental procedure developed in this work led to promising re sults\, despite being a first stage approach to landslide monitoring apply ing the maximum cross-correlation method. Many approaches were considered\ , varying the main parameters of the procedure (adding or removing a class ification phase\, considering different intervals between satellite images \, modifying the size of the moving window and others)\, and the whole pro cess was progressively improved and refined until satisfactory results wer e achieved. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Monitoring landslide displacements through maximum cross-correlatio n of satellite images - Lorenzo Amici URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/LHVPAD/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-MAYYPL@talks.osgeo.org DTSTART;TZID=CET:20220825T101000 DTEND;TZID=CET:20220825T101500 DESCRIPTION:This article is a work in progress report on the introduction a nd exploitation of persistent identifiers (PID) within the OSGeo Foundatio n and its software project communities. Following an introduction to the t opic of Persistent Identifiers (PID)\, an overview of the currently achiev ed states and emerging new opportunities\, but also new challenges is give n. The latter enables the OSGeo project communities to actively participat e in the further development of data-driven open science and the evolution of the FAIR (Findable\, Accessible\, Interoperable\, Reusable) Guiding Pr inciples for scientific data management and stewardship from the original data focus to research software and community software projects. With the rise of the Internet and World Wide Web\, Universal Resource Locators (URL ) have become common practice to reference web resources. A URL specifies its location on a computer network and a mechanism for retrieving it. Howe ver\, URLs are not a sustainable practice for scientific citation because they will break once the referenced resource is transferred to another web address\; i.e.\, the original URL cannot be resolved anymore and an error message is returned instead (e.g.\, HTTP error 404). To counter this\, pe rsistent identifiers have been introduced as long-lasting references to we b resources\, including research data\, source code\, audiovisual content\ , and also human individuals or communities. Persistence is always achieve d by infrastructure services which resolve the references to their target objects. This requires open standards\, operation of infrastructure servic es and best practices for sustainable long term use. The adoption of PID u se in the OSGeo Foundation continues for different application areas\, wit h increasing synergy effects forming the foundation of a greater whole. Th e introduction of PID in OSGeo started in 2014 for a newly discovered vers ion of the historical GRASS GIS informational video from 1987\, which is p reserved in the AV Portal of TIB Hannover (https://av.tib.eu/) and can be accessed through a permanent Digital Object Identifier (DOI) (https://doi. org/10.5446/12963\, https://doi.org/10.5446/31768). Since 2016\, OSGeo con ference videos have been collected as a permanent service in the AV Portal \, with the collection growing by approximately 100 hours of video recordi ngs annually (pre-Covid). In 2017\, the rasdaman software project register ed a DOI for the first time for release version 9.4.2 in the Zenodo data r epository (https://doi.org/10.5281/zenodo.1040170). Zenodo is a general-p urpose open-access repository operated by the European Organization for Nu clear Research (CERN) since 2015. In 2019\, the next DOI registration foll owed for the GMT software project for release version 6.0.0 (https://doi.o rg/10.5281/zenodo.3407865). Further improvements of the technical integrat ion of project software repositories hosted on the GitHub platform and Zen odo have enabled a simplified handling of software versioning: When regist ering a DOI as a PID for a software project\, at least two references are created\, which are linked to each other: The Concept DOI\, which represen ts the software project as a higher-level intellectual construct\, and an initial Version DOI\, which references a specific software release. With t he integration now available between GitHub and Zenodo\, the successive cr eation of additional Version DOI for upcoming new software releases can be done automatically. Since 2021\, the number of DOI registrations by OSGeo software projects has increased significantly. Currently\, DOIs are alrea dy available for 19 software repositories related to OSGeo projects (https ://wiki.osgeo.org/wiki/DOI). More than half of the official OSGeo software projects can already be referenced by means of DOI. All projects that hav e registered a DOI have chosen an official scheduled release to initiate D OI versioning. Equipping OSGeo projects and content with PID results in si gnificant added value for scientific users\, but also for the respective p roject communities. Well formatted citations for software project DOI can be conveniently generated in thousands of different citation styles by onl ine citation services (e.g. https://citation.crosscite.org/). Citation of OSGeo projects is already actively used in scientific publications (e.g. S pringer Handbook of Geo Information\, 2nd Ed. https://doi.org/10.1007/978- 3-030-53124-9\, in print). The metadata of a PID for data and software can also reference PIDs for the authors and others involved. As a result\, it is now possible that once the Version DOI of a software release is cited\ , the involved persons can also be referenced using an individual PID\, su ch as the Open Researcher and Contributor ID (ORCID)\, and receive measura ble scientific credit for their effort. This allows that that collaboratio n efforts in FOSS software projects will become a measurable and rewarding part of the scientific track record. Furthermore\, PID of software\, data and other information sources can be related to each other by specifying related persistent identifiers in the metadata. This field is currently un dergoing rapid development. A further step will be the linking of the now available concept and version DOI of the OSGeo projects with the PID of th e OSGeo conference videos\, which will improve the discoverability and re- use of the conference contributions.The OSGeo Foundation can be understood as a growing continuum of software projects\, functionalities\, groups of people\, but also knowledge and information.\nProviding an up-to-date map ping of internal linkages and dependencies of the OSGeo continuum has not been satisfactorily solved yet. In the past\, there have been several appr oaches (e.g. http://pathfinder.terrasigna.com/oss/index2.html or https://d oi.org/10.5446/14652)\, which have remained snapshots due to the lack of p ersistent references to the described objects and manual maintenance for r egular updates. The availability of PID for software and persons creates a stable base for this for the first time\, seconded by the conceptual appr oach of an integrated PID-based graph\, which was developed in the FREYA p roject (https://www.project-freya.eu/). This approach models resources whi ch are identified by PIDs (software projects\, data\, publication\, person s) and the connections between them in a graph of the network of interconn ected PID systems\, based on their PID metadata. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:OSGeo\, Persistent Identifiers and the shape of things to come - Ma rkus Neteler\, Peter Löwe URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/MAYYPL/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-S9ACUV@talks.osgeo.org DTSTART;TZID=CET:20220825T101500 DTEND;TZID=CET:20220825T102000 DESCRIPTION:The need to make electricity production increasingly sustainabl e requires careful planning of production plants\, mainly for wind and pho tovoltaic energy conversion. Planning areas correctly\, while respecting e xisting environmental constraints\, is not an easy task and requires the c ollaboration of a panel of experts with different skills.\n\nThe need to s earch for new sites to be allocated to renewable energy generation plants is dictated by the most pressing current events\, the search for non-impac ting energy sources to whose research and development specific points of t he National Resistance and Resilience Plan are dedicated\, to which are ad ded the consequences of the newborn Ukrainian conflict that has definitive ly discovered the problematic relationship-dependence of Italy and Europe with energy supplies from non-European countries. Both issues are pushing the country towards a rapid search for new energy strategies for environme ntal reasons and to make up for natural shortages that require massive imp orts of gas and other resources from abroad. \nIn particular\, the Nationa l Recovery and Resilience Plan (PNRR)\, part of the European Next Generati on EU (NGEU) programme\, a 750 billion euro package allocated by the Europ ean Union to counteract the economic damage caused by the Covid-19 global pandemic\, is an economic plan worth 248 billion euro that Italy can use i n the five-year period from 2021 to 2026 to implement various reforms and repair the damage created by the pandemic crisis. \nThe plan\, presented t o the EU under the name 'Italia Domani'\, envisages investments along thre e main axes: digitalisation and innovation\, ecological transition and soc ial inclusion. These economic interventions are intended to resolve the dr ama caused by the advent of the Sars-Cov2 virus and help solve structural problems in the Italian economy\, accompanying the country towards a path of ecological and environmental transition. It also aims to resolve import ant issues such as territorial\, generational and gender gaps. \nIt is in this context that the national legislation is undergoing a revision\, whi ch has entrusted the regional administrations with the task of identifying the territorial criteria that favour or prevent the establishment of cert ain plants in the various areas of the territory. Each regional administra tion has the right to graduate the criteria according to the specific geom orphologic characteristics of its own territory and therefore the most eff icient procedure would be to verify\, with simulations in GIS environments \, the effect of defining certain criteria on the territory to assess in a dvance which and how many areas could have greater or lesser suitability. On the basis of this consideration\, we proceeded to experiment with the e ffects of the most common constraints by developing a real simulation on t he territory of the Lazio Region.\n\nThe experimentation used the well-kno wn open environment QGIS 3.22\, which made it possible to exploit the poss ibilities offered by the open territorial databases of the Lazio Region.\n It should be noted that the Lazio Region (like most Italian regions) has m ade many spatial data available in open format in recent years. The Europe an directive called "Inspire" gave a boost to the use\, standardisation an d free dissemination of spatial data. It provides for the creation of a Co mmunity data infrastructure that simplifies the sharing between public adm inistrations and user access to spatial information. In Italy\, the direct ive was transposed into Italian law by Legislative Decree no. 32 of 27 Jan uary 2010\, which established the National Infrastructure for Spatial Info rmation and Environmental Monitoring as a node of the Community infrastruc ture. As a result of this implementation\, the National Geoportal was crea ted\, which was followed by the various Regional Geoportals\, such as that of the Lazio region.\n\nThe implementation of the open data in QGIS 3.12 made it possible to identify topological inaccuracies in the files provide d and shared on the Lazio Region site\, which led to necessary decisions s uch as the correction of some polygons that presented errors\, such as the ir overlapping or imperfect closure (the correction of the latter case was suggested by QGis itself\, through the "reopen geometries" function). \nA possible inaccuracy was also found in the "lowland species" and "mountain species" files of the Regional Ecological Network\, which seems to show a n error in the transcription of the relative geodetic datum on the website \, where it is reported as WGS84\, UTM33N and resulting from verifications and overlapping more likely ED50 . Uncategorised areas also emerged in th e file called "PTPR Regione Lazio (Tav. A - Tav. B)" which were however ex cluded from those considered to be unsuitable since a more detailed analys is revealed the area of the Parco della Caffarella in Rome\, which is hard ly conceivable as the site for a wind farm or large-scale photovoltaic pla nt. Extraterritorial areas within the Region's territory belonging to the State of "Vatican City" were also added as "unsuitable".\nThe first result s show that the remaining areas after eliminating all those that are certa inly unsuitable are a limited part of the Region itself. It should be note d that these areas are not definitely suitable areas but those that are no t unsuitable or potentially suitable\, even if further investigation is re quired to ascertain their suitability.\nThe limited extent of the areas re maining after the exclusion of the unsuitable areas suggested that we make an initial estimate of the sustainability of a total conversion to these energy sources for the whole region in order to assess its potential energ y autonomy.\nThe analysis was extended to individual municipalities by com paring average yields per conceivable plant area\, then comparing them wit h inhabitants for an initial estimate of energy needs at least for domesti c use. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Simulation of the effects of possible regulations for the location of wind and photovoltaic power plants in the Lazio Regional Administration (Italy) - Valerio Baiocchi URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/S9ACUV/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-QACV83@talks.osgeo.org DTSTART;TZID=CET:20220825T102000 DTEND;TZID=CET:20220825T102500 DESCRIPTION:In this section\, we describe the main routines of the MSPA cod e with reference to the morphological image analysis operations they rely on with links to their implementation in the open source Morphological Ima ge Analysis Library (MIAL) recently released on GitHub at github.com/ec-jr c/jeolib-miallib by the first author. All morphological image analysis ope rators at the basis of MSAP are described in [Soille\, 2004]. We briefly p resent the main MSPA foreground classes with reference to source code of t he main morphological function used to compute them: core\, boundaries\, i slets\, connectors\, and branches. The actual pseudo-code will be added in the final version of this paper and will include details on the computati on of all MSPA feature classes including those of connected components of background pixels. The underlying code in the C programming language is av ailable on GitHub at github.com/ec-jrc/jeolib-miallib/blob/master/core/c/m spa.c\n\n# Performance\nThe performance of the algorithm is evaluated on i mages of increasing size as well as for on-the-fly computation for interac tive analysis and visualisation. We demonstrate experimentally that the co mplexity of the proposed implementation is linear. That is\, the computati onal time increases linearly with the number of pixels. We also show that the algorithm can handle images up to 2^64 pixels. For example\, a Global MSPA map of forest cover in equal area projection and with a pixel resolut ion of 100 meter (400\,748 x 147\,306 pixels) was processed on the JRC Big Data Analytics Platform [Soille et al. 2018] in 12 hours. Processing larg e images is very much needed to mitigate dependencies with regards to the image definition domain because pixel classes may depend on the observatio n domain.\n\nAs for the on-the-fly computation for interactive analysis an d exploratory visualisation based on Jupyter notebooks [De Marchi and Soil le\, 2019]\, we show that the proposed implementation is fast enough for i ntegration in JupyterLab with on-the-fly computation in an area correspond ing to the mapview area and at resolution matching its zoom level. A Voila dashboard is in preparation and will be available for demonstration at th e conference.\n\n# Conclusion\nMorphological spatial pattern analysis has gained traction since its inception in 2008. For many years\, we maintain a dedicated MSPA website with extensive documentation\, various GIS extens ions and a user-friendly provision of MSPA within the desktop application GTB and the server application GWB. The present open release of MSPA will further expand the potential user-community. We are in the process of maki ng MSPA directly available in the pyjeo python package [Kempeneers et al.\ , 2019]\, so that data scientists using python for their analysis will dir ectly benefit from the MSPA open source release. Since MSPA is available t hrough a library compiled in C\, it can be easily integrated in other data science environments. We therefore expect the release of the MSPA code un der an open source license to further boost its use for the analysis of ge ospatial patterns and indeed any other types of spatial patterns occurring in other scientific domains.\n\n\n# References\n- Soille\, P.\, Vogt\, P. "Morphological segmentation of binary patterns" (2009) Pattern Recognitio n Letters\, doi: 10.1016/j.patrec.2008.10.015\n- Soille\, P. et al. 2018. "A versatile data-intensive computing platform for information retrieval f rom big geospatial data" Future Generat. Comput. Syst. doi: 10.1016/j.futu re.2017.11.007\n- Ossola\, A. et al. "Yards increase forest connectivity i n urban landscapes" Landscape Ecol 10.1007/s10980-019-00923-7\n- Julien Ca rlier et al. "Using open-source software and digital imagery to efficientl y and objectively quantify cover density of an invasive alien plant specie s" Journal of Environmental Management doi: 10.1016/j.jenvman.2020.110519\ n- Victor Rincon et al. "Proposal of new Natura 2000 network boundaries in Spain based on the value of importance for biodiversity and connectivity analysis for its improvement" Ecological Indicators doi: 10.1016/j.ecolind .2021.108024\n- Giuseppe Modica et al. "Implementation of multispecies eco logical networks at the regional scale: analysis and multi-temporal assess ment" Journal of Environmental Management\, Volume 289\, 2021\, doi: 10.10 16/j.jenvman.2021.112494\n- Vogt\, P. and Riitters\, K. "GuidosToolbox: Un iversal digital image object analysis (2017) European Journal of Remote Se nsing" doi: 10.1080/22797254.2017.1330650\n- Peter Vogt et al. "GuidosTool box Workbench: spatial analysis of raster maps for ecological applications " (2022) Ecography. doi: 10.1111/ecog.05864\n- Soille\, P. "Morphological Image Analysis: Principles and Applications" (2004). Springer\, doi: 10.10 07/978-3-662-05088-0\n- D. De Marchi and P. Soille\, "Advances in interact ive processing and visualisation with JupyterLab on the JRC big data platf orm (JEODPP)"\, in Proc. of BiDS'19\, 2019. doi: 10.5281/zenodo.3239239\n- Kempeneers\, P. et al. "pyjeo: A Python Package for the Analysis of Geosp atial Data" ISPRS Int. J. Geo-Inf. 2019. doi: 10.3390/ijgi8100461 DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Morphological Spatial Pattern Analysis: Open Source Release - soill e\, Peter Vogt URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/QACV83/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-UJJRQJ@talks.osgeo.org DTSTART;TZID=CET:20220825T113000 DTEND;TZID=CET:20220825T120000 DESCRIPTION:Image semantic segmentation focuses on the problem of properly separating and classifying different regions in an image depending on thei r specific meaning or use\, e.g. belonging to the same object. It is worth to notice that in general segmentation is a ill posed problem: it is not possible to provide a unique solution to such problem\, different solution s can typically be acceptable\, depending on the segmentation criterion wh ich is applied. Nevertheless\, regularization techniques are typically use d to reduce the issues related to ill posedness\, hence ensuring the compu tability of a unique solution. In the case of semantic segmentation\, ill posedness is also reduced by the specific data and object interpretation t hat shall be included in the semantic part of the data. \nIt is also worth to notice that image semantic segmentation tools can be useful in many se veral applications\, related both to the interpretation of images themselv es\, but also of other entities related to such images. The latter is for instance the case of a point cloud\, whose objects and areas are also desc ribed by some images. In this case\, a proper image semantic segmentation could be back projected from the images to the point cloud\, in such a way to exploit such process to properly segment the point cloud itself.\nAuto matic image semantic segmentation is a quite challenging problem that nowa days is usually handled by taking advantage of the use of artificial intel ligence tools\, such as deep learning based neural networks. \nThe availab ility of reliable image segmentation datasets plays a key role in the trai ning phase of any artificial intelligence and machine learning tool based on the image analysis: indeed\, despite artificial intelligence tools can currently be considered as the state of the art method in terms of recogni tion and segmentation ability\, they do require a huge size learning datas et in order to ensure reliable segmentation results.\nThe developed graphi cal user interface aims at supporting the semi-automatic semantic segmenta tion of images\, hence easing and speeding up the generation of a ground t ruth segmentation database. Then\, such database can be of remarkable impo rtance for properly training any machine or deep learning based classifica tion and segmentation method.\nDespite the development of the proposed gra phical user interface has been originally motivated by the need of easing the process of producing a ground truth segmentation and classification of plastic objects in maritime and fluvial environments\, within a project a iming at reducing plastic pollution in rivers\, the developed tool can act ually be used in contexts that are more general.\nIndeed\, the interface s upports in particular two types of quite specific operations: 1) segmentin g and identifying objects in a single image\, 2) exporting previously obta ined results in new images\, while also enabling the computation of certai n related parameters (e.g. navigation related\, such as tracking the same object over different data frames). Different types of images are supporte d: standard RGB\, multispectral images (already available as TIFF (Tagged Image File Format) images) and thermal ones.\nFor what concerns the semant ic segmentation of a single image\, several alternative segmentation optio ns are supported\, starting from manual and going to semi-automatic segmen tation methods. First\, the manual segmentation of the objects is ensured by means of properly inserted polylines. Then\, intensity based and graph based methods are implemented as well. On the semi-automatic side\, two to ols are provided: a) a machine learning based method\, exploiting few clic k choices by the user (implementing a rationale similar to that in (Majumd er et al.\, “Multi-Stage Fusion for One-Click Segmentation”\, 2020)\, i.e. aiming at minimize the user input)\, b) when images are periodically acquired by a UAS\, at quite high frequencies\, two successive frames are expected to be not that different from each other. Consequently\, the syst em aims at determining the camera motion between different frames\, and us ing machine learning tools to properly extend and generalize the results i n the previous image to those of the new one.\nThe latter method opens to a wider scenario\, where some more information may come by the availabilit y of consecutive frames. In particular\, such additional information that could be determined by properly analyzing consecutive frames could be used to: assess and track the UAS movements while acquiring the video frames\, increase the automation in the segmentation and classification process of an object.\nOverall\, the developed graphical user interface is expected to be useful to support the semi-automatic identification of objects\, and to help determining the UAS and the object movements as well.\nDespite fu ll autonomous image semantic segmentation would clearly be of interest\, i ts development seems to be quite challenging. Nevertheless\, future invest igations will be dedicated to these aspects\, in order to increase the pro cedure automation level.\nThe simulator will be freely available for downl oad from the website of the GeCo (Geomatics and Conservation) laboratory o f the University of Florence (Italy). DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Development of a graphical user interface to support the semi-autom atic semantic segmentation of UAS-images - Andrea Masiero URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/UJJRQJ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-KB7UV9@talks.osgeo.org DTSTART;TZID=CET:20220825T113000 DTEND;TZID=CET:20220825T120000 DESCRIPTION:1. Introduction\n\nFor most end-users\, the term ‘software’ is equivalent with executing a given application to obtain a desired resu lt. Moreover\, the highest importance is usually attributed to the softwar e being free to use. Besides intuitive use\, a key requirement for success and wider acceptance of a software application is easy access\, which is often facilitated though open-source projects. While users naturally only care about stability and functionality of the software\, software develope rs often see their task completed once the application reaches a certain d egree of maturity and its source code is made available. However\, in addi tion to ease of use and targeted software development\, a third component in the life cycle of software design [Vogt 2019] is the software provision . The importance of adequate software dissemination entails a wide range o f aspects\, which are often undervalued but are crucial to best meet end-u ser expectations and to achieve the highest application acceptance. \nIn t his manuscript\, we outline a perspective on approaches to appropriately a ddress issues of software provision aimed at promoting software in an effi cient way. We illustrate the motivation and features of various aspects of software provision on the recently published software GWB [Vogt et al.\, 2022] [1] and its implementation on the FAO cloud computing platform SEPAL [2]. \n\n\n2. Software provision\n\nThis section summarises reflections o n various aspects when disseminating a software application.\n \n* Source code: The provision of the source code is often perceived as a final produ ct delivery. However\, most end-users cannot make any use of the source co de because they do not understand the programming language\, do not know h ow to compile it or how to properly link required dependencies. The large number of Linux distributions provides an additional challenge due to vary ing inter-dependencies of distribution-specific compiled libraries and pac kaging policies. Packaging - the conversion of source code into a function al executable binary - is a science on its own and is beyond the skills of a typical end-user. \n\n* Target platform: Maximum outreach is achieved through a software setup that will work on as many platforms and operating systems (OS) as possible. \n\n* Software packaging: The scope of packagin g is to bundle the entire application into a single archive\, including or linking OS-specific dependencies\, pre- and post-installation instruction s\, and the integration into the OS via menu entries. Examples are rpm and deb-packages (Linux)\, dmg-packages (macOS)\, and exe/msi-packages (MS-Wi ndows). Packaging allows for efficient system-wide software management: in stallation\, upgrades\, and removal of the application and provides applic ation access to all OS users. However\, it also requires administrator rig hts\, which are not available on many secured or closed IT environments\, such as in government agencies\, where users may fully access a limited OS -space only\, i.e.\, their $HOME directory. Yet\, this situation can be ad dressed by setting up the software and all required components in a self-c ontained single directory\, which is then compressed into a self-extractin g installer. Any user can then download such a standalone installer\, extr act it and have full access to the application without administrator right s. A similar result can be achieved with a Docker container [3].\n\n* Docu mentation: Documentation is crucial for software adoption\, including a us er manual\, product sheets with application examples\, and guided instruct ions in workshop material. The manual should also be completed by a user c ommunity to help end-users answer the questions they will raise while usin g the software. Developers of the tools should be actively involved in tac kling these questions [Srba et al.\, 2016].\n\n\n3. Application\n\nThe Gui dosToolbox Workbench (GWB) [Vogt et al.\, 2022] provides various generic i mage analysis modules as command line scripts on 64-bit Linux systems. In this section we use GWB to exemplify how we addressed the software provis ion points mentioned before.\n* Source code: in addition to the distributi on-independent compiled executables we provide the plain text source code for all modules in a dedicated subdirectory of the application.\n* Softwar e packaging: all modules are launched via customised Bash scripts and setu p in the IDL programming language [4]. Because IDL provides its own set of highly efficient processing libraries\, all scripts and required librarie s can be stored in a single\, distribution-independent application directo ry. Combined with customised packaging setup-files\, this archive is then converted into distribution-specific packages for common Linux distributio ns. In addition\, we provide a generic standalone installer using the make self [5] archiving tool. The standalone installer can be used on any Linux distribution for either\, system-wide installation\, or installation in u ser space on restricted systems\, i.e.