BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org// BEGIN:VTIMEZONE TZID:-03 BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:-03 TZOFFSETFROM:-0300 TZOFFSETTO:-0300 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-RVNL7M@talks.osgeo.org DTSTART;TZID=-03:20241204T103000 DTEND;TZID=-03:20241204T110000 DESCRIPTION:# Poster Session I\n\n## Analyzing Randomness in Point Patterns : An Algorithmic Approach\n\n- Tony Sampaio\, Earth Science Department\, D epartment of Geography\, Federal University of Paraná\, Brazil\; Spatial Pattern Analysis and Thematic Cartography Lab\, Federal University of Para ná\, Brazil\n- Cláudia M. Viana\, Centre of Geographical Studies\, Insti tute of Geography and Spatial Planning\, University of Lisbon\, Portugal\; Associate Laboratory Terra\, Lisbon\, Portugal\n- Eduardo Gomes\, Centre of Geographical Studies\, Institute of Geography and Spatial Planning\, Un iversity of Lisbon\, Portugal\; Associate Laboratory Terra\, Lisbon\, Port ugal\n- Silvana Camboin\, Geodetic Science Graduate Program\, Department G eomatics\, Federal University of Paraná\, Brazil\; Open Geospatial Lab\, Federal University of Paraná\, Brazil\n- Fábio Breunig\, Earth Science D epartment\, Department of Geography\, Federal University of Paraná\, Braz il\; Spatial Pattern Analysis and Thematic Cartography Lab\, Federal Unive rsity of Paraná\, Brazil\n- Edenilson Nascimento\, Earth Science Departme nt\, Department of Geography\, Federal University of Paraná\, Brazil\; Sp atial Pattern Analysis and Thematic Cartography Lab\, Federal University o f Paraná\, Brazil\n- Elaine de Cacia de Lima Frick\, Earth Science Depart ment\, Department of Geography\, Federal University of Paraná\, Brazil\; Spatial Pattern Analysis and Thematic Cartography Lab\, Federal University of Paraná\, Brazil\; Geography Teaching Laboratory\, Federal University of Paraná\, Brazil\n- Jorge Rocha\, Centre of Geographical Studies\, Inst itute of Geography and Spatial Planning\, University of Lisbon\, Portugal\ ; Associate Laboratory Terra\, Lisbon\, Portugal\n\n## Performance Benchma rking for Resource Allocation Optimization in GeoNode Ecosystems on Kubern etes Clouds\n\n- Marcel Wallschläger\, Leibniz Centre for Agricultural La ndscape Research (ZALF)\, Müncheberg\, Germany\n- Igo Silva de Almeida\, Leibniz Centre for Agricultural Landscape Research (ZALF)\, Müncheberg\, Germany\n- Xenia Specka\, Leibniz Centre for Agricultural Landscape Resear ch (ZALF)\, Müncheberg\, Germany\n\n## Applying Spatio-Temporal Analysis for Data Mining on Shooting Data\n\n- Felipe Sodré Mendes Barros\, Facult ad de Ciencias Forestales\, Universidad Nacional de Misiones\, Argentina\n - Terine Husek Coelho\, Instituto Fogo Cruzado\, Rio de Janeiro\, Brazil\n - Iris Rosa\, Instituto Fogo Cruzado\, Rio de Janeiro\, Brazil\n- Davi San tos\, Instituto Fogo Cruzado\, Rio de Janeiro\, Brazil DTSTAMP:20260422T231510Z LOCATION:Main Hall SUMMARY:Poster Session I - URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/RVNL7M/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-VHU3TT@talks.osgeo.org DTSTART;TZID=-03:20241204T140000 DTEND;TZID=-03:20241204T143000 DESCRIPTION:Standards in the Geographic Information Systems (GIS) domain ar e crucial for ensuring interoperability\, data consistency\, and efficienc y across diverse applications and platforms. As in other domains\, they ar e necessary to ensure that different GIS software can work together. Moreo ver\, the continuous improvement and development of these standards are es sential to keep pace with evolving technologies and user requirements\, en hancing the overall functionality and usability of GIS. By adhering to and advancing these standards\, the GIS community can foster innovation\, sup port informed decision-making\, and address complex geospatial challenges more effectively.\nTherefore it’s important to be conservative\, using w idely supported standards\, but also open to emerging technologies\, prepa ring for the future global leap\, and welcoming it proactively. The design of the eMOTIONAL Cities Spatial data infrastructure (SDI) is built around this approach (Simoes\, J.\, and Cerciello\, A. (2022). Serving Geospat ial Data Using Modern and Legacy Standards: a Case Study from the Urban He alth Domain. The International Archives of Photogrammetry\, Remote Sensing and Spatial Information Sciences\, 48\, 419-425)\n\nThe environment we li ve in affects our mental health and well-being. ​​The eMOTIONAL Cities project has set out to understand how the natural and built environment c an shape the feelings and emotions of those who experience it. It does so with a cross-disciplinary and data driven approach\, which resulted in num erous datasets from more “traditional” GIS based fields like Urban Pla nning\, as well as other fields like Neuroscience. The common denominator between all these datasets is the geospatial dimension. One of the main go als of the project is to assemble these disparate datasets in a common SDI \, in order to enable scientists and eventually the general public\, to di scover and access the data for the purposes of analysis and decision makin g.