\, under $HOME. All installer packag es include two sample images and module-specific usage descriptions\, aime d at generating sample output illustrating the features of each module.\n* Target platform: With its focus as a server-application\, GWB is setup f or the Linux OS\, which can also be used on a regular desktop PC. The inst allation on cloud computing platforms\, including an interface to upload/d ownload personal data\, greatly enlarges the outreach into the user commun ity and allows usage of the software from any device having a Web browser and Internet access. A Jupyter [Kluyver\, T.\, 2016] based application was developed within the SEPAL platform [2]. This application uses widgets an d interactive displays to help the end-user provide personal data to the s oftware. This application is developed in Python using the sepal-ui [8] fr amework\, embedding a fully independent set of requirements. As the applic ation is run using the voila dashboarding tool [QuantStack\, 2019]\, the e nd-user is never confronted with the CLI\, vastly improving the scope of p otential end-user to non-IT experts.\n* Documentation: the project homepag e [1] provides a brief overview and installation instructions. Highly deta iled usage instructions are available on SEPAL for the command-line use [6 ] and the interactive Jupyter dashboard [7].\n\n\n4. Conclusion\n\nSoftwar e provision is an often overlooked yet critical component in software desi gn. It comprises various aspects\, which when addressed appropriately\, ca n make a great impact in the promotion\, outreach and acceptance of a soft ware application. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Tackling the challenges of software provision - Peter Vogt\, pierri ck rambaud URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/KB7UV9/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-MENQ73@talks.osgeo.org DTSTART;TZID=CET:20220825T120000 DTEND;TZID=CET:20220825T123000 DESCRIPTION:### Motivation & Contribution\n\nMobility researchers using GPS first obtain raw coordinates and timestamps from GPS instead of the varia bles they're interested in. Conversion is needed to acquire\, for example\ , the time spent out of home\, the number of revisited places\, or the tot al time spent on the go. All of these rely on the ability to precisely ide ntify stops and trips and are therefore fundamental when it comes to mobil ity research.\n\nThe commonly adopted strategy involves a combination of a distance and a time threshold to identify significant places (Ash- brook and Starner\, 2002\, Ye et al.\, 2009). Here\, GPS records are grouped tog ether\, if they lie within such a pre-defined radius and time. When we pla nned the technical basis for a mobility intervention study\, we tested sev eral existing systems based on this approach. We observed\, on the one han d\, significant segmentation of the identified stops\, due to the relative ly large amount of signal noise. On the other hand\, we could only identif y stops having a duration greater than a pre-defined time threshold\, usua lly five minutes. Hence\, the temporal resolution of this analysis was sub -optimal. Reduced this threshold\, lead to an increased number of falsely identified stops (false positives) and segmentation. To solve this\, we de veloped a modern stop and trip identification algorithm.\n\nFor a human an notator\, this task is fairly easy: when dwelling on a spot\, the GPS reco rds scatter around the true position because of its imperfect signal. Reco rds obtained from a trajectory through an environment are clearly distingu ishable - although the imperfect signal diverges from the true position si milarly. This observation inspired us to create a new algorithm around the idea of investigating the signal patterns\, and therefore the geometric p roperties of the signal noise.\n\nWe describe the algorithm's mechanics in detail and discuss its design decisions. Further\, we provide benchmark r esults against established and frequently cited libraries.\n\n### Approach \n\nFundamentally\, the algorithm is based on a multitude of different\, g eometric analyses. Each analysis method is applied to a rolling window of subsequent GPS samples. For example\, one metric evaluates the ratio betwe en total path length and the bounding box of the set. Another is concerned with the mean angles between the point vectors. Subsequently\, all metric s are combined to form a majority-based classification decision for each i ndividual GPS sample. This way\, the different methods can compensate for a wrong decision of a minority of the metrics.\n\nIf available\, the accel eration of the device is also taken into account to exclude unambiguous pe riods of non-movement. Therefore\, we created a simple metric that transfo rms a three-dimensional vector of x\, y\, and z acceleration into a motion score that expresses the amount of physical movement of the recording dev ice.\n\nThe labels of individual GPS samples are then used to aggregate st op intervals. In the last step\, the resulting stop intervals are filtered . Therefore\, each interval is compared against the neighboring ones to de cide if a) it should be kept as it is\, b) if it should be merged with a c lose stop-interval to reduce segmentation\, or c) if it should be discarde d.\n\n### Validation\n\nTo test the accuracy of our analysis approach\, we benchmarked the system against the built-in methods for stop and trip det ection of Moving Pandas (Graser\, 2019) and Scikit Mobility (Pap- palardo et al.\, 2019). These represent a large share of the most commonly used to ols for mobility research.\n\nTo test the classification performance\, we created a large dataset containing trajectories from over 126 days of ever yday life and captured 692 stops.\n\nThis reference acts as ground truth f or the comparison of different frameworks. We investigate sample-by-sample classification metrics (accuracy\, precision\, recall/sensitivity\, speci ficity\, and F1) and stop/trip interval specific metrics (stop-counts\, se veral metrics to quantify the number of detected stops against the referen ce\, such as % matched reference stops\, absolute duration error\, missed stop duration\, absolute start deviation\, absolute end deviation\, and po sition deviation). To ensure a fair comparison of the algorithmic approach es\, we did not take the acceleration data into account\, as the reference systems do not support filtering stop and trip intervals using this kind of data.\n\n### Results & Discussion\n\nOur Stop & Go Classifier outperfor ms other systems in most metrics: it identifies more stops correctly\, the stops it misses are shorter in duration\, and the start and end times of the identified stops are almost twice as precise as the closest competitor .\n\nThe core ideas of the system are a) it uses unfiltered\, raw GPS data \, b) it analyzes these regarding their geometric properties\, and c) it u ses multiple scoring mechanisms to create one solid classification.\n\n### Code Availability\n\nThe Stop & Go Classifier is free software under a BS D 3-Clause license. The repository includes a reference implementation of the algorithm and small usage examples: https://github.com/RGreinacher/Sto p-Go-Classifier DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Making Sense of the Noise: Integrating Multiple Analyses for Stop a nd Trip Classification - Robert Spang URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/MENQ73/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-VJPFRN@talks.osgeo.org DTSTART;TZID=CET:20220825T120000 DTEND;TZID=CET:20220825T123000 DESCRIPTION:Free and open source software for geospatial analysis (FOSS4G) supports burgeoning possibilities for practicing open and computationally reproducible human-environment and geographical research (Singleton et al 2016).\nOpen and reproducible research practices may accelerate the pace o f scientific discovery and enhance the scientific community's functions of knowledge verification\, correction\, and diffusion (Rey 2009\, Kedron an d Holler 2022).\nGeospatial metadata provides the foundation for reproduci bility and open science and accordingly\, requires more support in open so urce geospatial software.\nFollowing Wilson and others' (2021) five star g uide for reproducibility\, researchers can achieve four stars by conductin g research with open data and software and documenting metadata according to the standards of the International Organization for Standardization (IS O) and OGC (Open Geospatial Consortium).\nFor Tullis and Kar (2021)\, meta data is the key to documenting the provenance of research data artifacts\, preserving information about every detail of data creation and transforma tion.\nWilkinson and others' (2016) FAIR Guiding Principles for scientific data management enumerate functions for metadata in each of the principle s for research: findable\, accessible\, interoperable\, and reusable.\nHow ever\, open source geospatial software platforms generally lack the tools necessary for mainstreaming geospatial metadata into the full research wor kflow in support of more efficient research work and enhanced reproducibil ity and open science.\nThis research on metadata is part of a larger human -environment and geographical sciences reproducibility and replicability ( github.com/HEGSRR) project aimed at conducting formal reproduction and rep lication studies in the geographical sciences and integrating reproducibil ity into undergraduate and graduate level curricula in research methods.\n \nFollowing the National Academies of Science\, Engineering and Medicine ( NASEM\, 2019)\, a reproduction study aims find the same results using the same data and methodology as a published study.\nA replication study aims to test the findings of a published study by collecting new data and follo wing a similar methodology\, which may intentionally modify one or more re search parameters.\nTogether\, reproduction and replication studies offer a deep understanding of the original research\, test its credibility and g eneralizability\, and enhance the self-corrective mechanisms of the scient ific community.\nMetadata is information about data\, including essential contextual information about the data's spatial structure\, attributes\, c reation\, maintenance\, access\, licensing\, and provenance.\n\nA key comp onent of reproducible research is an executable research compendium contai ning all of the data\, code\, and narrative required to compile a research publication from raw data (Nust and Pebesma 2021 and the Opening Reproduc ible Research Project).\nComputational notebooks like Jupyter notebooks or R Markdown are commonly used to interweave narrative with code in executa ble compendia.\nIn order to maximize replicability and inferential power\, the research compendium should begin with a pre-registered research plan prior to data collection\, requiring researchers to fully specify metadata for all of the research data and analyzes that they intend to create (Nos ek et al. 2018).\nIt is recommended to store compendia in version tracking systems like Git in order to preserve a full history of changes to the re search project.\nFinally\, the compendium should be published parallel to academic publications so that other researchers can independently re-run\, check and verify the analysis\, or incorporate the research in future pro jects.\nIn order to maximize the findability and legibility of the researc h compendium for both humans and machines\, the overall repository and eac h of its components must be meticulously documented with metadata accordin g to international standards (Wilkinson et al. 2016\, Wilson et al. 2021). \n\nIn this three-part research paper\, we focus on metadata in research c ompendia and related research products through all phases of the research workflow.\nFirst\, we specify ideal requirements of geospatial metadata in support of reproducible research workflows and open science.\nWe consider metadata needs at each step of the research process\, including proposal writing\, pre-analysis registration\, ethics review board approval\, data collection and analysis\, publishing\, and reproducing published research. \nThe metadata software needs assessment is based on literature review of reproducibility and open science\, and on teaching and practicing reproduc ibility with geographic methods.\n\nSecond\, we review the Dublin Core and International Organization for Standardization (ISO) geospatial metadata standards and popular open source platforms for geospatial research and th eir support for the requirements of geospatial metadata articulated in the first part.\nThe scope of the review includes metadata functionality avai lable through spatial analysis software platforms\, including R\, Python\, QGIS\, GRASS and SAGA\; and it also includes metadata or cataloging tools \, including GeoNetwork\, GeoNode\, the USGS Metadata Wizard\, and mdedito r.\n\nFinally\, we articulate a vision for open source geospatial metadata software development in support of open and reproducible human-environmen t and geographical research.\nIn this vision\, metadata software tools sha ll integrate with executable research compendium to assist researchers wit h their workflow from inception to publishing and archiving.\nThe vision b uilds off our HEGSRR project\, in which we independently reproduce and rep licate published studies with open source geospatial software\, integrate reproduction and replication studies into project-based geographic informa tion science courses\, and develop curricula and infrastructure for reprod ucible research.\nEach section of the paper is thus supplemented with expe riences and examples drawn from the HEGSRR project.\nOf particular relevan ce\, we have already completed seven reproduction or replication studies w ith graduate and undergraduate students using open source geospatial softw are\, encountering numerous barriers caused by inadequate use or documenta tion of metadata in research planning\, execution\, and archiving.\nWe hav e also developed a template Git repository compendium for reproducible res earch and prototyped its use in our studies and teaching\, discovering sof tware barriers to documenting metadata and opportunities to integrate meta data into more efficient and reproducible research practices.\n\nWe have s elected FOSS4G for this research paper in hopes of reaching both the acade mic audience and developer audience at the conference. We hope to raise aw areness in the academic audience of the critical importance of geospatial metadata in each stage of the research workflow. We hope to raise interest in the open source geospatial software community for collaboration on imp roved support for geospatial metadata in research workflows. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Mainstreaming metadata into research workflows to advance reproduci bility and open geographic information science - Joseph Holler URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/VJPFRN/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-M9WUSQ@talks.osgeo.org DTSTART;TZID=CET:20220825T123000 DTEND;TZID=CET:20220825T130000 DESCRIPTION:Currently\, various kinds of geospatial data are provided as op en data/or map tile data. This implies that geospatial data have become ea sier to obtain and use than older data with traditional licenses and forma ts. By combining map tile data with Web Mapping clients\, such as Open Lay ers and Leaflet\, we can browse maps of any location without complicated p rocedures\, i.e.\, downloading data\, transforming coordinate system\, ext racting area of interest\, and installing software. These web mapping tech nologies have been developed mainly in the field of human interpretation o f map images.\n\nIn addition\, there has been the development of technolog ies for usage of map tiles not only background image of Web Mapping\, but also processing and visualizing data in a client-side Web browser. The G eological Survey of Japan (GSJ) has proposed Data PNG and related format ( 1) for distributing data as map tiles. This format provides data in the PN G format which allows retaining numerical attributes\, such as temperature \, elevation\, and geological classification. It is also possible to devel op web applications with good responsiveness to user requests and promote diverse data use (Nishioka\, 2019). The GSI published a demonstration site for the Data PNG tile (2) (3). Kitao (2020) developed a Web application f or visualizing mapping point cloud data provided as Data PNG. Mapbox Terra in-RGB provides elevation data in PNG format and Mapbox GL JS visualizes t hese data as a 3D map. These applications were implemented with the capabi lities of WebGL. WebGL is a cross-platform\, open web standard JavaScript API for 2D and 3D graphics in modern Web browsers that allows the GPU-acce lerated usage of image processing without the use of plug-ins.\n\nAs descr ibed above\, there are many applications for client-side data visualizatio n using WebGL. However\, an implementation of data analysis using WebGL\, especially the map algebra function\, has not been progressively developed . This paper aims to develop map algebra functions for Data PNG tiles with WebGL in a client-side Web mapping system. \n\nIn this study\, we attempt ed to develop map algebra functions for vineyard suitability assessment in Nagano Prefecture\, Japan. In recent years\, “Japan Wine\,” made excl usively from grapes grown in Japan\, has been gaining international recogn ition\, and new wineries in Japan are also increasing. Thus\, there is an urgent need to provide information to support the selection of appropriate vineyard sites and grape varieties. There have been attempts to assess th e suitability of agricultural fields for crop production applying GIS. Des pite these efforts\, suitable site evaluation of vineyards has not been fu lly disseminated due to the lack of the following components: (1) sufficie nt quality\, quantity\, and accurate information necessary to determine th e suitable site\; (2) appropriate criteria for evaluating suitable sites b ased on the information\, and (3) methods for providing evaluation results to consumers\, such as new farmers. Therefore\, we attempt to develop the client-side Web mapping system\, using only a Web browser without any spe cial skills and specific software\, which enables new farmers to evaluate suitable sites for vineyards.\n\nA variety of environmental information is required for assessing vineyard suitability. In this report\, we converte d spatial information about geology\, soils\, topography\, and meteorology \, which is available as open data\, to Data PNG tiles with FOSS4G tools\, such as QGIS\, TileMill\, and MBUtil for suitability assessment. The vine yard suitability assessment system consists of the following map algebra f unctions:\n\n1) Generate assessment values from a Data PNG tile layer by p erforming quadrature calculations\, specifying the order of operations usi ng parentheses\, and classification based on logical operation formulae.\n \n2) Comprehensive assessment function that performs a quadratic calculati on\, specifies the order of operations using parentheses\, and classificat ion based on logical operation formulae between the layers generated by th e above procedure.\n\n\n3) Vineyard suitability visualize function based o n the comprehensive assessment\n\nThe Web Mapping interface was developed with Leaflet\, which has a graphical interface to input map algebra formul ae and a function to display and export the image of vineyard suitability based on the comprehensive assessment result described above. A prototype of the vineyard suitability assessment system is available in the followin g URL: https://wata909.github.io/web-map-algebra/index_e.html.\n\nIn this system\, data used for assessment are provided as Data PNG tiles\, and a m ap algebra function is performed by WebGL on a client-side. In other words \, unlike many other Web Mapping systems\, our system does not require ser ver-side systems and/or middleware and can be operated using only a web br owser. This means that various entities can be operated on the same system at a low cost or on a free Web service\, such as GitHub pages. Additional ly\, the functions implemented in this system can be applied to various ev aluations using the map algebra functions.\n\nHowever\, our system contain s only seven items for assessing suitable locations\, which is not suffici ent. The arithmetic functions of the system are limited to four arithmetic and logical operations\, and it is not capable of implementing the comple x model calculations required for highly realistic assessment. We are curr ently constructing a suitability assessment model using machine learning\, information on the distribution of vineyards obtained from field surveys\ , and various environmental factors derived from field monitoring and publ ished open data. In the future\, we will use these data to improve the sys tem and make it more practical. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Client-side Web Mapping system for vineyard suitability assessment - Nobusuke Iwasaki URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/M9WUSQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-TNWYJL@talks.osgeo.org DTSTART;TZID=CET:20220825T123000 DTEND;TZID=CET:20220825T123500 DESCRIPTION:The survey took place in part of the so-called Roman Villa of C aposele\, also known as Villa Rubino (Giuliani and Guaitoli 1972\; Cassier i 2015). The Villa\, built by the Dukes of Marzano and subsequently passed into the hands of Charles of Ligny\, Prince of Caposele\, was purchased b y Ferdinand II of Bourbon in 1845\, with the aim of making it a luxurious summer residence. The building overlooks the inlet of Caposele\, where the re must have been a small harbour\, and is squeezed between the Via Appia and the sea. To the west of the small port are the remains of an imposing structure with a central courtyard\, datable to the 1st century B.C.\, whi ch scholarly tradition has identified as Cicero's Academy or School\, alth ough it is probably a horreum\, testifying to the utilitarian vocation of this area of the villa. In later phases\, while retaining its intended use \, the horreum would be incorporated into a residential building complex t ogether with other structures further to the west that\, too\, may have se rved as warehouses in the earlier phase. To the east of the marina is the residential area\, the area in which the survey operations were concentrat ed. Here\, on a front about 140 metres long\, there are a series of rooms with barrel vaults that were probably part of the basis villae of the buil ding. In two of these rooms are the so-called minor and major nymphaea. Th e first consists of an almost quadrangular room with a roof supported by f our Doric brick columns\; on the back wall\, in a large niche\, spring wat er gushes out. The wall decorations include stucco\, shells and incrustati ons of glass paste and small stones. The main nymphaeum\, on the other han d\, is divided into three naves and covered with a rounded coffered vault supported by Doric columns. The large niche at the bottom of the nymphaeum contains a pool of spring water\; the floor is in white mosaic with polyc hrome dots. These nymphaeums constitute the focus of the intervention. \nI n front of this front there was a very large fishpond\, which ran into the sea for about one hundred metres in length\, with a width of over 200.\nB ecause of its architectural features and good state of preservation\, the central body of the monument has always been a great attraction for visito rs and scholars\, many of whom have left descriptions and drawings in thei r diaries.\n\n\nThe two nymphaeums have to be surveyed both for conservati on and study purposes and in order to allow a virtual visit\, which is par ticularly important since they are located inside a private property. As a lready described\, the structure is complex\, with a succession of rooms a nd environments in an archaeological complex extending approximately 480 m etres in an east-west direction and approximately 50 metres in a south-nor th direction. The survey of such an extension and such an articulation wit h consolidated techniques such as terrestrial laser scanning would probabl y have required days of work\, and for this purpose we wanted to test the possible use of the most modern SLAM techniques\, in particular using a GE OSLAM Zeb Horizon\, totally transportable by an operator and with a range of up to 100 metres (https://geoslam.com/solutions/zeb-horizon/).\nIn orde r to compare the times\, modes\, precision and accuracy of the point cloud thus obtained\, we took advantage of the possibilities provided by the op en software "Cloud Compare 2.11.3 64 bit version"\, which allows us to com pare point clouds of different origins. Cloud Compare allows comparisons t o be made with various methods of calculating distance and to estimate pre cision and accuracy separately\, allowing one cloud to be fitted to the ot her or to be compared while remaining within their absolute coordinates.\n In the present experimentation it was therefore decided to survey both nym phaea with the "GEOSLAM"\, also surveying all the internal connecting room s and corridors between these two environments. The whole survey was carri ed out in a few tens of minutes and therefore the survey continued over mo st of the exterior of the entire structure.\nThe survey of the entire comp lex was not carried out because the main interest of this project was to t est the SLAM technology and validate its precision and accuracy in compari son with more consolidated techniques.\nFor comparison\, only the major ny mphaeum was surveyed with a more consolidated laser\nFaro" terrestrial sca nning laser.\nIn order to verify the validity of the Slam also on the exte rnal part\, a survey was carried out using a DJI Matrix drone with laser s canning. Finally\, the same survey was also carried out with an optical ca mera on the same Matrix drone and with the most widely used drone for phot ogrammetry\, i.e. the "Phantom 4 pro"\, also by DJI.\nAll the surveys were framed with respect to the same network of ground control points\, in ord er to refer them to the same framing system and be able to assess their pr ecision and accuracy. \nIt should be noted that Slam was only able to stat ion a few of the GCPs while\, as can be easily guessed\, the drones acquir ed practically all of them.\n\nThe comparison showed very limited deviatio ns whose statistical validation is in progress\, demonstrating that the SL AM technique can advantageously be used in such vast archaeological comple xes where the completeness of the survey is more important than millimetri c accuracy. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Efficient three-dimensional survey techniques and their comparison in open software in the archaeological test site of "Ninfeo maggiore" and "Ninfeo minore" of Formia (latina\, Italy) - Valerio Baiocchi URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/TNWYJL/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-T9LLQV@talks.osgeo.org DTSTART;TZID=CET:20220825T123500 DTEND;TZID=CET:20220825T124000 DESCRIPTION:1. INTRODUCTION\nIn a new initiative to deliver higher-quality data and support improved geospatial analysis\, the U.S. Geological Survey (USGS) is upgrading the elevation and hydrography datasets into the 3D Na tional Topography Model (3DNTM)\, which will include fully integrated hydr ography and elevation. The USGS 3D Elevation Program (3DEP) recently compl eted acquisition of interferometric synthetic aperture radar (IfSAR) eleva tion data at 5-meter spatial resolution for Alaska (USGS\, 2022). Other pa rts of the United States are being mapped at higher resolution with lidar- derived elevation data. \n\nUnder the 3DNTM\, new hydrography data are acq uired through methods that derive or extract the features directly from be st available 3DEP elevation data to ensure proper integration of the hydro graphy and elevation layers. By applying specifications for deriving 1:24\ ,000 or larger scale hydrography from high resolution elevation data (Arch uleta and Terziott\, 2020\; Terziotti and Archuleta\, 2020)\, a tenfold in crease in the number of features in the National Hydrography Dataset (NHD) is expected. Consequently\, highly automated machine learning methods to extract and validate the hydrography data collection are being investigate d.\n\nXu et al. (2021) demonstrated that the U-net fully convolutional neu ral network (Ronneberger\, Fischer\, and Brox\, 2015) is capable of extrac ting hydrography from lidar elevation data with 80 to 90 percent accuracy. Stanislawski et al. (2021) applied a similar U-net model using several If SAR and IfSAR-derived input layers to predict hydrography for a 50-watersh ed study area in northcentral Alaska\, where 68 percent average F1-score a ccuracies were achieved on test watersheds. Further work to refine U-net p redictions of hydrography using IfSAR for the same 50-watershed area in Al aska achieved average F1-scores for test watershed of better than 80 perce nt (Stanislawski et al.\, 2022). Research presented in this paper builds u pon this earlier work by testing transfer learning methods and scaling-up U-net predictions of hydrography from IfSAR for other areas of Alaska usin g workflows in high-performance computing environment. \n\n2. METHODS\nA w orkflow was developed to automate downloads and processing of IfSAR-derive d tiles of digital elevation model (DEM)\, digital terrain model (DTM)\, a nd orthorectified intensity (ORI) data for user-selected watersheds from t he 3DEP database. The workflow mosaics common tiles and derives several ra ster data layers from the DEM that are related to surface hydrology\, such as topographic position index and shallow water channel depth. Overall\, seventeen data layers are generated and coordinated with identical raster projection systems. The layers were used in U-net modelling for predicting hydrography for the 50-watershed Kobuk River study area (Stanislawski et al.\, 2022). In this study a transfer learning process begins with the Kob uk River U-net model and subsequently includes additional training data fr om outside the Kobuk area. Hydrography predictions are then generated from the transfer learning model and assessed. Several levels of refinements t o training data are tested and the accuracy of predictions are assessed. R eference data consist of vector hydrography features derived by USGS contr actors.\n\nThe data processing workflows are implemented with Python\, lin ux shell scripts\, and opensource software libraries such as the Geospatia l Data Abstraction Library (GDAL). Neural network modelling is implemented through TensorFlow\, and data processing is completed on a 12-node linux cluster and through the GPU nodes of the USGS Tallgrass computing faciliti es (https://hpcportal.cr.usgs.gov/hpc-user-docs/Tallgrass/Overview.html).\ n\n3. DISCUSSION\nMapping hydrography for the state of Alaska is a dauntin g task\, given its vast area and terrain that is difficult to navigate. Bi g challenges with large high-quality datasets are well suited to take adva ntage of recent advancements in neural networks (Usery et al.\, 2021). Thi s research demonstrates the tremendous potential to improve and speed up m apping of surface water features in Alaska\, and elsewhere in the world ha ving challenging terrain and limited resources. \n\nReported accuracy scor es measure how well a machine can reproduce hydrography generated with met iculous editing by numerous subject matter experts. It is not a score of h ow well the surface water features are mapped by the model. The human fact or in contemporary broad scale mapping efforts cannot be ignored and warra nts consideration as a source of uncertainty in the related accuracy metri cs. How well the maps fit what is on the ground can only be definitively c onfirmed by being on the ground at any given point in time\, as hydrologic conditions are constantly in flux. Thus\, the work here could be used as an aid to human cartographers in their efforts to interpret what is import ant to the map user. \n\nThis work could also benefit change detection eff orts. As new and better elevation data are collected\, automated strategie s such as the model presented here could be used to identify regions with significant changes in surface water distribution. This type of automation would be valuable to maintain an accurate national map over time and help address the numerous challenges that society faces related to hydrology. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Scaling-up deep learning predictions of hydrography from IFSAR data in Alaska - Larry Stanislawski URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/T9LLQV/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-ZBGHFH@talks.osgeo.org DTSTART;TZID=CET:20220825T124000 DTEND;TZID=CET:20220825T124500 DESCRIPTION:1. Background\nOpenStreetMap (OSM) can supply useful informatio n to improve land use/land cover (LULC) mapping (Arsanjani\, 2013\; Schult z\, 2017\; Zhou\, 2019). A dictionary is needed to convert each OSM tag in to an LULC class. However\, such a dictionary was mostly created subjectiv ely or with only one pair of OSM and reference datasets. As a result\, the existing dictionaries may not be applicable to other study areas. This st udy designed four measures: sample count\, average area percentage\, sampl e ratio and average maximum percentage\; and used multiple pair of OSM and reference datasets to create a dictionary. 50 pan-European metropolitans were involved for testing and 1409 different OSM tags were found. We furth er found that: 1) Only a small proportion of OSM tags play a decisive role for LULC mapping. 2) An OSM tag may correspond to multiple different LULC classes\, but the issue that which and how different LULC classes corresp ond to each OSM tag can be determined. Moreover\, not only the proposed di ctionary is useful for various applications\, e.g.\, producing LULC maps\, obtaining training and/or validation samples\, assessing the quality of a n OSM dataset\, but also the approach to creating this dictionary can be a pplicable to different study areas and/or LULC datasets.\n\n2. Data\nOSM d atasets of the 50 metropolitans were acquired for free from http://downloa d.geofabrik.de/index.html in June 2020. Corresponding reference datasets (called urban atlas or UA) were available from https://land.copernicus.eu /local/urban-atlas/urban-atlas-2012/# in June 2020 freely.\n\n3. Methodolo gy\nThe tenet of our approach is to use multiple pairs of OSM and referenc e datasets for creating an OSM-LULC dictionary. In each pair of datasets\, an OSM tag may correspond to different LULC classes\, it is therefore nec essary to determine which is the most appropriate LULC class for each OSM tag. we assumed that most OSM tags have been tagged by volunteers correc tly (Zhou et al. 2019). Following this assumption\, the way to determine t he most appropriate LULC class for each OSM tag includes two steps. Firstl y\, all objects of an OSM tag are intersected with those of different LULC classes\, respectively. After that\, the LULC class with the maximum in tersecting area is viewed as the most appropriate one for this OSM tag. Fo ur attributes and four measures are designed to describe an OSM- LULC dict ionary. They are: Tag ID\, Tag Name\, Class ID and Class Name in terms of attributes\; and Sample Count\, Average Area Percentage\, Sample Ratio and Average Maximum Percentage in terms of measures. They are introduced as f ollows: 1. Tag ID denotes the ID of an OSM tag\, 2. Tag Name denotes the n ame of an OSM tag. 3. Class ID denotes the ID of an LULC class. 4. Class N ame denotes the ID of an LULC class.5. Sample Count (SC) denotes how frequ ent an OSM tag is appeared in different study areas or datasets. 6. Averag e Area Percentage (AAP) denotes the average of the area percentages of an OSM tag in multiple different OSM datasets. 7. Sample Ratio (SR) denotes t he percentage of study areas or datasets that an OSM tag corresponds to an LULC class. 8. Average Maximum Percentage (AMP) denotes the average of al l the maximum percentage in different study areas or datasets.\n\n4. Concl usion and application\nThis study proposed an approach to creating an OSM- LULC dictionary. The tenet of this approach was to involve multiple pairs of OSM and reference datasets for the analysis. First of all\, each pair o f OSM and reference datasets were intersected and the most appropriate LUL C class for each OSM tag was determined. Then\, the four measures\, i.e.\, sample count (SC)\, average area percentage (AAP)\, sample ratio (SR) and average maximum percentage (AMP)\, were designed and calculated based on multiple pairs of OSM and reference datasets. More precisely\, a total of 50 pairs of OSM and reference datasets in pan-European metropolitans were chosen as study areas for creating an OSM-LULC dictionary. Finally\, a nu mber of 1409 different OSM tags were found and they were reclassified into five and 14 different LULC classes\, respectively. Moreover\, this dictio nary was also analyzed with the four proposed measures. Results showed tha t: \nFirstly\, most OSM tags (＞ 1\,000) were only found in less than fiv e study areas (SC ＜ 5). Moreover\, only 37 of the 1409 OSM tags had a pe rcentage of average area (AAP) larger than 0.1%. This indicates that a sma ll proportion of OSM tags can play a decisive role. \nSecondly\, an OSM ta g may correspond to multiple different LULC classes within a pair of OSM a nd reference datasets\; The most appropriate LULC class for each OSM tag m ay also vary among different pairs of datasets. Thus Both the SR and AMP m ay also vary in different pairs of OSM tag and LULC class. \nWith the prop osed dictionary\, it is possible to understand the differences of differen t OSM tags and different pairs of OSM tag and LULC class. This is essentia l not only for producing LULC maps\, but also for picking up training and/ or validation data from an OSM dataset and also for detecting incorrect ta gs in an OSM dataset. Therefore\, we concluded that it has benefits for cr eating an OSM-LULC dictionary based on multiple pairs of OSM and reference datasets. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Creating a land use/land cover dictionary based on multiple pairs of OSM and reference datasets - ShuZhuWang\, YaoMing Liu URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/ZBGHFH/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-SUVEZL@talks.osgeo.org DTSTART;TZID=CET:20220825T124500 DTEND;TZID=CET:20220825T125000 DESCRIPTION:Thankfully to the European Commission initiatives such as INSPI RE (2007) and other governmental policies\, spatial data are available pub licly on different national\, regional and municipality geoportals for fur ther use. When it comes to the cultural heritage and Italian context\, bas ed on the decree of the Ministry of Culture (MiBACT\, 2008)\, different ac tivities concerning heritage has been assigned to the ICCD (i.e.\, Central Institute for Catalogue and Documentation) such as research and technical -scientific collection of the documentation and coordination of cataloguin g of cultural heritage and its digitalization. These regulations allowed t he public entities to share substantial information about geographical and spatial data with a wider audience. Specifically in the region of Lombard y\, data about cultural heritage are catalogued in SIRBeC (i.e.\, Regional information System for Cultural Heritage) that has been promoted since 19 92 and continues collecting\, managing\, and publishing a vast amount of i nformation. Vector shapefiles are freely available for download on the Geo portale Lombardia. The scope of the research was collecting information ab out cultural heritage in Lombardy that is freely accessible online. Data d ownloaded are point and polygon features files of the position of the cult ural heritage. Furtherly\, the methodology developed deals with the use of QGIS\, as the open and free software together with the Python console int egrated into the software and finally using the online software of the int egrated development environment (IDE) named Replit that is free\, open\, c ollaborative and in-browser Python coding application.\nThe methodology is based exclusively on free and open sources\, starting from the collection of data to their processing. Each vector file is enriched with the metada ta in the attribute table but the methodology is providing a combination o f software to obtain other data (e.g.\, coordination\, area\, etc.) and st atistical analysis (e.g.\, ratio\, percentage\, position\, distribution\, etc.)\, which are the initial part of each elaborated cultural heritage pr oject. Additionally\, the methodology is discussing different approaches t o reach the desired result and compares their differences. Firstly\, the P ython console in QGIS was examined\, and metadata were extracted from the vector file to the .csv file to be used in Replit. The online codding appl ication gave a higher degree of flexibility while coding\, and it was poss ible to implement data extracted in a .csv file into a coding panel\, usin g them to produce different statistical analyses. Furtherly\, the methodol ogy discusses the use of the plugin of QGIS called DataPlotly and data dif ferences\, from the representation to the utility level.\nResults through the Python Console in QGIS allowed the extraction of necessary data for fu rther analysis\, deleting the ones which are not needed. The good side of this approach is that metadata of the shapefile stay untacked\, and the Py thon is simply extracting selected data in a new external file. There have been selected four categories of interest: Name\, Category\, Typology and Municipality of the cultural heritage. The area of interest was a norther n part of Milan\, in the province of Monza e Brianza which has a dense and diverse category of cultural heritage. Using the python code\, these four categories are temporarily printed and saved in the console panel. Since there is no information about coordinates inside the metadata\, there are two approaches that are tested to obtain them. The first one used was the QGIS integrated option "Add geometry attributes"\, which created the new s hapefile enriched with the information about longitudinal and latitudinal coordinates. The second approach was extracting the coordinates through th e Python console with the f.geometry() function. Information about the fou r categories selected and coordinates are printed temporarily in the conso le\, and the user can control the order of the columns and delimited type\ , following the saving and extracting the .txt file. \nThe second part of the analysis also discusses two methods that were tested for the creation of statistical analysis of extracted data and their representation\, first ly in the QGIS plugin DataPlotly and then using Replit. Presenting statist ical analysis in the form of different charts is available directly throug h the plugin. Nevertheless\, when it comes to the great amount of data the plugin resulted not be very efficient for the representation nor easy to manage the view. Another constrain is that there is no option for exportin g graphs in a .pdf file. On the other side\, creating the charts through t he Python packages such as matplotlib or pandas shows a better degree of c ontrol over a graph. The advantage is that there is a possibility of expor ting it in many different files\, such as a .pdf or .svg file. Additionall y\, through the Python in-browser application\, there is a higher degree o f control and change of the visual representation of charts. \nIn conclusi on\, the process of extracting the coordinates from the previously georefe renced shapefiles can be useful when it comes to the georeferentiation of other collected material\, such as dense point clouds created by photogram metric techniques and other photographic material collected in-situ. In th e past years\, a lot of students\, researchers\, and professionals were no t able to continue their work because of the inaccessibility to the site a nd unavailability to perform the field survey which is necessary when it c omes to the investigation of cultural heritage. The process of using and c ombining open and free software\, including both those which are used off and online\, can provide to a certain degree some information that is not visible in attributes so that the study can be continued\, and research ca n be conducted also in a remote. The methodology\, processes and tools use d are simple\, yet they are creating clear guidelines of the potentiality and importance of freely shared data and stresses again the power of geogr aphic information tools in urban and architectural analyses. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:From QGIS to Python: comparison of free and open tools for statisti cal analysis of cultural heritage and data representation - Dina Jovanovic URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/SUVEZL/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-ZCEZF9@talks.osgeo.org DTSTART;TZID=CET:20220825T125000 DTEND;TZID=CET:20220825T125500 DESCRIPTION:Traffic accidents are a significant problem facing the world\, as they result in many deaths and injuries every year. Generally\, the pro bability of traffic accidents occurring at any point is not random. Factor s such as the condition of the road\, where the accidents occurred\, and t he general structure of the land play an essential role in the accidents t hat will occur at one point. For this reason\, traffic accidents tend to o ccur intensively in areas where these factors are different from usual.\n\ nIt is critical to identify such areas and take the necessary measures to ensure road safety and reduce traffic accidents. Identifying the different geographic locations where traffic accidents occur can help prevent more traffic accidents\, personal injuries\, and fatal accidents and understand the different accident occurrence conditions. When the literature is cons idered\, it is seen that many studies in this field are handled with diffe rent methods. Analyzing the locations where traffic accidents occur by con sidering the hot spots with spatial clustering methods plays a very active role in examining the tendency of traffic accidents to occur. In this stu dy\, it is thought to deal with detecting traffic accident hot spots by us ing the GIS-based Nearest Neighbor Hierarchical Clustering Method (NNH) an d Density-based clustering Method (DBSCAN).\n\nNearest Neighbor Hierarchic al Clustering Method (NNH) is a hot spot spatial clustering method that de tects accident hot spots. This method considers two types of criteria for spatial mapping clustering of spatial point data: the threshold distance ( d)\, which is the Euclidean distance between each pair of data points\, an d the minimum number of points that must be present in a cluster (nmin) (K undakci E\, 2014\; Kundakci and Tuydes-Yaman\, 2014\; Levine\, 1996\; Levi ne et al.\, 2004\; Ture Kibar and Tuydes-Yaman\, 2020). At the point of re alizing this method\, the crime stat program\, which was developed especia lly for hot spot clustering analysis of crimes\, is widely used. CrimeStat is a crime mapping software program developed by Ned Levine (Levine\, 199 6). \n\nDensity-based clustering\, on the other hand\, is also known as DB SCAN\, is a method for finding specific predefined events and hotspots. Th e algorithm\, moreover\, is open source and recommended for noisy data in large spatial databases (Ester et al.\, 1996). This method identifies a cl uster as the most densely connected set of points possible. There are two criteria addressed in this method: Epsilon and minimum scores. The maximal radius of the neighbourhood is epsilon\, and the minimal number of points in the epsilon-neighbourhood to describe a cluster is minimum points. Thi s clustering algorithm separates the point data into three different forms (Schubert et al.\, 2017).\n\nIn the study\, the Mersin province of Turkey was chosen as the pilot region for the analyses using the mentioned metho ds. Mersin is a port city located in the Mediterranean Region of Turkey\, located between 36-37° north latitude and 33-35° east longitude. As of 2 021\, it has a population of 1.891.145 (URL-1\, 2022). It is the most impo rtant domestic tourism center of Turkey and is on the way to becoming Turk ey's new tourism region with the appointments made in tourism in recent ye ars and new hotels built on the beach. \n\nThis study predicted determinin g the risky areas where speed-related traffic accidents will occur in Mers in\, which is an important point for the country\, and to make predictions by making evaluations depending on the road geometry at the determined po ints. In addition\, it will be examined whether the measures to be taken b ased on the analysis at the determined points are made comparatively with two different methods and whether these evaluations create differences by considering both based on a large region and the basis of a more local reg ion.\n\nThe study was planned in four phases. First of all\, spatial and n on-spatial data of the selected pilot region will be provided. For this st age\, traffic accidents data between 2013-2020 will be obtained from the g eneral directorate of safety and the general command of the gendarmerie. T he obtained data will be organized and then transferred to the geographic database for GIS-based analyses in the second stage. Since speed-related t raffic accident hot spot analysis will be performed in the study\, the dat abase will be suitable to include speed-related accidents. The NNH and the DBSCAN method will be performed in the third stage\, and the results will be discussed. At this stage\, the Crime Stat III program will be used for the NNH method\, and the open-source GIS program QGIS will be used for th e DBSCAN method. All results will be analyzed\, visualized\, and evaluated through the QGIS program. In the last stage of the study\, the results ob tained will be examined according to the probability of accidents. Finally \, the obtained risky areas according to the analysis results will be eval uated according to the geometry of the road. In short\, it will be examine d within the framework of accident-road geometry whether the structure of the road and the high-risk areas of the accidents overlap.\n\nThe fact tha t the points where speed-related accidents will tend to cluster will be de termined\, with the study to be carried out\, will address a significant g ap in this field. Since the effectiveness of the methods will be compared with a different analysis\, a study will be constituted a base for studies in a similar field. In addition\, since the reasons such as whether these methods produce effective results in large regions and more local regions will be examined\, it is thought that important suggestions will be made and contributions to the literature. Finally\, since the results obtained in the study will be evaluated depending on the road geometry\, the traffi c accident-road geometry relationship will be discussed. Thus\, a base for similar studies will be provided. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Speed-related traffic accident analysis using GIS-based DBSCAN and NNH clustering - Baran Topcuoglu URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/ZCEZF9/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-MCGBBT@talks.osgeo.org DTSTART;TZID=CET:20220825T141500 DTEND;TZID=CET:20220825T144500 DESCRIPTION:# Introduction\n\nOpenStreetMap (OSM) has evolved to one of the most used geographic databases. It is a major knowledge source for many g eographic topics addressed by researchers\, professionals and the general public. To satisfy these diverse needs and capabilities\, the linked commu nities surrounded the project with an ever growing ecosystem of analyses t ools (e.g. OSM Contributors\, 2022). The most prominent analysis topic is data quality (Senaratne et al. 2015) where e.g. intrinsic indicators are u sed to estimate completeness (Brückner et al. 2021). Furthermore the comm unity is also interested in insights such as leader-boards or activity rep orts (e.g. Neis\, 2022). In recent years analyses have also more and more shifted towards doing large scale analyses (e.g. Herfort et al. 2021).\n\n This diversity of tools can be a challenge for data users who will find th emselves in a universe of highly specialised or complex tools using differ ent programming languages\, platforms\, interfaces\, output formats etc. W hile there have been efforts to provide users with higher level data insig ht and analyses platforms\, these still mostly concentrate on or are limit ed to certain topics or regions. To our knowledge no tool exists to analys e and combine topic independent aspects of the data at the highest possibl e resolution: single OSM elements.\n\nThe presented software (available at https://gitlab.gistools.geog.uni-heidelberg.de/giscience/ideal-vgi/osm-el ement-vectorisation) sets out to bridge this gap by integrating multiple a spects of the OSM ecosystem into one workflow that allows the quantitative assessment of selected OSM elements or all elements in a defined region. This enables new insights in a formalised and easy to use manner. The resu lt is a vectorisation of single OSM elements (sometimes also called embedd ing or feature construction). By producing a machine readable result\, the tool can be used for manual data investigations as well as for the ever g rowing field of machine learning where it can be linked to a range of labe ls.\n\n# Software\n\nThe tool is centred around a python package providing a command line interface suitable also for novice users. It draws on othe r sources where necessary such as POST-requests and Java. Further data pro cessing is done using the R scripting language while all data is stored in a PostGIS enhanced PostgreSQL database and can be exported automatically to .csv-files. The AGPL v3 license as well as the code structure and docum entation enable others to also use it as a framework to implement their ow n analyses logic in combination with the current procedure. A default setu p using Docker is provided for fast installation including a minimal examp le. The tool is fully functional and in use in our current research. Yet\, it is under active development towards a web interface and functionality extensions. While the development was made with land-use and land-cover (L ULC) information in mind\, the tool can be seamlessly applied to any polyg onal OSM data such as buildings and also supports linear and point data. T he tool is resilient towards missing data and can recover from many common issues like failed connections. The backend remains in a sane state throu ghout the workflow and error messages enable the user to adapt to any fail ures and simply rerun the tool that will automatically pick up from the la st savepoint. Benchmarks have shown that the tool is capable of processing around 1k elements per hour making it a suitable tool for larger analyses of custom regions or element sets.Out of the endless number of possible d ata aspects\, a set of 32 are currently available for the user to choose. These cover aspects concerning the element itself (e.g. object area\, geom etric complexity and object age) but also the surrounding data (e.g. the m apping saturation and community activeness) and the editors (e.g. their ex perience\, localness or editing software used).\n\n# Application\n\nTo pro ve its potential\, the tool is applied to a set of 1k randomly selected OS M LULC elements. We picked OSM LULC as an example as it has been shown to be valuable for applications such as earth surface monitoring. The results provide a status report on the already available data to the OSM communit y. It further enables a more informed planning of future activities like o rganised mapping or data curation efforts and enables data consumers to ma ke informed decisions on data usage by answering the question: What is OSM LULC made of? First\, three exemplary hypotheses were tested statisticall y on a global as well as a continental scale to analyse the triangular rel ation between elements' size\, age and location in terms of population den sity. In a second step\, k-means clustering was used to identify clusters based on the properties of the OSM objects. Before clustering\, the data were standardised and stripped of any geographic information as we were hy pothesising that the different clusters might be linked to different geogr aphic regions.\n\n\nThe results showed that larger objects were more frequ ently encountered in regions with a lower population density due to the 'n atural' factor of higher fragmentation in these areas. Yet\, the effect wa s surprisingly small on a global scale. A general mapping order where area s of high population density are mapped before lower population density ar eas could not be confirmed globally. This may be caused by a complex inter action between several indicators and regional tendencies\, that remains t o be fully understood. Regional tendencies were shown e.g. for the age of objects with North America and Europe containing older objects than Africa and Asia. The five k-means clusters formed interesting groups worth furth er investigation. For example the North American lakes or the complex Euro pean elements were each detected as distinct clusters by the algorithm.\n\ n\n# Outlook\n\nOur current and future work will investigate the causes of these insights and link them e.g. to data quality to identify OSM element s that need the communities' attention. The presented tool already enables other data users to join us on this path. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:OpenStreetMap Element Vectorisation - A tool for high resolution da ta insights and its usability in the land-use and land-cover domain - Mori tz Schott URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/MCGBBT/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-RKDK7X@talks.osgeo.org DTSTART;TZID=CET:20220825T141500 DTEND;TZID=CET:20220825T144500 DESCRIPTION:The “Destination Earth” initiative of the European Union en compasses the creation of Digital Twin Earths (DTEs)\, high-precision digi tal models of the Earth integrating various aspects of the Earth’s syste m to monitor and simulate natural phenomena and related human activities\, being able to explore the past\, understand the present\, and build predi ctive models of the future. There are multiple elements that a Digital Twi n Earth needs\, such as strong computation capabilities\, connectivity\, c loud computing\, Artificial Intelligence (AI)\, models that are able to de scribe physical phenomena\, scientific collaboration\, high volumes of goo d quality data (big data)\, and interoperability. \nA full-scope Digital T win Earth is a huge task that may require years to be built\, and Destinat ion Earth uses an incremental approach\, where multiple smaller parts are put together to create a single\, complete model by having smaller Digital Twins with the so-called digital twin precursors. This work presents an i nitial approach to address the big data\, interoperability\, cloud computi ng\, and scientific collaboration elements of the DTE\, by developing a mo dular web platform for integrating georeferenced open-source data using th e mediator-wrapper architecture to retrieve and query data from online sou rces. The scope of the project is to create this platform for the Italian Coast\, with the goal of being able to understand the interaction between the land and the sea\, the human impact\, and other factors that may affec t the coasts employing data analysis.