\nThe OGC API is a family of modern Standards from the Open Geospatial C onsortium (OGC)\, which leverage modern web technologies like OpenAPI\, RE ST and JSON (Percivall\, G. (2017) OGC® Open Geospatial APIs - White Pape r). Although very appealing to web developers\, they are relatively new co mpared to the OGC Web Services (OWS) like WFS\, WMS or WMTS\, which have b een in the GIS domain for more than twenty years. When we started the proj ect\, we were unsure if it would be possible to set up an SDI\, purely bas ed on OGC API\, both because of the maturity of the Standards and the avai lability and Technology Readiness Level (TLR) of implementations. This has led us to initially create an SDI that contains both a modern and legacy stack (Simoes\, J.\, and Cerciello\, A. (2022). Serving Geospatial Data Us ing Modern and Legacy Standards: a Case Study from the Urban Health Domain . The International Archives of Photogrammetry\, Remote Sensing and Spatia l Information Sciences\, 48\, 419-425). However\, in the past two years we have seen huge developments in OGC API Standards\, with many Standards ha ving parts approved and implementations catching up on those developments. One implementation in particular\, pygeoapi (https://pygeoapi.io/)\, is e xemplar in terms of the Standards development process\, by participating a ctively in the OGC Code Sprints\, by being an Early Implementer (EI) and e ven a Reference Implementation (RI) of several OGC API Standards. It embod ies the new OGC paradigm\, where the development of the Standard goes hand in hand with the development of implementations\, resulting in published Standards which are market ready.\nThe eMOTIONAL Cities SDI demonstrates t hat it is now possible to share geospatial data using OGC API with Free an d Open Source Software (FOSS). We have selected Standards to enable the pu blication of feature data (OGC API - Features)\, tiles of geospatial infor mation (OGC API - Tiles)\, sensor data (SensorThings API) and metadata (OG C API - Records). Although that was not the case when we started this work \, they are now all approved Standards. The SDI uses a stack of FOSS softw are\, with pygeoapi at its core and several supporting services. In order to ease the deployment and reproducibility of the system\, the services we re virtualized into docker containers and orchestrated using docker-compos e. This resulted in a system that is infrastructure agnostic and can be de ployed in any Cloud Service Provider (CSP) in a matter of minutes. The cod e is available on GitHub with an MIT license (https://github.com/emotional -cities/openapi-sdi) and released in Zenodo with DOI 10.5281/zenodo.659117 9. We have also set up pipelines to enable both humans and machines to ing est data and metadata into the SDI and extensive documentation about how t o access the SDI\, using clients such as QGIS\, MapStore or jupyter notebo oks.\nThe SDI is live at: http://emotional.byteroad.net/ and it includes 9 7 datasets from five different cities (e.g.: Lisbon\, London\, Copenhagen\ , Tartu and Lansing). It has collections that characterize the physical en vironment (e.g.: Normalized difference vegetation index (NDVI)\, Annual me an NO2 concentration)\, the built environment (e.g.: Buildings with repair needs ratio\, Average age of buildings) socio-economic aspects (e.g.: Are a Deprivation Index\, Number of People Travel by Bicycle to Work) and heal th data (e.g.: Crude percent of adults with depression\, Mortality rate)\, as well as results of experiments (e.g.: London outdoor walk test data: A ir Quality Temperature\, London outdoor walk test data: Sound Pressure lev els). The data can be discovered and queried in the OGC API - Records sear chable catalog: https://emotional.byteroad.net/catalogue\nIn this article we would like to share our journey during the process of implementing the SDI\, and how we navigated the technological and human challenges of adopt ing emerging technologies in constant development. We hope the results of this project can encourage scientists\, urban planners and other experts w ho deal with geospatial data in some way\, to embark on a similar journey and contribute towards making geospatial information FAIR\; e.g.: Findable \, Accessible\, Interoperable and Reusable. At the same time\, we hope to promote a family of GIS standards (e.g.: OGC API) that seeks to mitigate t he learning curve that