\nSince ancient times\, coasts have p layed a fundamental part in human civilization\, being a critical element for development\, economy\, transportation\, and tourism. In addition\, co asts host an important portion of global biodiversity and richness\, which is endangered by global warming and pollution. Thus creating a digital tw in of the coast is an important task\, in order to understand physical phe nomena happening on the land and on the sea\, as well as the interaction b etween those two elements\, and the role of human activity on it. Although this work is focused on the Italian Coast\, its modularity allows the pil ot to be extensible and reproducible for any coast in the world.\nAs the i dea is to address big data of good quality and interoperability\, by quali ty data we mean authoritative\, reliable\, and validated data\, and intero perability refers to data that can be easily used and integrated on any pl atform. Good quality data is found all over the internet\, but the biggest and most reliable homogeneous open data source for the European continent is Copernicus. Copernicus provides six services that focus on Land\, Ocea n\, Atmosphere\, Climate Change\, Security\, and Disaster Management. Two services are of great importance for studying the physical phenomena of co asts: the Copernicus Land Monitoring Service (CLMS: https://land.copernicu s.eu/)\, and the Copernicus Marine Environment Monitoring Service (CMEMS: https://marine.copernicus.eu/). The WorldPop population counts dataset (ht tps://www.worldpop.org/)\, which is also open data made available by The U niversity of Southampton\, is used for understanding human impact. The CME MS provides data on physical and biogeochemical variables for the sea whil e CLMS provides data on land cover and land use. Data ranges as far as 198 7 to the present\, its spatial resolution varies from 0.042° (approx. 3.5 km at the latitude of Italy) for biogeochemical variables to 10 meters for land cover and is offered as monthly\, daily\, and hourly averages. World pop population counts are available yearly from 2000 to 2020 and have a sp atial resolution of 3 arcseconds\, which correspond to approximately 70 me ters at the latitude of Italy. \nInteroperability is achieved by standards . All data that is georeferenced and that is available online should follo w certain guidelines and standards\, which are managed by the Open Geospat ial Consortium (OGC) and ISO (International Organization for Standardizati on). But mere standards do not completely solve the problem of interoperab ility because the way in which each data source presents its data is diffe rent\, meaning that to achieve full integration an additional step is nece ssary. In the developed platform\, this problem is addressed using a media tor-wrapper architecture\, where a mediator receives generic requests and calls the specific wrapper\, which is in charge of communicating with the specific data source and retrieving the data\, to pass it again to the med iator which translates it back to generate a generic response. In this way \, additional data sources can be integrated by building new wrappers. Dat a visualization is managed by the open-source web mapping library OpenLaye rs\, which can correctly display any type of georeferenced data that follo ws OGC standards.\nOther platforms exist that use online data sources to d isplay data and to build knowledge around it. E.g.\, CMEMS has its own pla tform (https://myocean.marine.copernicus.eu/data) for visualizing all its datasets and allows users to build plots and to extract subsets of the dat a at different times and elevations\; CLMS also allows users to see the da tasets and retrieve parts of them within their website (Corine Land Cover example: https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 )\; other more complex platforms consume multiple data sources and build A I models around them such as the ARIES (Artificial Intelligence for Enviro nment & Sustainability) platform (https://seea.un.org/content/aries-for-se ea) that is focused on ecosystem accounting. The main difference between t hose platforms and the digital twin of the Italian coast in development is the focus on a single type of location\, which makes models more specific and available data more accurate and localized. It is also possible to pe rform basic statistical analysis and to observe relations between layers\, being able to visualize results as plots\, tables\, and histograms\, as w ell as being able to download the produced data. Another novelty is the ad dition of demographic data to add the human factor to the analysis.\nAs th is is a work in progress (available online on https://dte-italycoast.herok uapp.com/)\, more features are planned\, such as capabilities to share pro jects and analysis\, adding more data sources\, AI models\, and more sophi sticated analysis than the current basic statistical analysis. DTSTAMP:20260914T082223Z LOCATION:Academic online SUMMARY:Building a digital twin of the Italian coasts - Juan Pablo Duque Or doñez URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/RKDK7X/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-VMNCM3@talks.osgeo.org DTSTART;TZID=CET:20220825T141500 DTEND;TZID=CET:20220825T144500 DESCRIPTION:Motivation:\n\nBecause of technological advancements\, public p articipation in scientific projects\, known as citizen science\, has grown significantly in recent years (Schade and Tsinaraki 2016\; Land-Zandstra et al. 2016). Contributors to citizen science projects are very diverse\, coming from a variety of expertise\, age groups\, cultures\, and so on\, a nd thus the data contributed by them should be validated before being used in any scientific analysis. Experts typically validate data in citizen sc ience\, but this is a time-consuming process. One disadvantage of this is that volunteers will not receive feedback on their contributions and may b ecome demotivated to continue contributing in the future. Therefore\, a me thod for (semi)-automating validation of citizen science data is critical. One way that researchers are now focusing on is the use of machine learni ng (ML) algorithms to validate citizen science data.\n\nMethodology:\n\nWe developed a citizen science project with the goal of collecting and autom atically validating biodiversity observations while also providing partici pants with real-time feedback. We implemented the application with the Dja ngo framework and a PostgreSQL/PostGIS database for data preservation. In general\, the focus of biodiversity citizen science applications is on aut omatically identifying or validating species images\, with less emphasis o n automatically validating the location of observations. Our application's focus\, aside from image and date validation (Lotfian et al. July 15-20\, 2019)\, is on automatically validating the location of biodiversity obser vations based on the environmental variables surrounding the observation p oint. In this project\, we generated species distribution models using var ious machine learning algorithms (Random Forest\, Balanced Random Forest\, Deep Neural Network\, and Naive Bayesian) and used the models to validate the location of a newly added observation. After comparing the performanc e of the various algorithms\, we chose the one with the best performance t o use in our real-time location validation application.\n\nWe developed an API that validates new observations using the trained models of the chose n algorithm. The Flask framework was used to create the API. The API uses the location and species name as parameters to predict the likelihood of o bserving a species (for the time being\, a bird species) in a given neighb orhood. Moreover\, the model prediction\, as well as information on specie s habitat characteristics are then communicated to participants in the for m of real-time feedback. The API has three endpoints: a POST request that takes the species name and location of observation and returns the model p rediction for the probability of observing the species in a 1km neighborho od around the location of observation\; a GET request that takes the locat ion of observations and returns the top five species likely to be observed in a 1km neighborhood around the location of observation\; and a GET requ est that returns the species common names in English.\n\n\nUser experiment :\n\nA user experiment was carried out to investigate the impact of automa tic feedback on simplifying the validation task and improving data quality \, as well as the impact of real-time feedback on sustaining participation . Furthermore\, a questionnaire was distributed to volunteers\, who were a sked about their feedback on the application interface as well as the impa ct of real-time feedback on their motivation to continue contributing to t he application.\n\nResults:\n\nThe results were divided into two parts: fi rst\, the performance of the machine learning algorithms and their compari son\, and second\, the results of testing the application through the user experiment.\n\nWe used the AUC metric to compare the performance of the m achine learning algorithms\, and the results showed that while DNN had a h igher median AUC (0.86) than the other three algorithms\, DNN performance was very poor for some species (below 0.6). Balanced Random Forest (AUC me dian 0.82) performed relatively better for all species in comparison to th e other three algorithms. Furthermore\, for some species where the other t hree algorithms performed poorly (AUC less than 70%)\, Balanced-RF outperf orms the others.\n\nThe user experiment results provided us with prelimina ry findings that support the combination of citizen science and machine le arning. According to the findings of the user experiment\, participants wi th a higher number of contributions found real-time feedback to be more us eful in learning about biodiversity and stated that it increased their mot ivation to contribute to the project. Besides that\, as a result of automa tic data validation\, only 10% of observations were flagged for expert ver ification\, resulting in a faster validation process and improved data qua lity by combining human and machine power. \n\n\nWhy it should be consider ed:\n\nData validation and long-term participation have always been two of the most difficult challenges in citizen science and VGI (volunteer geogr aphic information) projects. Various studies have been conducted on biodiv ersity data validation\, focusing primarily on observation images with aut omatic species identification\; however\, not enough attention has been pa id to observation location validation\, particularly automatic location va lidation taking into account species habitat characteristics. Furthermore\ , to the best of our knowledge\, the combination of machine learning and c itizen science for sustaining participation by providing real-time user-ce ntered and machine generated feedback to participants has received\, till now\, little attention and therefore our work is new\, original and comple tely coherent with the vision of community citizen science\, where scienti sts and citizen scientists are supposed to learn from each other. \n\nBibl iography:\n\n\nLand-Zandstra\, Anne M.\, Jeroen L. A. Devilee\, Frans Snik \, Franka Buurmeijer\, and Jos M. van den Broek. 2016. “Citizen Science on a Smartphone: Participants’ Motivations and Learning.” Public Under standing of Science 25 (1): 45–60.\n\nLotfian\, Maryam\, Jens Ingensand \, Olivier Ertz\, Simon Oulevay\, and Thibaud Chassin. July 15-20\, 2019. “Auto-Filtering Validation in Citizen Science Biodiversity Monitoring: A Case Study.” In Proceedings of the 29th ICA Conference. Vol. 2. https:/ /doi.org/10.5194/ica-proc-2-78-2019.\n\nSchade S\, Tsinaraki C.\; Survey r eport: data management in Citizen Science projects\; EUR 27920 EN\; Luxemb ourg (Luxembourg): Publications Office of the European Union\; 2016\; doi: 10.2788/539115 DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:An approach for real-time validation of the location of biodiversit y observations contributed in a citizen science project - Maryam Lotfian URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/VMNCM3/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-EGMDJQ@talks.osgeo.org DTSTART;TZID=CET:20220825T144500 DTEND;TZID=CET:20220825T151500 DESCRIPTION:Data-driven innovation\, as outlined by Granell et al. (2022)\, has seen recent advances in technology driven by the continuous influx of data\, miniaturization and massive deployment of sensing technology\, dat a-driven algorithms\, and the Internet of Things (IoT). Data-driven innova tion is considered key in several policy efforts\, including the recently published European strategy for data\, where the European Commission ackno wledged Europe’s huge potential in the data economy by leveraging on ava ilable data produced by all actors (including public sector\, private sect or\, academia and citizens). Technologies currently used for the managemen t\, exchange and transmission of data\, including geospatial data\, must b e evaluated in terms of their suitability to efficiently adapt to streams of larger data and datasets. As more users access data services through mo bile devices and service providers are faced with the challenges of making larger volumes of data available\, we must consider how to optimise the e xchange of data between these clients and servers (services). For many yea rs JSON\, GeoJSON\, CSV and XML have been considered as the 'de facto' sta ndard for data serialisation formats. These formats\, which enjoy near ubi quitous software tool support\, are commonly used for the storage and shar ing of large amounts of data in an interoperable way. Most Application Pro gramming Interfaces (APIs) available today facilitate data sharing and exc hange\, for a myriad of different types of applications and services\, usi ng these exchange formats (Vaccari et al.\, 2020). However\, there are man y limitations to approaches based on JSON and XML when the volume of data is likely to be large. Potentially the most serious of these limitations i s related to reduced computational performance\, when exchanging or managi ng large volumes of data where there are high computational costs associat ed with (de)serializing and processing these data. \n\nAgainst this backgr ound\, binary data serialization approaches allowing for the interoperable exchange of large volumes of data have been used extensively within scien tific communities such as meteorology and astronomy for decades. In recent years\, popular distributors of geospatial data have also begun making us e of binary data formats. Examples are OpenStreetMap (OSM) data (e.g. the OSM Planet and OSM Full History Planet files\, providing access to the who le OSM database and its history) as well as the popular ESRI Shapefile for mat's main file (.shp)\, which also contains geometry data and is stored a s a binary data file. \n\nIn this paper we describe the methodology\, impl ementation and analysis of a set of experiments to analyse the use of bina ry data serialization as an alternative to data exchange in XML or JSON da ta formats for several commonly encountered GIS workflows. Binary data ser ialization allows for the storage and exchange of large amounts of data in an interoperable fashion (Vanura and Kriz\, 2018). While anecdotal eviden ce indicates binary serialization approaches are more efficient in terms o f computation costs\, processing times\, etc.\, there are additional overh eads to consider with these approaches including special software tools\, additional configuration\, schema definitions\, etc. (Viotti and Kinderkhe dia\, 2022). Additionally\, there have been few\, if any\, investigations of binary data serialization approaches specifically for geographical data . Our set of experiments investigates the advantages and disadvantages of binary data serialization for three common GIS workflow scenarios: (1) geo location point data from an OGC SensorThings API\; (2) geolocation point d ata from a very large static GeoPackage dataset representing the conflatio n of address data from the National Land Survey of Finland and OpenStreetM ap\; and (3) geographic polygon datasets containing land cover polygons (c urrently ongoing work). We consider comparisons of JSON and GeoJSON with t wo very popular binary data formats (Proos and Carlsson\, 2020)\, namely G oogle Protocol Buffers and Apache Avro. Protocol Buffers (Protobuf) is an open source project developed by Google providing a platform neutral mecha nism for serializing structured data. Apache Avro\, another very popular s chema-based binary data serialization technique\, is also a language-neutr al approach which was originally developed for serializing data within Apa che Hadoop. Both Protobuf and Avro have wide support in many popular langu ages such as C++\, C#\, Java and Python. The full paper will provide detai led descriptions of the implementations of our experiments. However\, here we provide a summary of some of the key results and highlights of our ana lysis. \nAs binary data formats such as Protobuf and Avro are not self-des cribing schemata and schema definitions are required for each dataset or d ata stream\, these definitions are required for the serialization and dese rialization of the binary data files. Any changes in the underlying data m odels of the dataset or data stream will require a change in the schema de finitions. \nFor all of our experiments the serialized binary data files w ere at least 20% smaller on average than the original non-binary data file s. Processing times for binary serialization of data from API sources were approximately 3.7 times faster on average than serialization to JSON or G eoJSON formats. Processing times for binary serialization of the datasets were\, on average\, at least 10% faster than serialization to JSON or GeoJ SON formats. \nIt is difficult to point to a clearly defined set of result s which indicate that binary data formats are an overwhelmingly better cho ice for data exchange than XML\, JSON or GeoJSON. While binary data format s enjoy very good expert developer level support in major programming lang uage implementations\, this is dwarfed by the near universal levels of sup port for XML\, JSON and GeoJSON in almost all major programming languages. \n\nThere are a number of potential avenues for future\, including automa ted semantic interoperability for binary data serialization using linked g eodata\, opportunities for more integrated software tool support for binar y data processing and further computational experimentation on different t ypes of datasets and services which could benefit from binary data seriali zation. \n\nThe software implementation is carried out using Python 3 on U buntu Linux. All software code is made publicly available via the GitHub r epository https://github.com/petermooney/jrc_binarydata. Detailed instruct ions on how to reproduce and replicate all of the experimental analysis ar e provided within the repository. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Geospatial data exchange using binary data serialization approaches - Peter Mooney URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/EGMDJQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-9RPVCX@talks.osgeo.org DTSTART;TZID=CET:20220825T144500 DTEND;TZID=CET:20220825T151500 DESCRIPTION:Heritage graphic representation combining building spatial loca tion and urban/land planning provides a powerful tool for government agenc ies. These techniques support the decision-making\, simplify the developme nt of protection and conservation inventories and allow the treatment of b uildings from an integral urban/land scale view. From a technical perspect ive the representation of information at various detail levels\, and invol ving different types of data and supports\, provides a complete vision wit h multiple applications. Furthermore\, this graphical representation of hi storical buildings offers an informative contribution that can be used to promote the architectural heritage with educational and touristic purposes .\n\nThe so-called Mudejar architecture is unique of the Iberian Peninsula and represents the influence of the Muslim culture in art and architectur e between the 12th and 17th centuries within the territories conquered by Christians. The Autonomous Community of Aragón was one of the most influe nced territories and hence the Aragonese Mudejar gained its own peculiarit ies that differentiate it from the rest of the territory. Some representat ive Aragonese Mudejar buildings were declared as World Heritage Sites by U NESCO in 2001. In the field of architecture\, the typologies of fortress-c hurches and single-nave churches with a polygonal apse and simple ribbed v aults are representative from the Aragonese Mudejar. The bell towers have a characteristic structure\, with a morphology similar to that used in the minarets of Muslim mosques. The use of traditional materials such as bri ck\, plaster\, stucco\, ceramics and wood in the construction processes an d the use of geometric shapes and plant themes for ornamentation also stan d out\, derived directly from the muslin tradition.\n\nThis work presents the development of a digital system to document and inventory the Mudejar architectural style in Aragón\, involving a list of 225 buildings with un ique architectural elements that are part of the World Heritage. The deve lopment of useful graphic representations of the architectural heritage re quires to exceed the classical inventory description level and to design g raphical environments able to contain further information about the cultur al assets. It is necessary to clarify a methodology to collect\, organize and disclose information to common users and urban managers following a st andardized procedure. First\, the information collected from the historica l Mudejar buildings was structured following standardized criteria and sto red in digital sheets\, creating a complete inventory of the Aragonese Arc hitectural Heritage. This structured digital inventory of the Mudejar Heri tage ensures that the information lasts over time\, as well as helps desig n conservation measures and promotions actions (Quintilla\, 2021).\n\nA ge ospatial web tool has been developed to organize and make available 2D and 3D architectural data of the buildings which enriches the descriptive inf ormation provided by the digital inventory sheets. The main goal is to pro vide a standardized basis for recording digital 2D/3D graphic documentatio n\, supporting the use of this information in an understandable and cohere nt way in future conservation actions. The proposed geospatial web tool al lows the dissemination and exploitation of the architectural information b y different users through a website that integrates a cartographic viewer (WebGIS) and also offers access to a point cloud manager based on WebGL. T he geospatial structured data are accessible through an interface with dif ferent visualization styles that are adaptable depending on the purpose\, such as technical studies\, reconstruction actions\, informative campaigns \, etc.\, opening up the possibilities of use of the available information . Furthermore\, the 3D point cloud viewer supports the creation of a user- friendly repository of geometric information of the registered heritage as sets.\n\nThe Digital Inventory of the Aragonese Mudejar Architectural Heri tage has been developed using exclusively free and open source software. T he creation\, edition\, managing\, visualization and publication of the ge ospatial information is achieved using QGIS (QGIS Development Team\, 2022) \, a multiplatform Geographic Information System which allows the manipula tion of raster and vectorial data sets. PostgreSQL (PostgreSQL Global Deve lopment Group\, 2022)\, allows the creation of attribute tables with geome tric and spatial information and the analysis of the information by spatia l SQL queries. The Spatial Data Infrastructure (SDI) services offered by d ifferent organizations have been used to support the geospatial architectu ral information using standardized web services. The data are published th rough OGC standardized formats\, such as WMS\, WMTS\, WFS or WCS. The Java Script library Leaflet (Leaflet\, 2022) is used to create a web map applic ation which make available the Mudejar Inventory the end-users.\n\nThe 3D information collected for each historical building is made available to en d-users by means of an ad-hoc interactive point cloud environment based on the Potree viewer project (Potree\, 2022). The three-dimensional geometri c information is obtained by the combination of photogrammetry and laser s canner techniques. The result is a high-density point cloud model of the b uilding that is used as a 3D support where the data provided by the differ ent technicians involved in the documentation process can be incorporated. Traditionally\, presenting models to the end user required transferring l arge amounts of data and installing third-party applications to view it. H owever\, this point cloud viewer is based on the WebGL technology which en ables the delivery of 3D content through web browsers without and installi ng third-party applications and which is natively supported by all devices . Previously\, the software Cloud Compare (Cloud Compare\, 2022) has been used to perform cloud segmentation and sub-sampling\, as well as for the c lassification of different cloud groups into architectural elements.\n\nTh e Digital Inventory of the Aragonese Mudejar Architectural Heritage is a d igital repository with graphic material composed of photographic and 2D/3D volumetric information\, which forms a complete documentation of the geom etry of the building and achieves the correct characterization for metric or informative purposes. DTSTAMP:20260914T082223Z LOCATION:Academic online SUMMARY:Digital 3D inventory for the promotion and conservation of the arch itectural heritage - Sergio Martínez-Aranda URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/9RPVCX/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-VSSKSB@talks.osgeo.org DTSTART;TZID=CET:20220825T144500 DTEND;TZID=CET:20220825T151500 DESCRIPTION:Earth observation (EO) imagery has become an essential source o f information to better monitor and understand the impact of major social and environmental issues. In recent years we have seen significant improve ments in availability and accessibility of these data. Programs like Lands at and Copernicus release new images every day\, freely and openly availab le to everyone. Technological improvements such as data cubes (e.g. OpenDa taCube)\, scalable cloud-based analysis platforms (e.g. Google Earth Engin e) and standardized data access APIs (e.g. OpenEO) are easing the retrieva l of the data and enabling higher processing speeds. \n\nAll these devel opments have lowered the barriers for utilizing the value of EO imagery\, yet translating EO imagery directly into information using automated and r epeatable methods remains a main challenge. Imagery lacks inherent semanti c meaning\, thus requires interpretation. For example\, consider someone w ho uses EO imagery to monitor vegetation loss. A multi-spectral satellite image of a location may consist of an array of digital numbers representin g the intensity of reflected radiation at different wavelengths. The user\ , however\, is not interested in digital numbers\, they are interested in a semantic categorical value stating if vegetation was observed. Inferring this semantic variable from the reflectance values is an inherently ill-p osed problem\, since it requires bridging a gap between the two-dimensiona l image domain and the four-dimensional spatio-temporal real-world domain. Advanced technical expertise in the field of EO analytics is needed for t his task\, making it a remaining barrier on the way to a broad utilization of EO imagery across a wide range of application domains. \n\nWe propos e a semantic querying framework for extracting information from EO imagery as a tool to help bridge the gap between imagery and semantic concepts. T he novelty of this framework is that it makes a clear separation between t he image domain and the real-world domain. \n\n There are three main compo nents in the framework. The first component forms the real-world domain. T his is where EO data users interact with the system. They can express thei r queries in the real-world domain\, meaning that they directly reference semantic concepts that exist in the real world (e.g. forest\, fire). For s implicity reasons\, we currently work on a higher level of abstraction\, a nd focus on concepts that correspond to land-cover classes (e.g. vegetatio n). For example\, a user can query how often vegetation was observed at a certain location during a certain timespan. These queries do not contain a ny information on how the semantic concepts are represented by the underly ing data. \n\nThe second component forms the image domain. This is where t he EO imagery is stored in a data cube\, a multi-dimensional array organiz ing the data in a way that simplifies storage\, access and analysis. Besid es the imagery itself\, the data cube may be enriched with automatically g enerated layers that already offer a first degree of interpretation for ea ch pixel (i.e. a semantically-enabled data cube [1])\, as well as with add itional data sources that can be utilized to better represent certain prop erties of real-world semantic concepts (e.g. digital elevation models). \ n\nThe third component serves as the mapping between the real-word domain and the image domain. This is where EO data experts bring their expertise into the system\, by formalizing relationships between the observed data v alues and the presence of a real-world semantic concept. In our current wo rk these relationships are always binary\, meaning that the concept is mar ked either as present or not present. However\, the structure allows also for non-binary relationships\, e.g. probabilities that a concept is presen t given the observed data values. \n\nWe implemented a proof-of-concept of our proposed framework as an open-source Python library (see https://gi thub.com/ZGIS/semantique). The library contains functions and classes that allow users to formulate their queries and call a query processor to exec ute them with respect to a specific mapping. Queries are formulated by cha ining together semantic concept references and analytical processes. The q uery processor will translate each referenced semantic concept into a mult i-dimensional array covering the spatio-temporal extent of the query. It d oes so by retrieving the relevant data values from the data storage\, and subsequently applying the rules that are specified in the mapping. If the relationships are binary\, the resulting array will be boolean\, with “t rue” values for those pixels that are identified as being an observation of the referenced concept\, and “false” values for all other pixels. Analytical processes can then be applied to this array. Each process is a well-defined array operation performing a single task. For example\, apply ing a function to each pixel or reducing a particular dimension. The workf low of chaining together different building blocks can easily be supported by a visual programming interface\, and thus lowering the technical barri er for information extraction even more. This is demonstrated already in a n operational setting by Sen2Cube.at\, a nation-wide semantic data cube in frastructure for Austria\, which uses our proposed semantic querying frame work [2]. \n\nWe believe our proposed framework is an important contributi on to more widely accessible EO imagery. It lowers the barrier to extract valuable information from EO imagery for users that lack the advanced tech nical knowledge of EO data\, but can benefit from the applications of it i n their specific domain. They can now formulate queries by directly refere ncing real-world semantic concepts\, without having to formalize how they are represented by the EO data. To execute the queries\, they can use pre- defined mappings\, which are application-independent and shareable. The f ramework eases interoperability of EO data analysis workflows also for exp ert users. Mappings can easily be shared and updated\, and the queries the mselves are robust against changes in the image domain. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Semantic querying in earth observation data cubes - Lucas van der M eer URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/VSSKSB/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-MLJHFC@talks.osgeo.org DTSTART;TZID=CET:20220825T151500 DTEND;TZID=CET:20220825T154500 DESCRIPTION:Spectral Indices derived from Remote Sensing data are widely ut ilized for understanding Earth System dynamics. Accelerated climate and la nd change makes the monitoring of Earth surface processes a top priority. Vegetation Indices (VIs) are a set of indices developed to better understa nd biosphere processes\, including potential photosynthetic activity\, the reby providing key information for the terrestrial carbon cycle. Urban Ind ices (UIs) are designed to study anthropogenic footprint on the environmen t identifying artificial surfaces and their extent. Other types of indices such as Water Indices (WIs) and Burn/Fire Indices are also being constant ly developed for understanding hydrological processes and the impact of ex treme events on the biosphere\, respectively. The increasing amount of spe ctral indices led to the creation of spectral indices catalogs\, such as A wesome Spectral Indices\, an open-source catalog with more than 100 spectr al indices. Each spectral index in this catalog has numerous attributes\, including the index name\, formula\, reference\, and other additional meta data. This catalog is part of an ecosystem that allows users to query and compute spectral indices in a Python environment for different data types\ , including Google Earth Engine (GEE) classes. GEE is a cloud-based geospa tial processing service for vector and raster data (Gorelick et al.\, 2021 ). It includes a multi-petabyte catalog of remote sensing data that users may explore online. The GEE Application Programming Interface (API) is exp osed and accessible through JavaScript and Python client libraries. While the JavaScript API is more advantageous for rapid prototyping through the Code Editor\, the GEE web-based Integrated Development Environment (IDE)\, it lacks Python's ability to integrate third-party packages. Multiple too ls for computing spectral indices\, including mathematical raster operatio ns\, normalized difference methods\, and expressions evaluation methods in the case of more sophisticated indices have been developed in the source API. In the meanwhile\, third-party Python packages have implemented metho ds for automatically computing spectral indices without hard-coding them i n the GEE Python API (Montero\, 2021). However\, users still have to hard- code the desired spectral indices for the JavaScript API since there are n o implementations that link spectral indices catalogs to the Code Editor f or their easy calculation. Here we present spectral\, a JavaScript module that links the Awesome Spectral Indices Catalog to GEE for querying and co mputing spectral indices inside the GEE Code Editor. The spectral module u ses the Awesome Spectral Indices Catalog and implements several functions that leverage the access and computation of spectral indices for multiple remote sensing products in the GEE catalog. As a primary component of the module\, all spectral indices\, as well as their attributes from Awesome S pectral Indices\, are available for querying by using a key-value model re presented by a JSON object. As a secondary component\, users can compute o ne or more spectral indices by using a single method and passing the requi red bands for the spectral indices computation as a dictionary of paramete rs. This method uses the expression of the index\, contained as an attribu te in the spectral indices catalog\, for evaluating it as an image object in GEE. Furthermore\, the module adds an additional method to compute kern el images for novel generalized kernel indices such as the kNDVI (Camps-Va lls et al.\, 2021). Kernels such as the Polynomial Kernel as well as the R adial Basis Function (RBF) Kernel are included. As an additional feature f or preprocessing raster datasets\, the module also implements two methods for automatically scaling and offsetting all raster datasets included in t he GEE catalog\, thus\, leveraging this mandatory manual step for several spectral indices that are not normalized. The spectral module demonstrates that spectral indices can be easily computed inside the GEE Code Editor b y automatizing most of the hard-code scripting. Image objects\, as well as image collection objects\, can be used for the calculation of all spectra l indices in the catalog if the specific raster dataset counts with the re quired bands. This principle applies to most raster datasets in GEE\, incl uding Landsat Series and most products of the Sentinel Series\, as well as MODIS and VIIRS reflectance products. Furthermore\, generalized kernel in dices can be used in their pure form\, allowing their spatio-temporal opti mization by enabling the scale-length parameter as a free user choice in t he RBF Kernel or the polynomial degree in the Polynomial Kernel. We antici pate that spectral will be used by most GEE users for Earth System researc h as well as for educational purposes in Remote Sensing. Analyses conducte d by scientists\, researchers and students will be sped up by avoiding har d-coding and Remote Sensing investigations using GEE will be boosted. Furt hermore\, novel indices will increase their visibility for different proje cts where they can be useful without taking out the visibility of standard and classical spectral indices. The module is open-source and it is hoste d on GitHub (github.com/davemlz/spectral) with an MIT License\, allowing c ontributions from the community. Prior experience in Remote Sensing can ma ke it easier to get started using the module\, however\, it is not require d and the module counts with several tutorials and a complete documentatio n oriented to beginners. DTSTAMP:20260914T082223Z LOCATION:Academic online SUMMARY:spectral: Awesome Spectral Indices for the Google Earth Engine Java Script API - David Montero Loaiza URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/MLJHFC/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-NTQNJ7@talks.osgeo.org DTSTART;TZID=CET:20220825T151500 DTEND;TZID=CET:20220825T154500 DESCRIPTION:The Covid-19 outbreak has greatly impacted society behaviours f ostering proximity tourism and valorising the social role of peri-urban na tural protected areas as key locations for outdoor activities [1]. This sh ift in habits calls for an adaptation in the next years of the offerings a nd management of these areas to respond to users' expectations of positive experience opportunities in near-by locations [2]. In the context of digi tal transformation and peri-urban protected areas\, this research investig ates the contribution that open geospatial technologies can provide in the creation of new economic\, social and cultural values to propose solution s and identify gaps or open issues.\nThe adopted methodology is the “cas e study approach”\, in which real cases are used to design\, develop\, i mplement\, collect and analyse data to extrapolate information that contri butes to a deeper knowledge of the matter. This research is framed in the context of the Interreg INSUBRI.PARKS (www.insubriparksturismo.eu) and amo ng the project’s parks the selected case studies for technological testi ng are the Parco Gole della Breggia and the Collina del Penz. While being two natural protected areas closely located in the southern part of Switze rland\, in the Canton Ticino\, they greatly differ for in-place management structure\, available offers and users’ type and therefore represents d ifferent needs. From the discussion with local tourism organisations and p ark administrators we have identified three specific aspects that are of p articular concern: (a) the creation of 3D digital products\, (b) the monit oring of touristic fluxes and (c) the conduction of parks management activ ities. This work presents the intermediate results of the development and testing of different selected solutions which describes the approach\, the issues and the potential of explored solutions with respect to the open s ource software.\n\n3D digital products - In addition to a more traditional use for conservation scopes and activity planning [3]\, 3D models can be used to offer positive experiences thanks to an enhanced understanding of specific intangible aspects [4]. For example\, in the case study of the Pa rco Gole della Breggia\, it might be difficult for a tourist to fully real ise the extent of the anthropic impacts on nature. The area is geologicall y relevant for the visible calcareous formation hundreds of millions of ye ars old. From 1961 to 2003 the Breggia shores hosted a large cement plant that strongly modified the territory. Today\, only a small part of the pla nt is still in place as a testimonial of the anthropic impacts and element of industrial archeology. To support the perception of the real antropic impact we decide to implement three digital models representing the territ ory at three key epochs: before the cement plant construction\, at the max imum expansion of the plant and at the present state. The present state mo del can be created by means of laser scanning and photogrammetric surveys while the other two can be realised by digitising historical maps\, techn ical plans and historical pictures. The investigation identified a workflo w based on the evaluation of CloudCompare\, Riegl RiSCAN Pro and Cyclone 3 DR for 3D survey\, Regard 3D\; GRASS\, QGIS and ESRI ArcGIS Pro for spatia l data collection and management\; Blender\, AutoCAD 3D\, Rhinoceros and S ketchup for vector modelling of spatial elements\; Nubigon and Potree for a better graphical representation and further web dissemination of the res ults.\n\nMonitoring of tourtistic fluxes - The monitoring of touristic flu x is important for the correct management of the natural protected areas t o assure the Tourism Carrying Capacity (TCC) of trails is not exceeded\, t o assure adequate economic resources are allocated to maintain the assets\ , to understand the tourist behaviours and consequently develop strategies and plans to maximise the touristic value of the park [5]. While differen t solutions were proposed to this scope (accelerometers on iron plates and proximity radar sensors) it is important to capture specific tourist char acteristics\, like for example the presence of animals\, the direction and the use of bicycles or cars. To this aim\, Machine Learning models can he lp to automate the collection of such information by image analyses and ob ject detection [6]. The present paper presents a fully open prototype to i mplement and deploy a real-time tourist monitoring system composed of: sen sing device\, data communication\, data management and data visualisation platform. The system includes the usage of the YOLO open source solutions for image recognition\, the OGC SoS open standard and the istSOS implement ation for data management and sharing and the open source Grafana software for data visualisation and analysis. The results from the testing of the prototype in two locations for a period of 6 months is presented supplemen ted with field validation data.\n\nDigital Management of Protected areas - Protected areas are currently managed using different tools that are very often scarcely digitised. This approach does not exploit the potentiality of digitalization and does not foster the capacity to extract insights fr om data. While different open source project management software exists\, none is specifically designed to address natural area management processes . For this reason a novel application\, based on an open source platform h as been developed and implemented. The cloud solution named Park Asset Man agement (PAM) is based on the usage of PostgreSQL/PostGIS and OpenLayers i n conjunction with KeyCloak authorization platform\, the Hasura GraphQL En gine integrated in the Vue.js framework. The containerized application off ers the following features: park asset management and information sharing\ , working task management and execution\, rentals management\, notificatio n management\, offering a map interface and a more classic calendar and ta ble views. This platform enables insights extraction like maintenance cost of itineraries\, income from location rental by months and by years\, cos t and time required to replace items and the frequency of occurrence of ev ents. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Tourism\, natural protected areas and Open Source Geospatial techno logies - Massimiliano Cannata\, Massimiliano Cannata URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/NTQNJ7/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-MNJ3TT@talks.osgeo.org DTSTART;TZID=CET:20220825T151500 DTEND;TZID=CET:20220825T152000 DESCRIPTION:opic modelling is a branch of Natural Language Processing that deals with the discovery of conversation topics in a document corpus. In s ocial media\, it translates into aggregating posts into topics of conversa tion and observing how these topics evolve over time (hence the “dynamic ” adjective [Murakami\, 2021]). Conveying the results of topic modelling to an analyst is challenging since the topics often do not lend themselve s naturally to meaningful labelling\, where relationships between them can involve hundreds of dimensions. Furthermore\, the popularity of topics i s itself subject to change over time.\n\nIn this paper\, we propose a spat ialization technique based on open-source software that reduces the intrin sic complexity of dynamic topic modelling output to familiar topographic o bjects\, namely: ridges\, valleys\, and peaks. This offers new possibiliti es for understanding complex relationships that change over time\, that ov ercomes issues with traditional topic modelling visualisation approaches s uch as network graphs [Karpovich\, 2017].\n\nSpatialization [Fabrikant\, 2 017]\, a technique that uses spatial metaphors to aid cognitive tasks\, ha s been a research field since the early ‘90s. It can be used to make sen se of vast amounts of information by reducing them to a physical landscape . In this work\, we consider spatialization of topics in a 3D space where the X-axis is the similarity of topics posted on the same day\, the Y-axis is the similarity of topics across time and how their relationships evolv e\, and the Z-axis is a measure of the topic popularity. With this approac h\, a topic is therefore reduced to a single point in a 3D space\, and the interpolated surface constructed out of these points becomes a landscape with peaks\, ridges\, and valleys. More precisely\, the “valleys” repr esent less popular topics\, while “peaks” are the more popular ones an d flat surfaces indicate the average topics.\n\nOur team is working on the Australian Data Observatory project\, which has been collecting tweets an d other social media posts (Instagram\, Reddit\, YouTube\, Flickr\, etc)) related to Australia for the last 12 months. Through the use of the new Tw itter academic license\, the project is harvesting 10s of millions of twee ts per month. The social media posts are stored and analyzed daily using t he deep learning BERTopic package. The BERTopic output is then stored and served through a ReST API\, which is used by different clients (at present these are Jupyter notebooks and a web application). The intended audience of our platform is composed of the average topics domain researchers incl uding social scientists\, linguists\, and data journalists. The goal is to support big data exploration at scale and overcome the smaller scale cott age industry of social media research that has hitherto been the norm in a cademia in Australia\n\nTopic modelling is often presented using 2D visual izations\, such as circles with size proportional to topic popularity and position related to the similarity between topics\, The dynamic (temporal) aspect of topic evolution is typically shown with animations that show ho w topics morph into different ones and wax and wane in popularity or it is ignored completely and researchers just use static topic modelling visual isations. here is merit in trying a different approach for dynamic topic v isualisation: namely\, to map the social media landscape to the physical o ne\, as this metaphor allows the simultaneous appreciation of time\, topic similarity\, and popularity while allowing -via zoom operations- the aggr egation/disaggregation of topics into bigger/smaller cluster of posts. Thi s 3D landscape naturally aids the end-user in understanding complex highly dimensional data at a scale and volume that would otherwise be impossible . The formation of islands\, archipelagos\, mountain ranges or valleys rel ated to mainstream topics such as Covid\, vaccination\, lockdown\, through to geopolitical events such as the invasion of Ukraine provides a finger on the pulse of what is being discussed at scale by the broader population across the social media landscape.\n\nThis approach is currently realised using a web application that enables the “topographic” exploration of the topic landscape with functions to improve the user experience in the areas of topic labelling and inter-topic distance.\n\nThere are a few crit icalities in the proposed visualization: \ndistance between topics has to be drastically reduced in dimensionality from the ones provided by the Dee p Learning model to just one (the X-axis)\;\nthe Y-axis (time) has to be p ut in relation to a completely different measure (distance between topics) to make it amenable to an interpolation\;\ntopic popularity (the Z-axis) has a huge variability leading to irregular surfaces\, hence the need for a non-linear scaling of the Z-axis\;\ncommunicating the meaning of each to pic to the user is difficult\, as the top terms of each topic may not be m eaningful to a human\, and make for a poor label.\nThe proposed processing and visualization is developed using only open-source tools and framework s\, leveraging the work of the open-source geospatial community.\n\nAll th e software developed in the course of the Australian Data Observatory proj ect is available under the Apache 2.0 license\, and available through the University of Melbourne GitLab source code repository. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Mapping the Chatter: Spatial Metaphors for Dynamic Topic Modelling of Social Media - Luca Morandini URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/MNJ3TT/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-CHBS39@talks.osgeo.org DTSTART;TZID=CET:20220825T152000 DTEND;TZID=CET:20220825T152500 DESCRIPTION:Spatial data infrastructures prioritize data interoperability t o serve their diverse communities. Geospatial knowledge graphs (GKG) are a form of database representation and handling that aim to meet the challen ges of data interoperability\, reasoning for information storage and knowl edge creation\, and user access that provide coherent spatial context to a domain of information. This paper discusses the development of a prototy pe GKG based on national topographic databases. Geospatial data are used t o test interoperability aspects of ontology creation\, faceted search and retrieval using GeoSPARQL (Open Geospatial Consortium\, 2022)\, and user i nterface for data visualization and evaluation. The challenges are to capt ure and represent geographic semantics inherent in the source data\, to in tegrate data from outside sources through SPARQL Protocol and RDF Query La nguage (SPARQL) queries and to visualize the data using a cartographic use r interface. \n\nPoore (2003) identified four levels of data interoperabil ity: articulation\, sharing\, integration\, and alignment. These concepts are carried into the semantic technology design and application. Called th e Map as Knowledge Base (MapKB)\, the approaches use software components t o build a system architecture aligned with available standardized vocabula ries and is composed entirely of free and open-source software for geospat ial data The application was created in the context of The National Map of the U.S. Geological Survey (USGS). For purposes of data interoperability\ , the GKG ontology\, queries\, and visualization were studied for the syst em. \nData pre-processing involved creating a GKG ontology. The ontology w as semi-automatically transformed from source databases through the applic ation of rules on schema attribute\, domain\, and metadata files to create classes\, properties\, and other triple resources of Resource Description Framework (RDF) and Web Ontology Language (OWL) (Hayes and Patel-Schneide r\, 2014\; Hitzler and others\, 2012). An R2RML file was created using Web -Karma for transforming the feature-level instance data using the ontology and confirmed using standards specifications (University of Southern Cali fornia\, 2016\; Das and others\, 2012). The converted data and ontology ar e imported into a triplestore for data handling. \n\nA cartographic user interface (UI) was created as a foundation for the visualization and inter action of users with the triplestore graphs. The general guidelines given by the information search process model serves to guide UI functionality ( Kuhlthau\, 2004). The user interface offers menu search options by namespa ce for typically retrieving initial results. Multiple graphs can be visual ized at once. Other queries can be performed on the initial results appear ing on a map or table by faceted search and by query builder interfaces fo r SPARQL. An advanced feature description function retrieves related prope rties to support browsable graph searches. Linked Open Data were retrieved using SPARQL endpoints to test linking triples. Some GeoSPARQL support wa s created for geospatial queries on feature geometries of the GKG use case s. \n\nThe automated transformation ontology revealed aspects of data silo s that were known to exist. However\, the ontology model created a new per spective of data resources across the enterprise\, where resource semantic s could be streamlined for reuse. This was demonstrated in the post-proces sing stage of the ontology creation. The system and ontology design were v alidated through reasoning of semantically related data and pre-determined competency questions relevant to reasoning results. An ontology pattern o f aligning feature classes represented as codes and geometries of The Nati onal Map matched to the GeoSPARQL ontology feature and geometry classes wa s validated using reasoners. The ontology for feature interoperability pro vided inferred information for competency questions such as “What type o f feature is classified as FCode 73002\,” or “How are streams represen ted geometrically?” The GKG alignment with Linked Open Data used some s pecific widely used vocabularies to be reused between graphs\, and problem s encountered could be resolved by designing a better metadata annotation approach for structural alignment in addition to syntax matching. Multipl e GeoSPARQL queries executing topological relations on features were succe ssfully demonstrated with a pre-built query to find specified buildings on a road section between two cross streets. Such a query can depend on the shape of the road\, building distance from the roadway\, and other factor s. The queries required a change in viewpoint from machine computation to landscape cognition creating related semantic factors\, and then were foll owed by GeoSPARQL function computation. \n\nThis project tested some key c hallenges for GKG applications for spatial data infrastructure interoperab ility including data transformation\, ontology design\, information search and retrieval\, and multi-modality cartographic visualization. Completin g the resulting ontology from automated data transformation for knowledge representation is still a cognitive activity. RDF and OWL vocabulary were sufficiently expressive to demonstrate linking and reasoning successes. I mproved metadata annotation systems are needed for on-the-fly entity resol ution. Although initial tests of GeoSPARQL techniques were successful\, th e full capabilities of SPARQL as a rule-based reasoning tool would need fu rther research for queries that leverage the full semantic capabilities of knowledge graphs and for their portrayal. \n\nDisclaimer Any use of trad e\, firm\, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:A knowledge graph prototype for national topographic data - Dalia V aranka URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/CHBS39/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-NN8FGL@talks.osgeo.org DTSTART;TZID=CET:20220825T152500 DTEND;TZID=CET:20220825T153000 DESCRIPTION:The last two decades have seen the development and diffusion of new technologies and digital ecosystems for managing geographic data. The se include\, among others\, smartphones\, drones and open access satellite s on the one hand\, and the web 4.0\, GIS\, WebGIS\, geo-app and georefere nced data\, both open-source or proprietary\, on the other. This great var iety of tools\, accompanied by the sharing of new digital knowledge and sk ills\, have made the creation and management of spatial information much m ore accessible than it was in the past.\nThis has led to a proliferation o f processes for exploring\, creating and sharing geographical data from be low as a way for citizens\, that assume the role of neo-geographers or pro sumers\, to take part in decision-making in different kind of processes\, such as territorial\, environmental and climate change issues (Goodchild\, 2009\; Capineri et al.\, 2016\; See et al.\, 2016). \nHowever\, these are ongoing processes that have still to face technological\, cognitive and e conomic barriers. Universities with the use of open-source geo-information and communication technologies (Geo-ICTs) in enhance geographical learnin g should be a primary actor in supporting students and citizens in develop ing their own spatial thinking in a more efficient and engaging way (Käyh kö et al.\, 2021). In fact\, this is remarked also in objective 4 of the Sustainable Development Goals "to guarantee quality\, inclusive and equita ble education and to promote lifelong learning opportunities for all” an d many universities have signed the Higher Education Sustainability Initia tive (HESI) which commits them to integrate the concepts of sustainable de velopment into the curricula.\nIn this framework is involved also Universi ty of Padova (Italy) with its Jean Monnet Centre of Excellence on Climate Justice (Jean Monnet Erasmus+ project 2021-2023) led by the research group “Climate change\, territories\, diversities” (https://www.climate-jus tice.earth/). The Centre is trying to respond to the need of bringing the issues of Climate Justice and just transition from the EU Green Deal frame work into the dialogue between the academic world\, society\, and policy m akers. To do this\, it is carrying out different research and didactical a ctivities\, among which the development of a MOOC (Massive Open Online Cou rse) on GIScience for Climate Justice with the use of opensource and freew are Geo-ICTs\, that will be freely available for all before the end of 202 2.