has always characterized them. DTSTAMP:20260422T231510Z LOCATION:Room V SUMMARY:A Spatial Data Infrastructure using Modern Standards: Lessons Learn ed from the eMOTIONAL Cities Project - Antonio Cerciello\, Joana Simoes URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/VHU3TT/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-9GXCUV@talks.osgeo.org DTSTART;TZID=-03:20241204T140000 DTEND;TZID=-03:20241204T143000 DESCRIPTION:With the increasing presence of spatialized data and informatio n\nin people's daily lives\, the constant need to make data-driven\ndecisi ons\, and the expansion of artificial intelligence\ntechnologies in societ y\, this work seeks a technological solution\nfocused on simplifying geosp atial analyses. The goal is to\ndemocratize access to and understanding of these resources for\ncommon users without the need for advanced knowledge of the\nspecific geographic information tools currently most used.\nTo th is end\, a system was developed that transforms natural\nlanguage question s directly into SQL queries\, specifically using\nPostgreSQL/PostGIS (Li a nd Jagadish\, 2014\; Ramsey\, 2007).\nThis system is based on a chat model built on Gemini\, which\ninterprets user queries and generates the corres ponding SQL\nqueries. The back-end API executes these queries and returns the\nresults\, which are visualized in an intuitive and interactive\ngraph ical interface. This allows for dynamic exploration of\ngeospatial data\, facilitating the analysis and visualization of\ncomplex information withou t the need for advanced technical\nknowledge in SQL. The integration of na tural language\nprocessing (NLP) and geospatial database queries represent s a\nsignificant innovation. This system reduces the learning curve\nassoc iated with traditional GIS tools\, making the technology\naccessible to a broader audience (Craglia et al.\, 2012). By using\nthe Gemini model\, the system can understand and process a wide\nrange of natural language input s\, translating them into precise\nSQL queries that interact with the geos patial database.\nTo demonstrate the system's effectiveness\, a case study was\nconducted using a database composed of 20 tables containing\ndata re leased by the National Water Agency (ANA)\, the Brazilian\nInstitute of Ge ography and Statistics (IBGE)\, and the Energy\nResearch Company (EPE)\, w hich were adjusted for reading by\nthe system. This database includes a da ta dictionary that provides\ndetailed information on what each value repre sents and its\ncorresponding context.\nThe results were evaluated based on the accuracy of answers\ngiven to 192 questions posed within the context of the case study.\nOut of these 192 questions\, 167 answers were correct\ , yielding\nan accuracy rate of 87% in the total evaluated\, allowing deta iled\nvisualization of the geometries and information required by the\nuse r's query in the developed interface. Enhanced accessibility\nfor non-tech nical users is one of the most significant benefits\nidentified in this wo rk\, as there is no need for in-depth technical\nknowledge in spatial data filters\, in addition to the reduced query\ntime and the ability to gener ate valuable insights from these data\nsets.\nThis work also highlights th e importance of an intuitive and\ninteractive graphical interface system. The interface allows the\nvisualization of layers and tables resulting fro m the query\,\nenabling users to dynamically explore\, filter\, and manipu late the\ndata according to their needs\, and obtain insights that would b e\ndifficult to achieve without advanced knowledge of GIS tools (Li\nand W ang\, 2013).\nIt is important to note that this work represents a first st ep in an\ninitiative for this technological solution model implementing\nA rtificial Intelligence\, from which it was possible to identify\nseveral p oints of improvement not only in the model but also in\nthe databases and their construction and acquisition process. The\ncase study demonstrated t hat the system can accurately interpret\nand execute user queries\, provid ing reliable and relevant results.\nWith this approach\, new possibilities are opened for the\nexploration and analysis of geospatial data\, enhanci ng decisionmaking based on information obtained from various areas such\na s environmental monitoring\, urban planning\, and territorial\nplanning. F uture work should focus on improving the system's\ncapabilities\, expandin g its application domains\, and exploring\nnew ways to integrate emerging technologies\, continuing to drive\ninnovation in this critical area. DTSTAMP:20260422T231510Z LOCATION:Room II SUMMARY:Democratizing AI\, making geotechnology accessible to all - Thomaz Franklin de Souza Jorge\, Cauã Guilherme Miranda\, João\, Igor Augusto d a Costa Nunes\, Lucas Alvarenga Lopes\, Gabriel Viterbo URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/9GXCUV/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-3YCWGQ@talks.osgeo.org DTSTART;TZID=-03:20241204T140000 DTEND;TZID=-03:20241204T143000 DESCRIPTION:Tropical forests host half of Earth’s biodiversity (Dirzo & R aven\, 2003)\, 62% of global terrestrial vertebrate species (Pillay et al. \, 2022)\, and play a crucial role as a carbon sink (Mitchard\, 2018). Des pite their importance\, every year\, 3 to 4 million hectares of primary tr opical forests are lost\, mainly in Brazil\, Indonesia\, and the Democrati c Republic of Congo (DRC) (Hansen et al.