\nThis MOOC will provide videos and materials about practical activities concerning climate change and climate justice issues\, that the students can carry out autonomously using open-source and freeware tools. For every activity the workflow and a graphical abstract will be provided with aims and skills to be acquired and an introductive video with a real example o f use and suggestions about how to build collective projects of citizen sc ience. An auto-evaluation module will be available to students. MOOC will be tested with selected students and eventually adjusted before its online publication. A feedback and comment area to interact with staff members w ill be also available in the platform. The programme will follow learning by doing approach and is design to drive students through the main phases of a GIScience project:\n- The exploration and use of the European Platfor ms (e.g Earth Observation Portals\, Joint Research portals\, European Envi ronment Agency portals\, European Environmental Bureau)\n- The exploration and use of the Geonode on Climate Justice (https://research.climate-justi ce.earth/)\, the geo-platform of the Centre that will be available to ever yone with all the information collected by the Centre and the possibility to create online maps and to upload and share data by interested users or association groups. \n- The Collection and sharing of environmental and so cial information using geo-app and webGIS (e.g odk collect app and ona pla tform)\n- The exploration and use of Google Earth Pro and the OpenStreetMa p project Umap\n- The creation of storymaps to share climate change fighti ng initiatives and climate justice stories on the web (e.g knight lab stor ymap and geonode storymap tools)\n\nBy completing the MOOC\, students will learn how to autonomously update and increase their knowledge on climate change and climate justice issues\, learning to navigate and use European platforms and portals and to search for the documentation available in the European and international institutions. Practical activities will improv e skills of students and organizations of civil society to obtain and use data and information produced by European institutions\, to produce and sh are their own data\, and to prepare and manage collaborative projects for sustainability and environmental monitoring. \nOpen-source software will b e also the basis for the setup of the MOOC\, from its preparation using op en video editing and open document formats\, to its publication using the Moodle of the University of Padova. \nIn this contribution\, the theoreti cal background\, the entire methodology and workflow process for the prepa ration and dissemination of the MOOC will be presented and discussed\, wit h the aim to disseminate and share this experience to actors interested in developing similar activities of using of Geo-ICTs for Good. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Geo-ICTs for Good: a MOOC on GIScience for Climate Justice - Daniel e Codato URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/NN8FGL/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-YSG7XQ@talks.osgeo.org DTSTART;TZID=CET:20220825T153000 DTEND;TZID=CET:20220825T153500 DESCRIPTION:Lakes are a fundamental resource with a number of environmental benefits and with a not negligible influence on the local economy and on the quality of life. They work as a storage of water when floods or drough ts occur\, in the first case\, they are useful to laminate the excessed fl ux of water\, in the second as water supply during shortages. In addition\ , they influence the filling of groundwater and they play a role in the pr eservation of the general habitat biodiversity. From an economical point o f view\, they are an attraction for tourism\, residential living as well a s a source of recreation and of work for fishers.\nUnfortunately\, climate changes together with human activities are more and more threatening such resources modifying the known dynamics and affecting the general health s tatus of lakes (Fenocchi et al. 2018\; Free et al. 2021\; Lepori et al. 20 18).\nIn this context\, the INTERREG project SIMILE (System for the Integr ated Monitoring of Insubric Lakes and their Ecosystems)\, born from the co llaboration between Italy and Switzerland\, aim at developing an informati on system using an open source approach and based on innovative technologi es to help decision maker in the management and evaluation of the status o f the transboundary and sub-alpines lakes such as Lake Maggiore\, Lugano a nd Como. The SIMILE project wants to intensify the monitoring of these lak es by creating an open real-time monitoring system and by integrating data coming from different sources in order to create the possibility to fully exploit the potential with the heterogeneity of the available information and better studying the resource.\nThe work presented in this paper is fo cused on the achievements reached by the research carried out on lake Luga no in the context of the SIMILE project after two years of work. In partic ular\, the presented research is oriented on the automatic generation of s ome indicators that are usually calculated to evaluate the lake status thr ough the use of open standard\, software and hardware.\nLake Lugano is a t ransboundary lake divided in two main watersheds\, North and South\, respe ctively with an area of 27.5 Km2 and 21.4 Km2 and a maximum depth of 288 a nd 89 m. It is a eutrophic lake which has a critical health status in part icular during the 70s\, but thanks to new regulations and to the mitigatio n actions studied by the Swiss administration it is recovering. One of the fixed targets is to reach 150 gC/y which corresponds to a mesotrophic sta tus. This value gives information about the metabolism activity of the lak e and can be calculated using different approaches. At this moment\, on la ke Lugano\, to get such information monthly campaign according to the Niel sen method (Nielsen\, 1952). This approach is the one recognized by the ad ministration and it is conducted by specialist limnologists. However\, it has some issues that can be synthetized in three points: 1) it needs the u se of radioactive components\; 2) it is quite expensive in terms of man ho ur and the engagement of an external laboratory to analyze such kind of sp ecial samples\; 3) since it has a monthly temporal resolution it needs mat hematical model to interpolate data between the different campaigns.\nAcco rding to this overview\, the proposed paper wants to investigate a fully o pen web solution in order to calculate indicators that can help in underst anding the health status of the lake and try to solve the individuated lim its that are currently affecting the water monitoring. Such an open platfo rm uses open standards as the Sensor Observation Service (SOS) of the Open Geospatial Consortium (OGC) to integrate different sources of data and to offer the possibility to gather the information in a standardized way. Th anks to this achievement\, it was possible to develop. The scope is to st andardize the calculations and provide a solution where indicators can be calculated automatically saving also time since the traditional process. P otentially such an approach could calculate in real-time the indicators th anks to the use of the LISTEN/NOTIFY feature which exists in PostgreSQL\, the database technology on which the platform is based. Finally\, in this paper is presented the preliminary results of the development of a new al gorithm to calculate the lake metabolism which can\, if validated\, offer a new approach that can solve the individual issue of the current one. Bas ically\, the developed open monitoring system implemented and deployed on the lake offers real-time data\nThe platform is composed of dockerized and specialized services in order to offer a suite that is easily replicable\ , scalable and upgradeable.\nIn conclusion\, an overview of the results re ached during these years of project is presented. Such a solution increase s the replicability of the system since it is fully open and guarantees th e openness of data\, source code\, standards and also the hardware part. S uch technologies help in developing an automatic system that can calculate indicators to help decision makers in managing the water resource and sci entists to better study the new unknown dynamic and facing the new challen ges to which lakes are exposed. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Automatic assessment of lake health status using an open source app roach: Lugano lake case study - Daniele Strigaro URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/YSG7XQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-XTRBUU@talks.osgeo.org DTSTART;TZID=CET:20220825T153500 DTEND;TZID=CET:20220825T154000 DESCRIPTION:In developing countries\, sustainable development and territori al intelligence are of greater interest to public authorities and citizens . In Algeria\, the combination of resources with technological innovation goes in the direction of building a productive territorial intelligence. T his translates into a process aiming at developing a systemic approach of the territory in order to analyse its physical\, social and economic dimen sions in order to exchange the different points of view of the territorial \, social and economic actors and to make the policies more coherent. In t his contribution\, we have focused the research on studies related to deci sional computing used by governmental entities\, especially in the field o f public services. It turned out that the use of collaborative web platfor ms involving several actors belonging to different spheres (government\, e conomy\, social\, etc.)\, constitutes a tool for the development of territ orial intelligence thanks to the availability of data which allows a consi derable saving of time and cost. Indeed\, the construction of a territoria l information system makes possible the networking of these actors\, to el aborate clear and reliable schemes of urban planning for a liveable enviro nment\, which led us to think about the implementation of a web platform f or exchanges\, collections\, production and dissemination of data and soci al animation to reach equitable consensus. This will allow\, among other t hings\, the development of project management through the formalisation of objectives and collaborative work for the planning and optimisation of ta sks. Geographical information is a crucial element in most of the daily us es thanks to the intelligent applications put online and exploited by diff erent categories of connected people. Therefore\, the interest and necessi ty of sharing geo-located information for decision support systems is well proven nowadays. In the same context\, participatory mapping initiatives through voluntary geographic information (VGI)\, citizen-generated content or crowdsourcing are now being used as a new instrument for information g athering and two-way exchange between the various entities in the urban en vironment ranging from ordinary citizens to leading actors. This direct da ta is a key element in all the decision-making processes leading to the ac hievement of urban governance modalities. The objective of our work is to provide an interactive solution ensuring the collaboration of actors (deci sion-makers and citizens) on a webmapping platform for the reporting of ne eds by citizens in terms of public services such as road defects\, public lighting failures and any other existing problems in an urban area. This a pplication could also be used for emergency alerts (road accidents\, natur al disasters\, etc.). As a study area\, we chose the city of Oran\, locate d in the west of Algeria\, which is the second largest urban metropolis in the country. The realization of the collaborative web mapping platform is based on Free and Open-Source Software for Geospatial (FOSS4G). As a spat ial database management system\, we used PostgreSQL with its spatial exten sion PostGIS\, which is classified as one of the most powerful open source DBMS. The GIS server used in our application is GeoServer\, which guarant ees to satisfy a maximum of required webmapping services (WMS\, WFS\, WMTS \, WCS\, etc.). The webmapping interface must offer two main components: a n interactive citizen space with the web map and a space for decision make rs who will be able to consult\, verify and validate the data sent in orde r to proceed with the action. Among the development options for this type of webmapping interface\, we are interested in GeoNode\, an open source fr amework based on mature and robust frameworks and software like Django\, O penLayers\, PostGIS\, GeoServer and pycsw. In our case\, GeoNode will allo w the integration of a multitude of geospatial functions for manipulating data and responding to any type of request on the web map. The platform\, which we have named "Wilayati"\, will offer new participatory methods for monitoring activities in the urban environment. Its functionalities will e nsure\, on the one hand\, the sharing of data on a map based on voluntary contributions from the citizens of Oran and\, on the other hand\, the visu alisation and manipulation of the data by decision-makers in order to give them a support for the management of localised interventions. Different t ypes of data on the urban fabric of the city of Oran were collected from t he processing of satellite images as well as datasets on the road network of Oran obtained from OpenStreetMap after improving the intrinsic quality. In parallel\, a campaign on social networks will soon be launched\, with the aim of better analysing the orientations of the public services most r equested by citizens. The application\, under development\, will provide a new source of data that can be easily exploited in urban governance and w ill provide a way for citizens to participate in improving their environme nt through regular updates of the geographical database. Finally\, as a pe rspective\, the results\, after deployment of the platform\, will give an overview of the impact of citizens in participatory mapping highlighting p oints of interest and urban infrastructures of cities in Algeria. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Development of a collaborative platform for intelligent territorial mapping of the city of Oran - Nedjma Hadj Kaddour URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/XTRBUU/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-QZNUNL@talks.osgeo.org DTSTART;TZID=CET:20220826T090000 DTEND;TZID=CET:20220826T093000 DESCRIPTION:Processing Earth observation data modeled in a time-series of r aster format is critical to solving some of the most complex problems in g eospatial science ranging from climate change to public health. Researcher s are increasingly working with these large raster datasets that are often terabytes in size. At this scale\, traditional GIS methods may fail to ha ndle this processing and new approaches are needed to analyze these datase ts. The objective of this work is to develop methods to interactively anal yze big raster datasets with the goal of most efficiently extracting vecto r data over specific time periods from any set of raster data. \n\nIn thi s paper\, we describe RINX (Raster INformation eXtraction) which is an end -to-end solution for automatic extraction of information from large raster s datasets. RINX heavily utilizes open source geospatial techniques for in formation extraction. It also complements the traditional approaches with state-of-the-art high-performance computing techniques. This paper will di scuss details of achieving this big temporal data extraction including met hods used\, code developed\, processing time statistics\, project conclusi ons\, and next steps.\n\nThe input for RINX is a set of rasters from which the information has to be extracted and a set of data point locations for which the information needs to be extracted. The output for RINX is a str uctured representation of extracted information from the raster datasets f or each data point in CSV text format. The loading and pre-processing of t he input datasets to RINX is accomplished using a combination of Bash and SQL scripting techniques for automation. This pre-processed input is then fed into the open source spatial database PostGIS to extract the required information by using multiple spatial techniques. Finally\, the extracted output is post-processed for deduplication and standardization of extrac ted information for research use. RINX is designed in a way that makes it easy to deploy and scale on any local\, cloud\, or cluster computing plat form.\n\nRINX was created to aid the study of environmental conditions and how they affect the health of people over their lifespans. This involves calculating exposures such as air pollution\, humidity\, precipitation\, t emperature\, and other exposures at cohort member address locations over t ime. For initial work with one cohort\, daily precipitation\, temperature\ , and humidity estimates were needed for 4\,796 cohort address locations f or a 19 year time period\, 1999 – 2017.\n\nThe 800-meter resolution PRIS M Spatial Climate Dataset for the Conterminous United States was used as t he input for this data extraction. PRISM refers to Parameter-elevation Rel ationships on Independent Slopes Model\, created by the PRISM Climate Grou p\, Oregon State University. The PRISM dataset is published in .BIL raster format\, with one raster representing one climate variable per day for th e time period 1981 - 2020. The total size of the dataset is around 8 TB wi th over 100\,000 rasters of size 85 MB each. \n\nFor work on the initial c ohort\, RINX enabled the extraction of 7 key climate variables: precipitat ion\, temperature (maximum\, minimum\, mean)\, dew point temperature (mean )\, and vapor pressure deficit (minimum\, maximum) for 19 years of data fr om 48\,500 800-meter resolution rasters for 4\,796 data points. This resul ted in a total of 10.3 Million “patient-day” calculations creating a t otal of 72.1M observations. Additionally\, absolute and relative humidity were calculated using the existing mean temperature and dewpoint variables . RINX provided a unified solution of 9 climate variables for all persons/ days for the entire dataset. It was deployed and scaled on multiple server s on a high-performance computing cluster. Our initial results reveal that it is extremely fast and efficient in processing large raster datasets. I t took 1 day to load and 4 days to process and extract 7 climate variables from 48\,500 rasters for the 72.1M observations at 4\,796 locations. RINX enabled the researchers to analyze this big climate dataset at a fine-gra ined address level with high efficiency and speed. Once the scripts were w ritten\, tested\, and fine tuned\, processing time was reduced from months to days compared to traditional methods\, resulting in substantial time s avings.\n\nWe are currently testing RINX on a much larger dataset of 100\, 000 input point locations for a time period of 1981 - 2020\, spanning the full range of the PRISM 800m data. This climate data is only available for purchase\, however the PRISM Climate Group has made a version of this dat a available for free at a resolution of 4 kilometers. To make our solution entirely repeatable with open source software\, code\, and data\, we will use RINX to extract point location data from the freely available 4km PRI SM data. Results from these analyses will be presented as part of this pap er. \n\nOur solution is based on open source technology\, using PostGIS th at can be deployed on local or cluster computing environments. It provide s an efficient way to solve geospatial big data problems\, particularly th ose involving large temporal raster datasets where point location data ext raction is desired. Big data is changing the ways data is managed and anal yzed. The next generation GIS tools can help researchers process big data at scale. RINX is an end-to-end data extraction and processing solution fo r large raster datasets. RINX is open-source and will be shared on Github. It can be easily deployed and scaled on any local\, cloud\, or cluster co mputing environment. We used RINX for processing on a large number of PRIS M climate datasets\, however our solution could be applied to any temporal raster data such as NDVI\, night lights\, and more. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:RINX: A Solution for Information Extraction from Big Raster Dataset s - Devika Jain URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/QZNUNL/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-XHUGFC@talks.osgeo.org DTSTART;TZID=CET:20220826T090000 DTEND;TZID=CET:20220826T093000 DESCRIPTION:Map renderers play a crucial role in various applications deplo yed in Web\, desktop\, mobile\, and embedded environments. For instance\, we rely upon them to travel\, commute\, find the best hotels and restauran ts\, and locate our closed ones. More digital applications emerge in vario us areas\, such as urban planning\, transportation\, or even pandemic moni toring\, as they get adopted. Beyond digital environments\, it is worth no ting that maps also get printed in books\, reports\, or pieces of urban fu rniture.\n\nIn this context\, code portability\, i.e.\, the ability to use the same codebase on various platforms\, is a common problem. For instanc e\, Mapbox and Maplibre both maintain a JavaScript codebase for the Web (e .g.\, maplibre-gl-js) and a C++ codebase for native platforms (e.g.\, mapl ibre-gl-native). These codebases enable their renderers to run in all majo r browsers (thanks to WebGL)\, in the main desktop and mobile environments \, on servers (e.g.\, for headless rendering)\, and in cars\, planes\, or embedded settings. Guarantying that these renderers behave similarly and p roduce the same outputs on all these platforms is hard\, costly\, and slow s down the ability of development teams to innovate and improve renderers. \n\nIn this paper\, we review the most popular map renderers from a portab ility point of view. We show that the existing codebases written in Javasc ript\, C++\, and Java fail at least in one area or another at producing a portable map renderer. Additionally\, we present a state of the art for co de portability\, and we describe emerging standards and technologies that promise to enable truly portable and high-performance map renderers writte n in C++ or Rust to emerge. Among these emerging technologies\, we find:\n \n- **Rust -** Rust is a high-level programming language designed for safe ty and high performance. The project started at Mozilla and is now develop ed by the Rust foundation. Its compiler targets native architecture\, enab ling it to compile applications for desktop (x86) and mobile (arm) environ ments. Additionally\, the Rust compiler can target WebAssembly\, a binary instruction format that can run on web browsers with near-native speeds. T his not only enables Rust applications to run in native environment but al so to be included as a library in Web applications. As a result\, the same codebase can be used anywhere with only a few modifications.\n\n- **WebGP U -** WebGPU is a 3D low-level API that runs on top of DirectX\, Metal\, V ulkan or OpenGL depending on the platform and gives the developer access t o the GPU. It is developed by the W3C GPU for the Web Community Group with engineers from Apple\, Microsoft\, Mozilla\, Google\, and others. It is c onsidered the successor of WebGL version 2. Contrary to WebGL version 1 an d WebGL version 2\, which were solely designed for the Web\, WebGPU implem ents a standard header file (webgpu.h) that makes it cross-platform.\n\nBa sed on the emerging technologies identified in the review\, we study the f easibility of creating a truly portable map renderer. We present maplibre- rs\, a proof-of-concept released under the terms of the Apache Software Li cense\, that can render vector tiles natively and in the browser. We descr ibe its overall architecture and highlight some of the challenges encounte red while devising a portable solution that transforms vector tiles into 2 d and 3d objects. These challenges include:\n\n- **Rendering 2d vector til es in a 3d environment -** The vector tile specification describes simple 2d objects encoded in grid coordinates\, such as points\, lines\, polygons \, multi-polygons\, and polygons with holes. Several steps enable to conve rt these 2d objects into 3d objects that can be rendered in a scene\, incl uding: the conversion of grid coordinates into 3d scene coordinates\; the tessellation of polygons to display surfaces in the 3d environment\; the e xtrusion of buildings based on their number of storeys with an attribute s tored in the vector tiles.\n\n- **Using WebGPU as a portable 3d rendering pipeline -** WebGPU exposes a wide variety of features to render 3d scenes . Among them\, we explore: the rendering of the 3d objects with the WebGPU Shading Language (WGSL) based on styling rules and object attributes\; Th e navigation within the 3d world with the camera\, user inputs\, rotation on 3 axes\, levels of details and occlusion culling\; The configuration of the graphic card\, graphics API and more. \n\n- **Devising a portable net work library -** Rust does not provide a network library that works both n atively and in the browser. We created a uniform interface to download vec tor tiles to address this issue. This interface\, based on the facade patt ern\, uses macros to select the proper implementation at compile-time depe nding on the targeted architecture. The native implementation relies on th e HTTP package of the standard library. The WebAssembly implementation rel ies on Fetch API bindings.\n\nFinally\, we present our future work and exp lore possible ameliorations. Overall\, this review and feasibility study g ives an exciting glimpse on a possible future for map renderers\, where th e same code can run natively and in a browser. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Maplibre-rs: Toward portable map renderers - Bertil Chapuis URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/XHUGFC/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-DTQGKM@talks.osgeo.org DTSTART;TZID=CET:20220826T093000 DTEND;TZID=CET:20220826T100000 DESCRIPTION:The submerged topography of rivers is a crucial variable in flu vial processes and hydrodynamics models. Fluvial bathymetry is traditional ly realised through echo sounders embedded on vessels or total stations an d GNSS receivers whether the surveyed riverbeds are small streams or dry. Besides being time-consuming and often spatially limited\, traditional riv erine bathymetry is strongly constrained by currents and deep waters. In s uch a scenario\, remote sensing techniques have progressively complemented traditional bathymetry providing high-resolution information. To date\, t he peak of innovation for bathymetry has been reached with the use of opti cal sensors on uncrewed aerial vehicles (UAV) systems\, along with green l idars (Vélez-Nicolás et al.\, 2021). The main obstacle in optical-derive d bathymetry is the refraction of the light passing the atmosphere-water i nterface. The refraction distorts the photogrammetric scene reconstruction \, causing in-water measures to be underestimated (i.e.\, shallower than r eality). To correct these distortions\, radiometric-based methods are freq uently applied. They are focused on the spectral response of the means cro ssed by the light and are typically built on the theory that the total rad iative energy reflected by the water column is function of the water depth (Makboul et al.\, 2017). The primary goal of the research on submerged to pography is to understand the relationship between the water column reflec tance and the water depth using statistical and trigonometrical models. Th e spread of artificial intelligence has given a new light of interest on s pectral-based bathymetry by investigating the non-linear and very complex relationship between variables (Mandlburger et al.\, 2021). To train artif icial intelligence models\, large amounts of data are usually necessary\; therefore\, participatory approach and data sharing are required to build statistically-relevant datasets. In this scenario\, FOSS tools and distrib uted resources are mandatory to manage the dataset and allow the replicabi lity of the methodology. \nThis work aims to test the effectiveness of ar tificial intelligence to correct water refraction in shallow inland water using very high-resolution images collected by Unmanned Aerial Vehicles (U AV) and processed through a total FOSS workflow. The tests focus on using synthetic information extracted from the visible component of the electrom agnetic spectrum. An artificial neural network is created with the data fr om three different case studies placed in west-north Italy\, and geologica lly and morphologically similar.\nThe data for the analysis were collected in 2020. Each data collection was realised using a UAV commercial solutio n (DJI Phantom 4 Pro)\, and the following datasets were generated: i) RGB georeferenced orthomosaic of the riverbed and banks obtained from photogra mmetric process\, ii) georeferenced Digital Elevation Model (DEM) of the r iverbed obtained from photogrammetric process\, iii) GNSS measures of the riverbed and the riverbanks.\nThe UAV-collected frames were elaborated thr ough a standard structure from motion (SfM) procedure. Visual SfM was empl oyed to align images and the 3D point cloud computation. The digital surfa ce model (DSM) and the orthomosaic production were generated starting from the point cloud in Cloud Compare software. By applying the so-called dire ct-photogrammetry\, the point clouds were directly georeferenced in the WG S84-UTM32 coordinate system thanks to the positioning information retrieve d from the embedded GNSS dual-frequency receiver (Chiabrando\, Lingua and Piras\, 2013). Using the information regarding the camera position and the local height model provided by the national military Geographic Institute (IGM)\, the ellipsoidal heights were translated into orthometric heights. The GNSS measures had 3 cm accuracy on the vertical component and 1.5cm o n the horizontal components. \nThe RGB information\, DSM and seven radiome tric indices (i.e.\, Normalised Difference Turbidity Index\; Red and Green Ratio\; Red and Blue Ratio\; Green and Red Ratio\; Green and Blue Ratio\; Blue and Red Ratio\; Blue and Green Ratio) were calculated and stacked in an 11-bands raster (input raster). The Up component of the bathymetry cro ss-sections constituted the so-called "Z_GNSS" dataset and is the dependen t variable of the regression. The position (Easting\, Northing\, Up) of ea ch Z-GNSS observation was used to extract the pixel values of each band of the input photogrammetric dataset\, including the photogrammetric DEM. Th e dataset was then normalised and divided into test (20% observations) and training (80% observations) datasets.\nIn this work\, a 5-layer multilaye r perceptron (MLP) networks model with three hidden layers was built in Py thon using the deep learning library Keras with TensorFlow backend (Abadi et al.\, 2016). The ReLu activation function was added to the ANN layers t o bring non-linear properties in the network. The dimension of the input l ayer is 11\, and the weights are initialised to small Gaussian random valu es (kernel initialiser 'Normal') despite usually skewed or bimodal. A kern el regulizer\, L1\, was added to reduce the overfitting. The applied optim iser to update weights in the network is the Adaptive Moment Estimation (A dam) search technique\, and the loss function\, which evaluates the model used by the optimiser to navigate the weights\, is the mean absolute error between the predicted output and the target output.\nThe network was trai ned on the normalised dataset. The r-squared score\, the Mean squared erro r and the Mean absolute error were computed. Finally\, the permutation imp ortance was measured using the eli5 python library. \nThe neural network r egressor performed over 0.80 of r-squared score on the test dataset. As ex pected\, the permutation importance analysis reveals the high impact of th e DEM and visible bands\, and low importance scores are reported for ratio s bands. \nThe results are satisfying and quite relevant\, although the mo del is the first step through a more complex and deeper neural network to correct water distortions in rivers. It has been trained on a relatively s mall dataset\, but we intend to follow up with the research\, add more dat a\, and develop a free and open tool for the scientific community. The pr esent work\, provide a good insight about the high reliability and accurac y of artificial intelligence approaches in optical-derived bathymetry. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Laying the foundation for an artificial neural network for photogra mmetric riverine bathymetry - Elena Belcore URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/DTQGKM/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-XTSQPL@talks.osgeo.org DTSTART;TZID=CET:20220826T093000 DTEND;TZID=CET:20220826T100000 DESCRIPTION:Digital elevation models (DEMs) are a representation of the top ography of the Earth\, stored as elevation values in regular raster grid c ells. These data serve as basis for various geomorphological applications\ , for example\, for landslide volume estimation. Access to timely\, accura te and comprehensive information is crucial for landslide analysis\, chara cterisation and for understanding (post-failure) behaviours. This informat ion can subsequently be used to effectively assess and manage potential ca scading hazards and risks\, such as landslide dam outburst floods or debri s flows. Freely available DEM data has been an important asset for landsli de volume estimation. Earth observation (EO) techniques\, such as DEM diff erencing\, can be leveraged for volume estimation. However\, their applica bility is reduced by high costs for commercial DEM products\, limited temp oral and spatial coverage and resolution\, or insufficient accuracy. \n\n Sentinel-1 synthetic aperture radar (SAR) data from the European Union's E arth observation programme Copernicus opens the opportunity to leverage fr ee SAR data to generate on-demand multi-temporal topographic datasets. Sen tinel-1 A & B data provide a new opportunity to tackle some of the problem s related to data costs and spatio-temporal availability. Moreover\, the E uropean Space Agency (ESA) guarantees the continuity of the Sentinel-1 mis sion with the planned launch of another two satellites\, i.e.\, Sentinel-1 C & D. Interferometric SAR (InSAR) approaches based on Sentinel-1 have of ten been used to detect surface deformation\; however\, few studies have a ddressed DEM generation (Braun\, 2021). For example\, Dabiri et al. (2020) tested Sentinel-1 for landslide volume estimation\, but highlighted the n eed to further research and systematically assess the accuracy of the gene rated DEMs. InSAR analysis is often conducted using commercial software\; however\, a well-structured workflow based on free and open-source softwar e (FOSS) increases the applicability and transferability of the DEM genera tion method. Although a general workflow for DEM generation from Sentinel- 1 imagery based on InSAR has been described and documented (ASF DAAC\, 201 9\; Braun\, 2020\, 2021)\, there is still a need for improvement\, harmoni sation and automation of the required steps based on open-source tools. \n \nWithin the project SliDEM (Assessing the suitability of DEMs derived fro m Sentinel-1 for landslide volume estimation)\, we explore the potential o f Sentinel-1 for the generation of multi-temporal DEMs for landslide asses sment leveraging FOSS. Relying on the open-source Sentinel Application Pla tform (SNAP) developed by the ESA\, the Statistical-Cost\, Network-Flow Al gorithm for Phase Unwrapping (SNAPHU) developed by Stanford University\, a nd several other open-source software publicly available for geospatial an d geomorphological applications\, we work on a semi-automated and transfer able workflow bundled in an open-source Python package that is currently u nder active development. The workflow uses available Python SNAP applicati on programming interfaces (APIs)\, such as snappy and snapista. We distrib ute the SliDEM package within a Docker container\, which allows its usage along with all its software dependencies in a structured and straightforwa rd way\, reducing usability problems related to software versioning and di fferent operating systems. The final package will be released under an ope n-source license on a public GitHub repository. \n\nThe package consists o f different modules to 1) query Sentinel-1 image pairs based on perpendicu lar and temporal baseline thresholds that also match a given geographical and temporal extent\; 2) download and archive suitable Sentinel-1 image pa irs\; 3) produce DEMs using InSAR techniques and perform necessary post-pr ocessing such as terrain correction and co-registration\; 4) perform DEM d ifferencing of pre- and post-event DEMs to quantify landslide volumes\; an d 5) assess the accuracy and validate the generated DEMs and volume estima tes against reference data. The core module focusses on DEM generation fro m Sentinel-1 using InSAR techniques available in SNAP. The script co-regis ters and debursts Sentinel-1 image pairs before generating and filtering a n interferogram. Phase unwrapping is performed using SNAPHU. The unwrapped phase is then converted into elevation values\, which are finally geometr ically corrected and co-registered to a reference DEM. Co-registration is based on assessing the normalised elevation biases over stable terrain (af ter Nuth and Kääb\, 2011). \n\nWe assess errors and uncertainties for e ach step and the quality of the Sentinel-1 derived DEMs using reference da ta and statistical approaches. The semi-automated workflow allows for the generation of DEMs in an iterative and structured manner\, where a systema tic evaluation of the resulting DEM quality can be performed by testing th e influence of different temporal and perpendicular baselines\, the usage of ascending and descending passes\, distinct land use/land cover and topo graphy\, among other factors. Several major landslides in Austria and Norw ay have been selected to evaluate and validate the workflow in terms of re liability\, performance\, reproducibility\, and transferability. \n\nThe SliDEM workflow represents an important contribution to the field of natur al hazard research by developing an open-source\, low-cost\, transferable\ , and semi-automated method for DEM generation and landslide volume estima tion. From a practical perspective\, disaster risk management can benefit from efficient methods that deliver added-value information. From a techni cal point of view\, SliDEM tackles scientific questions on the validity of EO-based methods and the quality of results related to the assessment of geomorphological characteristics of landslides. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:An open-source-based workflow for DEM generation from Sentinel-1 fo r landslide volume estimation - Lorena Abad URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/XTSQPL/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-Y98TUU@talks.osgeo.org DTSTART;TZID=CET:20220826T100000 DTEND;TZID=CET:20220826T103000 DESCRIPTION:Open mapping leverages on volunteer mappers mobilized and engag ed from the public. volunteers most often are trained and coordinated vir tually to carry out dedicated mapping task\, irrespective of their geograp hic location\, professional and academic background. In this study volunte er mappers engaged are categorized into two namely: the Local Volunteer Ma ppers (LVM) comprising of all the potential and actual mappers resident in Nigeria and the Remote Volunteer Mappers (RVM) comprising of all potentia l and actual mappers not resident in Nigeria. \nThe study sampled 2 Loca l Government Areas (LGAs) of River State from the 4 vulnerable oil spill d isaster LGAs of Ogoni land communities. Ogoni land is a major oil spill di saster vulnerable area of Nigeria\, being the major host communities of cr ude oil exploitation in the Niger Region of Nigeria. Following the hazardo us impact and damage of Ogoni land by oil Spill disaster over the years of oil exploitation in Niger Delta\, UNEP assessed that the environmental re storation of Ogoni land would require coordinated efforts on the part of g overnment agencies at all levels\, industry operators and communities. UNE P also presented its recommendations as a major opportunity to bring new i nvestment\, employment opportunities and a culture of cooperation to Ogoni land in addition to driving improvements in the environmental and health situation on the ground. To effectively implement the UNEP recommendations for restoration of Ogoni land\, there is a need for a geographic data tha t provides critical building footprint in the area\, especially\, to ident ify and access the vulnerable oil spill communities. Maps produced would b e used by government agencies and other stakeholders working to implement UNEP report on Ogoni land restoration as well sustainable development.\nCo nsequently\, the study engaged volunteer mappers to respond to sampled Oil spill communities viz-viz 3 LGAs in Rivers State\, Niger Delta Region of Nigeria. To assess the level of participation of Local (mappers in Nigeria ) and Remote Mappers (Not Resident in Nigeria)\, two mapping projects were created in HOT tasking manager for local and remote mappers respectively. For the purpose of campaigning for Volunteer Mappers the 2 project tasks were tagged ‘’ Mapathon Battle for Vulnerable Oil Spill Disaster Commu nities in Niger Delta’’ respectively. Project task 6358 was created ex clusively for remote mappers outside Nigeria to map Tai LGA\, while\, proj ect task 6359 was created exclusively local mappers resident Nigeria to ma p Gokana LGA in a Mapathon battle challenge. Project task 6358 had a total grided cells of 825 mapping tasks for online engagement of mappers while project task 6359 had an automated grided cells of 706 mapping tasks due t o differences in the size of the area. The Mapathon unveiled the followin g research results. Engagement of remote mappers for project task 6358-Tai LGA shows that out of the 583 tasks completely mapped\, only 13 were yet to be validated after 2 years of creating the project. This is as a result of archiving the project and diversion of attention to urgent tasks. The project recorded a total of about 16\,416 edits comprising of 13\,552 buil dings and 858km of roads mapped in Tai LGA within the timeline of the stud y. Demographic characteristics of the contributors to project 6358 on the basis of HOT Tasking Manager users by experience and level shows that 50% were advance mappers and 100 % has more than 1 year mapping experience .T he project engaged a total of 56 contribtors by mapping and validation. A ll mappers and validators by experience has used the tasking manager for m ore than 1 year while their mapping levels ranges between 40% for begin ner mapper\, 10 % for intermediate and 50 % for advanced mappers. The proj ect timeline as illustrated by the graph shows that mapping and validation of the Tai LGA task commenced on the same date: 6th August\,2019 at the r ate of 12% mapping and 2 % validation. Mapping progressively ascended to 6 4% on the 4th day and got to its peak on the 9th day being 15th August wit h 99% of the entire task mapped. However\, validation of the mapping task had a straight curve with the highest peak of validation being the 12th of September with 95% of the task being validated. By 8th January \,2020\, b eing 6th months of the project\,100% of the tasks were completely mapped w hile 13 of the 596 tasks were yet to be validated. The timeline statistics also shows that an average of 20mintes 46 seconds was the time spent per task to map a total of 583 tasks of 16\,416 edits. Also\, an average of 6m inutes 16seconds was spent for validation per task leaving about 1hour 21m inutes 29seconds to finish up the validation of 13 tasks left unvalidated due to a shift to other project tasks and less passion for the project und er study. However\, the analysis of local mappers engaged in HOT Project T ask 6359 Gokana LGA also unveiled the following: The study shows that 706 (100%) of the tasks were completely mapped except for validation of 473(67 %) tasks which requires further coordination of mappers. There is no recor d of bad imagery and tasks left unmapped. The project also recorded a tota l of about 2064 changesets for mapping a total of about 18\,367 edits\, co mprising of 14\,983 buildings and 521 km of roads. The project also recode d a total of 173 contributors comprising of 169 mappers and 8 validators. These mappers (100%) had more than 1year experience in online mapping with OpenStreetMap and are categorized into beginner mappers (72%)\, intermedi ate (6%) and advance mappers (21%). The entire project timeline by mapping and validation took a period of about 2years 4months(28months) from 6th A ugust 2019 to 27th December \,2021 as at the time of writing this report. Conclusively\, there is a lacuna worthy of research investigation in the m apping response level and capability of remote mappers from other countrie s and local mappers from Nigeria in crowdsourced rapid response mapping us ing OpenStreetMap. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Analysis of Local and Remote Mappers’ Open Geographic Data Contri bution to Oil Spill Disaster Response in Niger Delta Region\, Nigeria - Dr .Victor N.Sunday URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/Y98TUU/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-L3TESQ@talks.osgeo.org DTSTART;TZID=CET:20220826T100000 DTEND;TZID=CET:20220826T103000 DESCRIPTION:Introduction\n\nOrigin-destination (OD) datasets provide inform ation on aggregate travel patterns between zones and geographic entities. OD datasets are ‘implicitly geographic’\, containing identification co des of the geographic objects from which trips start and end. A common app roach to converting OD datasets to geographic entities\, for example repre sented using the simple features standard (Open Geospatial Consortium Inc 2011) and saved in file formats such as GeoPackage and GeoJSON\, is to rep resent each OD record as a straight line between zone centroids. This appr oach to representing OD datasets on the map has been since at least the 19 50s (Boyce and Williams 2015) and is still in use today (e.g. Rae 2009).\n \nBeyond simply visualising aggregate travel patterns\, centroid-based geo graphic desire lines are also used as the basis of many transport modellin g processes. The following steps can be used to convert OD datasets into r oute networks\, in a process that can generate nationally scalable results (Morgan and Lovelace 2020):\n\n OD data converted into centroid-based geographic desire lines\n Calculation of routes for each desire line\, with start and end points at zone centroids\n Aggregation of routes int o route networks\, with values on each segment representing the total amou nt of travel (‘flow’) on that part of the network\, using functions su ch as overline() in the open source R package stplanr (Lovelace and Elliso n 2018)\n\nThis approach is tried and tested. The OD -> desire line -> rou te -> route network processing pipeline forms the basis of the route netwo rk results in the Propensity to Cycle Tool\, an open source and publicly a vailable map-based web application for informing strategic cycle network i nvestment\, ‘visioning’ and prioritisation (Lovelace et al. 2017\; Goo dman et al. 2019). However\, the approach has some key limitations:\n\n Flows are concentrated on transport network segments leading to zone cent roids\, creating distortions in the results and preventing the simulation of the diffuse networks that are particularly important for walking and cy cling\n The results are highly dependent on the size and shape of geogr aphic zones used to define OD data\n The approach is inflexible\, provi ding few options to people who want to use valuable OD datasets in differe nt ways\n\nTo overcome these limitations we developed a ‘jittering’ ap proach to conversion of OD datasets to desire lines that randomly samples points within each zone (Lovelace\, Félix\, and Carlino Under Review). Wh ile that paper discussed the conceptual development of the approach\, it o mitted key details on its implementation in open source software.\n\nIn th is paper we outline the implementation of jittering and demonstrate how a single Rust crate can provide the basis of implementations in other langua ges. Furthermore\, we demonstrate how jittering can be used to create more diffuse and accurate estimates of movement at the level of segments (‘f lows’) on transport network\, in reproducible code-driven workflows and with minimal computational overheads compared with the computationally int ensive process of route calculation (‘routing’) or processing large GP S datasets. The overall aim is to describe the jittering approach in techn ical terms and its implementation in open source software.\n\nBefore descr ibing the approach\, some definitions are in order:\n\n Origins: locati ons of trip departure\, typically stored as ID codes linking to zones\n Destinations: trip destinations\, also stored as ID codes linking to zone s\n Attributes: the number of trips made between each ‘OD pair’ and additional attributes such as route distance between each OD pair\n Ji ttering: The combined process of ‘splitting’ OD pairs representing man y trips into multiple ‘sub OD’ pairs (disaggregation) and assigning or igins and destinations to multiple unique points within each zone\n\nAppro ach\n\nJittering represents a comparatively simple — compared with ‘co nnector’ based methods (Jafari et al. 2015) — approach is to OD data p reprocessing. For each OD pair\, the jittering approach consists of the fo llowing steps for each OD pair (provided it has required inputs of a disag gregation threshold\, a single number greater than one\, and sub-points fr om which origin and destination points are located):\n\n Checks if the number of trips (for a given ‘disaggregation key’\, e.g. ‘walking’ ) is greater than the disaggregation threshold.\n If so\, the OD pair i s disaggregated. This means being divided into as many pieces (‘sub-OD p airs’) as is needed\, with trip counts divided by the number of sub-OD p airs\, for the total to be below the disaggregation threshold.\n For ea ch sub-OD pair (or each original OD pair if no disaggregation took place) origin and destination locations are randomly sampled from sub-points whic h optionally have weights representing relative probability of trips start ing and ending there.\n\nThis approach has been implemented efficiently in the Rust crate odjitter\, the source code of which can be found at https: //github.com/dabreegster/odjitter.\nResults\n\nWe have found that jitterin g leads to more spatially diffuse representations of OD datasets than the common approach to desire lines that go from and to zone centroids. We hav e used the approach to add value to numerous OD datasets for projects base d in Ireland\, Norway\, Portugal\, New Zealand and beyond. Although useful for visualising the complex and spatially diffuse reality of travel patte rns\, we found that the most valuable use of jittering is as a pre-process ing stage before routing and route network generation. Route networks gene rated from jittered desire lines are more diffuse\, and potentially more r ealistic\, that centroid-based desire lines.\n\nWe also found that the app roach\, implemented in Rust and with bindings to R and Python (in progress )\, is fast. Benchmarks show that the approach can ‘jitter’ desire lin es representing millions of trips in a major city in less than a minute on consumer hardware.\n\nWe also found that the results of jittering depend on the geographic input datasets representing start points and trip attrac tors\, and the use of weights. This highlights the importance of exploring the parameter space for optimal jittered desire line creation.\nNext step s\n\nWe plan to create/improve R/Python interfaces to the odjitter and ena ble others to benefit from it.\n\nWe plan to improve the package’s docum entation and to test its results\, supporting reproducible sustainable tra nsport research worldwide. DTSTAMP:20260914T082223Z LOCATION:Room Hall 3A SUMMARY:Exploring jittering and routing options for converting origin-desti nation data into route networks: towards accurate estimates of movement at the street level - Robin Lovelace URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/L3TESQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-YRLJNG@talks.osgeo.org DTSTART;TZID=CET:20220826T113000 DTEND;TZID=CET:20220826T120000 DESCRIPTION:In many parts of Burkina Faso\, competition over land use has i ncreased tensions and often conflicts between farming and herding communit ies. Allocating land for farming or grazing is increasingly perceived as a zero-sum calculation among these communities. As a response\, the governm ent of Burkina Faso created “Pastoral Zones” across the country as res erves for livestock herders where animals could graze without the risk of entering cropland. Farming in these areas is typically prohibited unless d one by herders residing within the reserve. However\, farms have appeared in pastoral zones over the years\, reducing resources available to herders and exacerbating already fraught tensions between herding and farming com munities (Nébie et al 2019). This study uses Sentinel 2 imagery to quanti fy to what extent agricultural growth is encroaching on two such pastoral zones in Southern Burkina Faso\, Niassa and Sondré-Est. This study found a significant growth of agricultural cultivation in both zones between the period of 2016 and 2021.\n\nTo map agricultural growth\, Sentinel 2 image ry was used in Google Earth Engine (GEE). Reproducibility and accessibilit y were prioritized\, hence the use of a free platform and open EO data was prioritised. Google Earth Engine stood out as an accessible cloud platfor m to easily access the imagery and run the analysis (Gorelick et al\, 2017 ). To visualise agricultural areas\, the “3 Period Timescan” (3PTS) Me thod was employed. This method uses a series of NDVI Images from the Senti nel 2 satellite throughout a growing season to isolate areas of active cul tivation. This product consists of a Red-Green-Blue composite of Sentinel- 2 Images where the red band represents the maximum NDVI value during the f irst period of the growing season\, the green the maximum NDVI in the midd le\, and the blue the maximum NDVI at the end. As a result\, the method is able to create a seasonal time-series profile of NDVI. A single NDVI prod uct provides an indication of vegetation presence on a given date\, but it is not sufficient to distinguish croplands from other types of vegetation . Croplands are thus identified by their temporal evolution of NDVI values throughout the different phases of the agricultural season: photosyntheti c activity of crops is low during the planting period (“beginning of the season”\, approximated by 15th June to 1st August)\, increases during t he growing phase (“middle”\, 2nd August to 1st September) until reachi ng a maximum value right before the harvest\; once harvested\, NDVI values decrease drastically (“end of season”\, 2nd September to 15th Octobe r). Thus\, the approach employed for investigating cropland change conside rs maximum NDVI values for those three separate subperiods of the agricult ural season and aggregates this information into a higher-level product\, a RGB color composite so-called 3-Period TimeScan\, reflecting the vegetat ion temporal evolution during the agricultural period\, at 10m resolution (Boudinaud and Orenstein\, 2021). \n\n3PTS images allow for a user-friendl y method to visually identify cropland. Cropland pixels from 3PTS images\, when visualized in GEE appear in a dark blue due to the sharp changes fro m the 2nd and 3rd periods of the time series. This contrasts well with nat ural vegetation\, which has a smoother temporal profile with a noticeable peak in the 2nd period and thus appears greener or a much lighter blue. F orests\, due to their high NDVI values throughout the entire growing seaso n appear in white\, due to the saturation of all 3 bands. Bare soil\, with it’s low NDVI values throughout all 3 periods appears as nearly black p ixels. \n\nRather than machine learning\, visual identification was the pr eferred method of identification due to the relatively small size of each pastoral zone. The time needed to prepare training data and clean the resu lts of a supervised classification would have exceeded the time to manuall y identify each area of cropland. As a result\, once the images were treat ed by GEE\, they were manually traced within QGIS. The 3PTS script\, origi nally made for GEE was then translated to run in PyQGIS. Once run\, the sc ript created a raster image for each year’s growing season in the archiv e (2016-2021) and polygons were traced over each visualised cluster of cro pland. The total surface area of all polygons was then calculated for each year. A github repository contains both the PyQGIS and GEE code and can b e run with no prerequisites (https://github.com/oren-sa/3PTS).\n\nThe resu lts of the study indicate a significant increase in cultivation in both zo nes between 2016 and 2021. For Sondré Est\, this change amounted to 40% a nd 160% for Niassa.Curiously\, the largest increase in cultivation seems t o occur between 2016 and 2017. This is especially so for Niassa. Nonethele ss\, increases in cultivation increased with each passing year until the p resent year of 2021. A number of these fields are suspected to be encroac hments\, given their proximity to the border of the zone and that many are contiguous with the agricultural fields outside of the zone’s borders. However\, it is estimated that a number of the fields are the result of th e zones’ resident herders planting fodder or other cereals. The latter a ssumption is made based on the location of the fields in question (far fro m the borders of the reserves) and their proximity to permanent structures in the reserves (habitations\, wells or park buildings). DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Using Sentinel 2 images to quantify agricultural encroachment in Bu rkina Faso’s protected livestock reserves - Alex Orenstein URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/YRLJNG/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-YWMWFK@talks.osgeo.org DTSTART;TZID=CET:20220826T120000 DTEND;TZID=CET:20220826T123000 DESCRIPTION:Producing and providing useful information for climate services requires vast volumes of data to come together that further requires tech nical standards. Beside ordinary base processes for climate data processin g like polygon subsetting\, there is the special case of extreme climate e vents and their impacts\, where scientific methods for appropriate assessm ents\, detection or even attribution are facing high complexity for the da ta processing workflows. Therefore the production of climate information s ervices requires optimal science based technical systems\, named in this p aper climate resilience information systems (CRIS). CRIS like the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S) are conne cted to distribute data archives\, storing huge amounts of raw data themse lves and containing processing services to transform the raw data into usa ble enhanced information about climate related topics. Ideally this climat e information can be requested on demand and is then produced by the CRIS on request by the user. This kind of CRIS can be enhanced when scientific workflows for general climate assessment or even extreme events detection are optimized as information production service\, accordingly deployed to be usable by extreme events experts to facilitate their work through a fro ntend. Deployment into federated data processing systems like CDS requires that scientific methods and their algorithms be wrapped up as technical s ervices following standards of application programming interfaces (API) an d\, as good practice\, even FAIR principles. FAIR principles means to be F indable within federated data distribution architectures\, including publi c catalogs of well documented scientific analytical processes. Remote stor age and computation resources should be operationally Accessible to all\, including low bandwidth regions and closing digital gaps to ‘Leave No On e Behind’. Aggreeing on standards for Data inputs\, outputs\, and proces sing API are the necessary conditions to ensure the system is Interoperabl e. Finally they should be built from Reusable building blocks that can be realized by modular architectures with swappable components\, data provena nce systems and rich metadata.