\, 2013\; Seymour\, 2022)\, contri buting to 22% of total greenhouse gas (GHG) emissions worldwide along with agriculture\, forestry and other land use (AFOLU) (IPCC\, 2023).\n\nPreve nting deforestation requires understanding its root causes\, particularly the capital availability to the farm sector. In many tropical countries\, rural credit is available as loans at subsidized interest rates to improve agricultural production or support agricultural costs (Servo\, 2019). How ever\, these loans may be leading to more deforestation. Some studies have analyzed this issue on a municipal scale\, but few peer-reviewed studies have linked rural credit to individual property-scale deforestation. Recen tly\, the NGO Greenpeace (Greenpeace\, 2024) and the Climate Policy Initia tive (Mourão et al.\, 2024) published two studies showing the relationshi p between rural credit and deforestation. Understanding this relationship can improve public policies to prevent deforestation from happening even b efore it starts.\n\nMethods\nIn this study\, I used open data and FOSS4G t o quantify the amount of Rural Credit released to rural properties that co mmitted Deforestation. The datasets came from different open data sources. The Central Bank of Brazil provides data on rural credit on the SICOR Sys tem. The National Institute of Space Research (INPE) provides data on defo restation in the Terrabrasilis system. The Brazilian Forest Service provid ed data for each property's Rural Environmental Registry (CAR)\, providing their boundaries. The Brazilian Institute of Geography and Statistics (IB GE) provides data for administrative boundaries (state and municipality).\ n\nUsing the Terra library in CRAN-R\,. I processed the data sets from thr ee states that contributed the most to deforestation: Rondônia\, Mato Gro sso\, and Pará. I used a Spatialite database and QGIS Geographic Informat ion System to check the results. The novelty here is that by using R scrip ts\, it was possible to rebuild the relational database from SICOR in a ge ospatial environment\, providing a reproducible environment. All steps are described below.\n\nFirst\, using R\, all the data needed for the analysi s was downloaded from their source and loaded into the R environment. The second step\, still using R\, was to recreate the SICOR\, CAR\, and PRODES Deforestation tables and populate them into a Spatialite (SQLite) databas e. This step provides a valuable tool for monitoring by both environmental agencies and the banks that provide loans for rural credit.\n\nThe next s tep was to intersect the deforestation data with the CAR property boundari es\, calculating the amount of deforestation on each property using PRODES data between 2008 and 2023. Next\, the total number of loans between 2013 and 2023 was identified for each property. All these steps were processed using the Terra library in R.\n\nResults\nIn 1992\, the Brazilian Parliam ent enacted Law 4\,829\, creating subsidies for rural credit\, known as Sa fra Plan. The interest rates of the Safra Plan have always been significan tly lower than those practiced in the market. In March 2019\, for example\ , while the average interest rate on loans for non-rural purposes stood at 31.6% per year\, rural credit was observed at 10.8% p.y. on market rates\ , and even lower with controlled rates observing an average rate of 6.1% p .y.(Servo\, 2019) .\n\nThe results show that from 2013 to 2023\, more than BRL 17 billion was loaned to properties with some deforestation in these three states (RO\, PA\, MT). Counting deforestation from August 2008 to Ju ly 2023\, 8197 km² is the total amount of clearing in properties that rec eived rural credit in those same three states\, representing 8.5% of all d eforestation for the period. DTSTAMP:20260422T231510Z LOCATION:Room I SUMMARY:The relationship between rural credit and deforestation - George Po rto Ferreira URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/3YCWGQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-FPQDTF@talks.osgeo.org DTSTART;TZID=-03:20241204T140000 DTEND;TZID=-03:20241204T143000 DESCRIPTION:Application-oriented research projects often involve diverse co nsortium members\, from universities to research institutions\, to authori ties\, to domain end users. This requires the integration of very heteroge neous data sources\, the facilitation of their combined processing\, and t he presentation of the results in adequate ways. The challenge here is not only the technical realisation itself\, but maybe even more to design sol utions catering to this wide user group\, balancing feature-richness with easy usability. Data is abundant\, and processing plentiful\, but it all n eeds to go the last mile to the final user.\n\nOne such research project i s “AgriSens DEMMIN 4.0”\, which is advancing remote sensing for the di gitalisation in agricultural crop production. Thus\, the user group includ es programmers\, domain scientists\, as well as farmers. Until now\, agric ulture is not yet widely taking advantage of EO products. Therefore\, the project not only addresses the creation of novel remote-sensing-based appl ication techniques and their implementation\, but puts an equally distinct emphasis on the development of an accompanying data integration and visua lisation system. In this work\, we describe how our architecture – consi sting of several pieces of free and open source geo software – closes th e gap between data providers and information consumers as it facilitates n ecessary analysis steps and combines these with adequate presentation to d ecision makers.\n\nIn our IT architecture\, we utilise one central datacub e to conquer this problem\, which acts as a cloud-based geospatial data ho lding and computation platform. It gathers a multitude of data\, ranging f rom optical and radar raster imagery through weather data to in-situ field measurements\, and pre-processes it into an interoperable\, analysis-read y state. These resources can then be accessed through APIs for external us age\, or computations can be carried out directly on the datacube and the results immediately visualised with tools hosted on the same server.\n\nTh e whole system is located at the Leibniz Supercomputing Centre of the Bava rian Academy of Sciences and Humanities (LRZ). Apart from utilising the co mputing resources available there\, this also opens up synergies with alre ady-existing projects: We can make use of the enormous amount of EO data t hat is already available within the LRZ’s “Data Science Storage” and the DLR’s “terrabyte” platform. These storages are directly mounted into our server so that the datacube can access petabytes of imagery with out having to duplicate it again\, saving costs and emissions.\n\nAt the c ore of our infrastructure is an instance of the “Open Data Cube” (ODC) software package. Metadata is ingested into its PostgreSQL database and c an be retrieved via the built-in web-based data discovery application “O DC Explorer” or via an API endpoint of the emergent STAC standard (which in turn can be accessed via the “STAC Browser” web application or any other compatible software). All raster data is provided in the Cloud-Opti mised GeoTIFF (COG) format\, allowing efficient access even from remote ma chines.\n\nOur main interface for scientific computation is Jupyter Hub\, enabling collaborative work across institutions. For each user\, a dedicat ed Jupyter Lab instance is spawned in its own Docker container that can ac cess all the data of the previously mentioned storages and has a certain a mount of computing resources allocated to it. Users can write their code i n Python or R\, arguably the most popular programming languages in the EO community\, which offer straightforward packages for connecting to an ODC\ , namely “datacube” and “odcR”. This way\, scientists receive the typical professional online analysis environment in which they can work cl osely to the data and use all the powers of these programming languages an d their EO-friendly ecosystem.\n\nAnother option to work with the data is via openEO\, the new standardised way to interact with big EO data cloud p rocessing backends. We incorporate this standard by utilising the “openE O Spring Driver”\, an adapter to translate user-submitted openEO process graphs into analysis code that can be run using ODC. For compatibility wi th legacy software\, it is also possible to request rendered images of pre -configured analysis algorithms via WMS\, which are being served by an ins tance of the powerful “datacube-ows” package.