\nGeneral building blocks for climate resili ence information systems\nA particular focus will be the "roocs" (Remote O perations on Climate Simulations) project\, a set of tools and services to provide "data-aware" processing of ESGF (Earth System Grid Federation) an d other standards-compliant climate datasets from modelling initiatives su ch as CMIP6 and CORDEX. One example is ‘Rook’ an implementation of the OGC Web Processing service (WPS) standard\, that enables remote operation s\, such as spatio-temporal subsetting\, on climate model data. It exposes all the operations available in the ‘daops’ library based on Xarray. Finch is a WPS-based service for remote climate index calculations\, also used for the analytics of ClimateData.ca\, that dynamically wraps Xclim\, a Python-based high-performance distributed climate index library. Finch a utomatically builds catalogues of available climate indicators\, fetches d ata using “lazy”-loading\, and manages asynchronous requests with Guni corn and Dask. Raven-WPS provides parallel web access to a dynamically-con figurable ‘RAVEN’ hydrological modelling framework with numerous pre-c onfigured hydrological models (GR4J-CN\, HBV-EC\, HMETS\, MOHYSE) and terr ain-based analyses. Coupling GeoServer-housed terrain datasets with climat e datasets\, RAVEN can perform analyses such as hydrological forecasting w ithout requirements of local access to data\, installation of binaries\, o r local computation.\n\nThe EO Exploitation Platform Common Architecture ( EOEPCA) describes an app-to-the-data paradigm where users select\, deploy and run application workflows on remote platforms where the data resides. Following OGC Best Practices for EO Application Packages\, Weaver executes workflows that chain together various applications and WPS inputs/outputs . It can also deploy near-to-data applications using Common Workflow Langu age (CWL) application definitions. Weaver was developed especially with cl imate services use cases in mind.\n\nCase of AI for extreme events investi gations\nHere we present challenges and preliminary prototypes for service s which are based on OGC API standards for processing (https://ogcapi.ogc. org/processes/) and implementation of Artificial Intelligence (AI) solutio ns. We will presenting blueprints on how AI-based scientific workflows can be ingested into climate resilience information systems to enhance climat e services related to extreme weather and impact events. The importance of API standards will be pointed out to ensure reliable data processing in f ederated spatial data infrastructures. Examples will be taken from the EU Horizon2020 Climate Intelligence (CLINT\; https://climateintelligence.eu/) project\, where extreme events components could optionally be deployed in C3S. Within this project\, appropriate technical services will be develop ed as building blocks ready to deploy into digital data infrastructures li ke C3S but also European Science Cloud\, or the DIAS. This deployment flex ibility results out of the standard compliance and FAIR principles. In par ticular\, a service employing state-of-the-art deep learning based inpaint ing technology to reconstruct missing climate information of global temper ature patterns will be developed. This OGC-standard based web processing s ervice (WPS) will be used as a prototype and extended in the future to oth er climate variables. Developments focus on heatwaves and warm nights\, ex treme droughts\, tropical cyclones and compound and concurrent events\, in cluding their impacts\, whilst the concepts are targeting generalized oppo rtunities to transfer any kind of scientific workflow to a technical servi ce underpinning scientific climate service. The blueprints take into accou nt how to chain the data processing from data search and fetch\, event ind ex definition and detection as well as identifying the drivers responsible for the intensity of the extreme event to construct storylines. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Deployment of AI-enhanced services in climate resilience informatio n systems - Nils Hempelmann\, Carsten Ehbrecht URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/YWMWFK/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-VUQSVM@talks.osgeo.org DTSTART;TZID=CET:20220826T123000 DTEND;TZID=CET:20220826T130000 DESCRIPTION:Generalization is one of the fundamentals of scientific researc h. In the context of spatial information\, generalization needs to allow f or finding common properties but also for spatial contiguity. Therefore\, such generalization is often made through regionalization - partitioning o f space into spatial clusters or regions. This process is vital for enviro nmental studies\, where many patterns and processes are autocorrelated spa tially. Examples of regionalizations include delineation of ecoregions\, d etection of homogeneous zones for precision agriculture\, definition of cl imate regions\, and so on.\n\nTraditionally spatial generalization was per formed manually\, often based on a compilation of pre-existing\, independe ntly conducted studies. This approach lack of quantitative framework\, and thus no systematic checks\, modifications or objective updates are possib le. Currently\, the abundance of remote sensing spatial data\, such as sat ellite imagery\, gridded climate data\, or land cover maps\, allows fast e xtraction of relevant spatial information on regional and global scales\, making possible studies rooted in a clear quantitative framework.\n\nSuch data\, however\, still requires spatially-aware generalization to formulat e general concepts or claims. Remote sensing data stores information as a set of raster cells\, where a single cell is unaware of its spatial contex t. This is often not enough to understand underlying objects or processes. \n\n(Geographic) object-based image analysis (OBIA) (Blaschke 2010) is fre quently applied to resolve this issue. It is an approach to partition spac e consisting of raster cells into homogeneous objects and thus make spatia l regionalization possible. Several generalization techniques were develop ed for OBIA\, including a superpixels approach that proved to perform best for image processing and remote sensing data analysis (Csillik 2017).\n\n The main idea of superpixels is to create connected groupings of cells wit h similar values (Ren and Malik 2003\; Achanta et al. 2012). Each superpix el represents a desired level of homogeneity while at the same time mainta ining spatial structures. Superpixels also carry more information than eac h cell alone\, and thus they can speed up the subsequent processing effort s (Ren and Malik 2003\; Achanta et al. 2012).\n\nThe original superpixels algorithm has\, however\, two major drawbacks for spatial data problems ot her than RGB images. Firstly\, the algorithm uses the Euclidean distance\, which is adequate in many cases\, such as RGB images. However\, it limits the possible usability for environmental datasets – Euclidean distance is not suitable for many types of spatial raster data (e.g.\, categorical rasters) and has undesirable properties for multi-dimensional data (e.g.\, a set of monthly climate data)\, where the results based on Euclidean dis tance contradict human intuition (Aggarwal\, Hinneburg\, and Keim 2001). Secondly\, the superpixels technique does not result in regions per se but rather over-segmentation – some spatial objects/regions could be repres ented by one superpixel\, while others could consist of many very similar superpixels. \n\nOur preliminary results presented during the GIScience 20 21 conference (Nowosad and Stepinski 2021) provide a basis for using other distance measures to create superpixels. The proposed extension can also be used for various scenarios\, such as creating regions of similar multi- dimensional spatial and temporal patterns or similarly ranked areas. The e xtension is also already available as an open-source software in the form of an R package. The supercells package has extensive documentation in the form of a help file and additional vignettes that can be found\, together with its installation instructions\, at https://jakubnowosad.com/supercel ls/.\n\nThe second issue is\, however\, still not resolved. Many clusterin g methods exist that could be used for merging similar connected superpixe ls\, including traditional ones such as hierarchical clustering and spatia l-aware ones such as SKATER or REDCAP. Wang et al. (2018) developed a REDC AP-based workflow for merging superpixels\, which showed good image result s and outperformed similar techniques\; however\, their work was based on the original superpixels algorithm and thus used Euclidean distance on 3-d imensional RGB images only. Additionally\, it could be worth testing how g ood modern unsupervised machine learning techniques would perform in this task.\n\nOur main goal is to present the work in progress related to devel oping a robust method for merging superpixels and thus creating high-quali ty regionalization. We will test clustering/grouping methods based on thre e main criteria: accuracy\, universality\, and computational performance. Accuracy will be obtained based on the resulting regions’ internal homog eneity and their isolation compared to the neighbors. Universality will be tested on several datasets to check if the method works for various scena rios\, including RGB images\, categorical rasters\, spatial time-series\, etc. The computational performance will be evaluated based on the time nee ded for each method’s calculation and their use of computer resources.\n \nReferences\n\nAchanta\, R.\, A. Shaji\, et al. 2012. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods.” IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11): 2274–82. https://doi. org/f39g5f.\n\nAggarwal\, Charu C.\, Alexander Hinneburg\, et al. 2001. “On the Surprising Behavior of Distance Metrics in High Dimensional Spac e.” In Database Theory — ICDT 2001\, edited by Jan Van den Bussche and Victor Vianu\, 1973:420–34. Lecture Notes in Computer Science. Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-44503-X_27.\n\nBlaschke\ , T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Jou rnal of Photogrammetry and Remote Sensing 65 (1): 2–16. https://doi.org/ d4ksqf.\n\nCsillik\, Ovidiu. 2017. “Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels.” Re mote Sensing 9 (3): 243. https://doi.org/f92zgd.\n\nNowosad\, J.\, and T. Stepinski. 2021. “Generalizing the Simple Linear Iterative Clustering (S LIC) Superpixels.” GIScience 2021 Short Paper Proceedings. 11th Internat ional Conference on Geographic Information Science. September 27-30 2021. Poznań: Poland (Online). https://doi.org/gnw982.\n\nRen\, and Malik. 2003 . “Learning a Classification Model for Segmentation.” In Proceedings N inth IEEE International Conference on Computer Vision\, 10–17 vol.1. Nic e\, France: IEEE. https://doi.org/c6s237.\n\nWang\, Mi\, Zhipeng Dong\, et al. 2018. “Optimal Segmentation of High-Resolution Remote Sensing Image by Combining Superpixels With the Minimum Spanning Tree.” IEEE Transact ions on Geoscience and Remote Sensing 56 (1): 228–38. https://doi.org/gc t8gv. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:A method for universal superpixels-based regionalization (prelimina ry results) - Jakub Nowosad\, Mateusz Iwicki URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/VUQSVM/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-QBP83H@talks.osgeo.org DTSTART;TZID=CET:20220826T141500 DTEND;TZID=CET:20220826T144500 DESCRIPTION:Urban sprawl is associated with negative environmental impacts such as the loss of habitat and the loss of most fertile soils for agric ulture. The hinterland of Cologne\, Germany is facing these challenges. Th e area is expected to face a population increase by 200\,000 inhabitants i n the next twenty years. Given past development trends\, this population i ncrease will have to be mainly absorbed by the cities and villages in the hinterland. While this provides ample economic opportunities\, negative im pacts on ecosystems as well as on agriculture have to be assumed due to ur ban sprawl and increasing fragmentation. The region is known as as one of the most productive agricultural regions in Central Europe. As highest fer tile soils are located in the direct neighborhood of existing settlements\ , urban sprawl will lead to strong trade-offs with agricultural production .\nThe aim of the scientific project NACHWUCHS is to identify alternatives to the continuation of existing development patterns. Therefore\, we deve loped a baseline land use model and compare it to scenarios that assume di fferent brownfield development activities. Stakeholder involvement is at t he core of the project\, as policies for alternative pathways cannot be su ccessfully implemented without the support by farmers\, real estate compan ies\, environmental stakeholder \, the municipalities and the district adm inistration. The most important aspect of land use change in the region is the allocation of new housing areas. This is modeled by a tool-chain base d on a free software stack\, that uses PostgresSQL with a Postgis extentio n\, Python and QGIS. The allocation model for new housing areas is current ly based on a random forest classifier that has been trained on the offici al governmental ATKIS vector land use data set. The predictors of the mode l included distance to public transport and social infrastructure as well as existing land use development plans. The allocation of new housing area s was limited to areas outside of protected areas. Furthermore\, only a fe w land use classes – mainly agriculture – were allowed for the allocat ion of new housing areas. The distance-based predictors were calculated by the openrouteservice\, which uses OpenStreetMap data to build the routing graph and to assign routing weights.\nA 100 by 100m vector grid was used for model training and prediction. Model performance was evaluated based on a split in test and training data that considered spatial relationships . Based on the suitability of the grid cells the demand for projected new housing areas was allocated. We used nine scenarios that differed in the building density for new housing areas as well as by the extent of brownf ield development . In the study presented\, building density is expressed in residential units per hectare. Residential units per hectare is simpli fied as the number of flats in a building. In the simulated scenarios\, t hree density classes (10\, 30 and 50 residential units per hectare) and th ree different proportions of brownfield development (10\, 20 and 40 per ce nt) were combined. In the simulated period from 2018 to 2040\, we had an a rea increase of more than fifty percent between the scenario with the lowe st density and the lowest proportion of brownfield development and the sce nario with the highest density and the highest proportion of brownfield de velopment . The results of the allocation procedure was evaluated based on a set of indicators which cover environmental\, agricultural and social a spects. Examples are the supply of agriculture related ecosystem services\ , soil fertility\, economic value of agricultural production and hemeroby. We used the Open Data of the State of North Rhine-Westphalia\, which conta ined geodata for the relevant domains economy\, environment and nature co nservation\, agriculture\, social affairs and transport. The data are Insp ire-compliant and available under a free licence (DL-DE->Zero-2.0) . T he data set further allowed the evaluation of the model results with rega rd to the consequences of the flood disaster of the 14th July 2021\, which severely affected parts of the hinterland of Cologne. \nOur results will be used in the context of a mission statement for the future regional dev elopment\, developed together with locals stakeholders. The mission statem ent defined development goals for four sub-regions derived by socio-econo mic and environmental properties based on 17 UN SDGs. With the help of the above-mentioned indicators\, we will evaluate how close or how far the re sults of the different scenarios are to these goals and assist local sta keholders\, e.g. in the search for locations of new residential areas. A t ransfer of the model to regions with similar settings is possible as long as suitable data is available for retraining the model and for the estimat ion of the indicator sets\, highlighting again the importance of open data . The ATKIS data used is openly available for some of the federal states o f Germany but not beyond. For North-Rhine Westphalia a transfer semms reas onable- Test runs based on the CORINE land use / land cover product lead t o comparable results\, indicating that this might be a suitable replacemen t for the ATKIS based land use information.The Python code of the model\, the necessary scripts to generate the required postgisdatabase\, a QGIS pr oject example for the visualisation of the results as well as a set of tra ining and test data are provided under free licence via a Gitlab repositor y. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:How to grow? -Modeling land use change to develop sustainable pathw ays for settlement growth in the hinterland of Cologne\, Germany - Mirko B linn URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/QBP83H/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-P8PLUS@talks.osgeo.org DTSTART;TZID=CET:20220826T144500 DTEND;TZID=CET:20220826T151500 DESCRIPTION:Slope stability is strongly influenced by soil hydraulic condit ions\, affected by the meteoric events to which the site is subject. With particular reference to shallow landslides triggered by rainfalls\, the st ability conditions can be influenced by the propagation of the saturation front inside the unsaturated zone. The soil shear strength varies in the v adose zone depending on the type of soil and the variations of soil moistu re. In general\, monitoring of the unsaturated zone can be done by measur ing suction and/or water content. \n\nThe measurement of the volumetric wa ter content can be performed using low-cost instrumentation\, such as the Waterscout SM100 capacitive sensors (Spectrum Tec.)\, distributed over the study areas. Such sensors provide data in near-real time and are relative ly easy to install and replace. However\, it is essential to perform a sit e-specific calibration of the instrumentation\, since previous work (Bovol enta et al. 2020) has shown that the factory settings lead to a general ov erestimation of the actual volumetric soil water content. Therefore\, foll owing a sampling of the analyzed soil and a specific laboratory procedure\ , it is necessary to define the calibration curve that allows the transiti on from raw data\, meant as the ratio between sensor output voltage and in put voltage\, to soil water content. \n\nThen\, the knowledge of soil wate r content allows the estimation of the suction parameter\, thanks to a Wat er Retention Curve (WRC)\, and consequently the definition of the soil she ar strength in partly saturated conditions. \n\nSeveral methodologies for landslide susceptibility assessment\, based on global Limit Equilibrium ( LEM) or Finite Element (FEM) methods\, need the soil shear strength descri ption in order to evaluate the slope stability conditions. Both in the rec ent literature (Escobar-Wolf et al. 2020\, Moresi et al. 2020) and in the GRASS GIS software (r.shalstab)\, models are already proposed for shallow landslide susceptibility estimation in GIS\, based mainly on LEM. However\ , these models do not usually consider the unsaturated soil behaviour\, bu t at most take into account the strength contribution provided by the vege tation root systems. \n\nThe present contribution describes the implementa tion of an automatic procedure in GRASS GIS that\, starting from monitorin g data related to the soil volumetric water content\, provides a 3D descri ption of the soil shear strength in the vadose zone\, that is essential fo r the subsequent landslide susceptibility assessment\, especially in the c ase of shallow landslides.\n\nSoil moisture sensors data come from five mo nitoring networks that were set up between 2019 and 2021 in the framework of the Interreg Alcotra AD-VITAM project. Each network was organized into measurement nodes (from three to five) instrumented with four soil moistur e sensors each and communicating via radio with a receiver. The receiver w as then connected to a modem for remote data transmission. The four sensor s in each node have been placed in the soil at four different depths (-15\ , -35\, -55\, -85 cm from the ground level). The monitoring systems allow to obtain data with a minimum frequency of 5 minutes\, in .csv format so t hat can feed a geodatabase.\n\nStarting from a properly storing of data re corded by the monitoring network in a geodatabase\, at the moment within G RASS GIS but in the near future in PostGIS\, the equation of the site-spec ific sensor calibration\, defined in laboratory\, and the equation of the WRC are implemented in a procedure that allows to pass automatically from the raw sensor data to the soil water content\, and then to the evaluate t he suction parameter. Hence\, the soil strength can be estimated for each depth at which a soil moisture sensor is installed. Moreover\, since the s tudy area is often in the order of few square kilometers\, the information must be spatialized over the entire area of interest\, through appropriat e techniques of interpolation and extrapolation. \n\nThis procedure could be integrated into a LEM or FEM\, including the above cited\, taking advan tage of the soil moisture measurements to improve the evaluation of the st ability conditions over time\, by analysing the evolution of the saturatio n front according to the weather conditions. \n\nThe authors\, in particul ar\, will integrate it into a system called LAMP (LAndslide Monitoring and Predicting)\, which has been under development for several years through the implementation in a GIS environment of an Integrated Hydrological-Geot echnical (IHG) 3D model for the assessment of landslide risk triggered by measured or forecasted precipitation. The integration of this procedure in LAMP will allow to obtain a simple but effective modelling for the assess ment of susceptibility to shallow landslides\, too.\n\nNote that the contr ibution in the landslide risk management of the present procedure could be important even in the days following the rainfall event of interest\, pro viding the technical staff in charge of territorial protection with a usef ul tool for the landslide susceptibility assessment\, especially in the ca se of shallow landslides.\n\nIn order to allow the scientific community to evaluate the usefulness of the proposed procedure and consequently to hav e the possibility to implement it in the above-mentioned methods (LEM-FEM) improving the assessment of landslide susceptibility\, soil moisture data at a specific site\, related to significant rainfall events\, and the imp lemented procedure will be openly shared\, once the testing phase is compl eted. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Landslide susceptibility assessment: soil moisture monitoring data processed by an automatic procedure in GIS for 3D description of the soil shear strength - Bianca Federici\, Stefania Viaggio URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/P8PLUS/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-QGSJLX@talks.osgeo.org DTSTART;TZID=CET:20220826T151500 DTEND;TZID=CET:20220826T154500 DESCRIPTION:The geomatic strategy for the survey campaign\, data processing and product fruition in an archaeological context is presented and discus sed. The case study is the Domus V situated in the Archaeological Park of Pompeii (Regio VII\, Insula 14)\, which was surveyed in September 2020 by the Geomatics Laboratory of Genoa University in collaboration with the arc haeologist group of the same University\, under the ministerial concession DG 553 Class 34.31.07/246.7 of 26 January 2016 and its renewal on 9 April 2019 (34.31.07/3.4.7/2018). \nThe survey campaign involved the following integrated geomatic techniques:\n- UAV photogrammetry\, performed with DJI Mavic 2 Pro. The shooting geometry was nadiral with two different altitud es of 40 m and 15 m. An additional survey with a tilting angle of 45° at a flight altitude of 15 m was performed along concentric paths around the site. The UAV dataset is composed of 1400 images. The photogrammetric surv eys are framed thanks to temporary Ground Control Points (GCPs)\, surveyed with GNSS in Network Real Time Kinematic (NRTK) positioning strategy. \n- Terrestrial photogrammetry\, 7000 images of the internal vertical walls w ere taken with a Canon Eos 40D camera at a shooting distance of about 2 m following a bottom-to-top trajectory. \n- Terrestrial laser scanning\, usi ng the Z+F 5006h phase difference instrument.\n\nThe integrated survey all owed to move from a general view of the entire site to an increasingly det ailed one\, mainly aimed at the vertical walls\, thanks to the global fram ing provided by the UAV survey.\nThe UAV and terrestrial photogrammetry ca mpaigns were processed through the open-source software MicMac [1] to crea te the dense point clouds\, and CloudCompare [2] to align the different bl ocks. \nMicMac was chosen for its open-sourceness and its rigorousness in the photogrammetric processing\, both related to the estimation of the ext ernal/internal orientation parameters and the dense matching to obtain the 3D point clouds from the images\, that is based on a multi-scale\, multi- resolution pyramidal approach that minimizes the outliers and the noise. \ nDue to the not linear computational time in respect of the number of imag es\, the MicMac processing was split in blocks of 500 images each (about 2 4 hours of processing time)\, with 100 overlapping images between two cons ecutive blocks\, to align them through a point-to-point strategy. The obta ined 3D point cloud was oriented and scaled using 15 natural points found on the terrestrial laser scanner point cloud\, obtaining deviations on poi nts positions ranging between 1 and 2 cm. The quality of the alignment was tested computing the distance between the laser scanner and the photogram metric point clouds using CloudCompare M3C2 algorithm [3] on a representat ive area of 1.60 m × 2.25 m of the fresco on the central wall of the surv eyed room\, obtaining distances of ± 5 mm orthogonally to the wall.\nMore over\, the software MAGO [4]\, developed in C++ environment within the Geo matics Laboratory\, was used to produce high-resolution orthophotos of ver tical walls. MAGO exploits a step-by-step self-adaptive mesh that fits the dense point clouds considering a triangular plane area\, where the image pixel is projected at its original resolution via the collinearity equatio ns. The needed inputs are the image(s) to be orthorectified\, the external and internal orientation parameters\, the user-defined orthophoto plane a nd the output orthophoto resolution. MAGO was recently updated to generate orthophotos of non-coplanar adjacent walls\, i.e.\, forming an edge betwe en them\, through a rotation so that the two walls are in a continuous com mon plane.\nThe orthophotos were made accessible and viewable via a QGIS [ 5] project built so to manage two different reference frames\, i.e\, the t raditional planimetric plane (X\,Y) and the vertical plane of the walls (X -Y\,Z)\, where the X-Y represent the planimetric coordinates along the wal l direction. This allows to introduce the third dimension in the typical G IS representation\, thus realizing a 3D GIS environment. The QGIS project is organized with a “master-slave” architecture\, where the master pro ject is dedicated to the (X\,Y) plane and reports the vectorial geometries (lines) representing the perimeter of the walls\, whereas a different sla ve project is dedicated to each specific wall with the corresponding ortho photo in a (X-Y\,Z) plane. Each slave project is connected to the master t hanks to a QGIS action that opens it when clicking on the corresponding wa ll in the master project. In each sub-project\, the orthophoto of the wall is displayed together with three default shapefiles: point\, line and pol ygon shapefile\, respectively. The attribute tables of the three shapefile s are set to automatically be updated with the following information once the user introduces a new geometry:\n- point shapefile: the image coordina tes (x\, y) in pixel units and in the corresponding object coordinates (E\ , N\, Z)\, where E and N represent the east and north coordinates in ETRF2 000-2008.0/UTM33N reference frame and Z is the height of the point on the wall\;\n- line shapefile: length of the drawn line in meters\;\n- polygon shapefile: length of the perimeter and polygon surface\, in meters and squ are meters\, respectively.\n\nAn additional feature of the QGIS project is the possibility of performing the orthophoto classification based on the state of conservation of the wall\, i.e.\, crumbling\, degraded\, good con ditions\, preserved\, through user-defined training areas\, from which the spectral signatures to be used in the supervised classification are compu ted.\nThanks to this "nested GIS" environment\, the ensemble of the produc ed orthophotos can be viewed and linked to the corresponding geometry\, fo rming a catalogue for an overall analysis of the entire archaeological sit e\, taking advantage of an increasingly detailed and precise zooming in th e areas of interest. This environment can also be used by non-expert geoma tics users\, making the survey products available for analysis in differen t specific disciplines. DTSTAMP:20260914T082223Z LOCATION:Room Modulo 3 SUMMARY:Photogrammetric processing and fruition of products in open-source environment applied to the case study of the Archaeological Park of Pompei i - Ilaria Ferrando URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/QGSJLX/ END:VEVENT END:VCALENDAR