\n\nThe final goal\, howe ver\, is to connect farmers and other end users to these results\, who can not or do not want to deal with complex interfaces. Therefore\, these high ly technical tools are not sufficient\, but need to be accompanied by easy -to-use graphical interfaces. Drawing on the possibilities of modern web t echnologies\, we realise these through purpose-built web apps. Arranged ar ound an OpenLayers-powered map component\, data products are streamed in C OG format from the datacube and displayed along with the needed additional tooling for interpretation. For example\, in this fashion we realised a d emonstrator showcasing the results of a water balance model for irrigated potato fields.\n\nAnother outlet of the project is the “FieldMApp”\, a mobile application designed to be used by farmers both in the field as we ll as in the office to digitise and monitor areas of lower yield within cr op fields. For evaluating plant vitality\, a vegetation-index-based raster product is calculated on the datacube using the latest Sentinel-2 imagery and long-term crop-specific averages. Due to the tablet application being programmed with the platform-agnostic Flutter framework\, but its standar d mapping component not yet supporting COGs\, it was necessary to resort t o WMS for serving the raster data. As mentioned above\, this is comfortabl y possible by configuring “datacube-ows” on top of ODC.\n\nOverall\, t he challenge of interoperating various data supplies\, processing chains a nd custom-tailored interfaces – as typically encountered in interdiscipl inary research projects – requires complex solutions\, but can be achiev ed quite well by utilising a datacube approach with free and open source g eo software building blocks. Our integrated system successfully demonstrat es such a use case for the domain of remote sensing in agriculture. DTSTAMP:20260422T231510Z LOCATION:Room III SUMMARY:Integrating\, Processing and Presenting Big Geodata with Earth Obse rvation Datacubes in an Interdisciplinary Research Context - Christoph Fri edrich URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/FPQDTF/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-JWML3M@talks.osgeo.org DTSTART;TZID=-03:20241204T143000 DTEND;TZID=-03:20241204T150000 DESCRIPTION:3D modeling involves the three-dimensional representation of ch aracters or scenes\, providing a greater visualization of details for the object being represented\, creating the concept of depth. This concept als o opens up a vast array of applications that a simple 2D drawing would be unable to present. This type of representation is widely used in various f ields\, such as the entertainment industry (e.g.\, films and games)\, auto motive engineering\, architecture/engineering\, etc.\, having diverse purp oses and applications. 3D modeling can be achieved through different metho dologies\, with the primary distinction between them being the intended us e of the modeled object. Notable methodologies include Box Modeling\, Digi tal Sculpting\, and Poly-by-Poly modeling.\nTraditionally\, Photogrammetry was defined as the 'science and art of obtaining reliable measurements th rough photographs' (American Society of Photogrammetry). Although it is a powerful technique for environmental detail formation\, there is inherent complexity in its equipment\, including both hardware and software\, with a significant cost associated with its acquisition and the necessity for s pecialized knowledge for effective use by its operators. However\, with te chnological advancements\, the availability of higher processing capacity equipment at lower costs and more user-friendly software has facilitated w ider dissemination and use among a larger number of users. Among these tec hnologies\, the introduction of drones into the technical and professional market\, with new forms of application and use of photography\, has spurr ed new growth in the use of photogrammetry across various professional sec tors and the development of techniques for processing small-format images. \nThus\, 3D modeling through photogrammetry allows for the acquisition of large-scale data\, enabling detailed studies\, sometimes at centimeter or even millimeter scales\, with a level of detail that would previously have been impossible or impractical. This contributes positively to feasibilit y studies\, risk analysis\, project presentation\, among other application s in various fields such as Civil Engineering\, Architecture\, and Surveyi ng. The combination of these technologies not only enhances project visual ization but also amplifies the collection of geospatial data\, promoting a more comprehensive and precise approach.\nVirtual Reality (VR) is a techn ology that creates a simulated environment through electronic devices\, th ereby providing users with a new way of visualization\, whether applied to video games or integrated with other fields. The combination of this tech nology with models obtained from photogrammetry provides realistic environ ments with impressive detail richness.\nThis study will address 3D modelin g through the close-range technique\, using terrestrial photographs. The r esearch is divided into four stages. The first three stages involve proces sing these images using various types of hardware and software. For the ha rdware\, both processing power and image capture quality are considered\, aiming to demonstrate the best possible results at a low cost. Regarding s oftware\, the use of open-source programs from various developers was expl ored\, with the intention of making comparisons and achieving the best res ults among them.\nIn the fourth and final stage of the research\, a market evaluation will be conducted to understand the needs of professionals in the field concerning this technology. To carry out the work\, photographs were initially taken with a Canon EOS 200D of the building housing the Gra duate Program in Modeling (PPGM) at the State University of Feira de Santa na (UEFS). Subsequently\, additional images were obtained using other capt ure equipment\, such as mobile phones\, following the same research line. A total of 100 photos were collected and processed using three open-source software programs: Meshroom\, Colmap\, and Regard3D\, which are noted for their prominence and positive recommendations among free software options .\nThe goal was to calibrate parameters to achieve the best possible model \, considering software and hardware limitations. With the obtained result s\, a comparison was made to determine which software offered the best out come\, combining modeling quality\, ease of post-processing\, and compatib ility with the graphics engine (Engine) that will be used for creating the realistic environment. This engine is called Unreal Engine\, developed by Epic Games\, widely used in video game development but with significant p otential for application in fields such as Civil Engineering\, Architectur e\, and Surveying.\nThus\, the research could delve into the combination o f modeling obtained from photogrammetry with virtual reality. One of the s oftware programs used\, which demonstrated good performance\, was Regard3D \, designed for creating 3D models from two-dimensional images. A machine with low processing hardware was used specifically to compare these result s with those from more modern computers. The configuration used is as foll ows:\n• Processor: Intel Pentium G620\n• Motherboard: DXH61Z M2 Duex\n • Graphics Card: RX580 8GB MingZhou\n• RAM: 16GB\nParameter selection was carried out iteratively in successive stages to improve processing qua lity. It was observed that processing times were high\, particularly in sp ecific stages such as mesh computation. During this process\, the software analyzes the provided images to find correspondences\, known as interest points\, which are distinct and uniquely characterized points in the image s. For the various software programs\, the most commonly used algorithms f or point detection are SIFT (Scale-Invariant Feature Transform) (Lowe\, 19 99) and ORB (Oriented FAST and Rotated BRIEF) (Ethan Rublee et al.\, 2011) \, describing them as characteristic vectors for finding common points bet ween images. After detection\, filtering is performed to discard points th at are misaligned relative to others.\nAdditionally\, significant time was observed in the densification process\, which involves increasing the num ber of points in a 3D model to add more interest points for better image q uality. Various techniques are used in this process\, with interpolation b eing particularly noteworthy\, as it uses the characteristics of nearby po ints to estimate geometry and generate additional points.\nUsing minimal p arameters\, the model generation time was extended on this computer config uration\, with high RAM usage. Significant storage was required\, as the m odels increased in size at each processing stage\, reaching approximately 20GB in the final stage. However\, the results were satisfactory compared to the available paid software on the market. Therefore\, the use of photo grammetry-generated models\, combined with virtual reality to create reali stic virtual environments\, can be considered positive\, with low cost and using free open-source software. DTSTAMP:20260422T231510Z LOCATION:Room III SUMMARY:Photogrammetry and 3D Modelling Applied to the Creation of Virtual Reality in Realistic Environments: Analysis of Free Software for Image Pro cessing - Felipe Oliveira Silva\, Rosangela Leal URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/JWML3M/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-8BGPQA@talks.osgeo.org DTSTART;TZID=-03:20241204T143000 DTEND;TZID=-03:20241204T150000 DESCRIPTION:The field of speleology is dependent upon the accurate mapping and analysis of data to gain an understanding of subterranean environments . Free and open-source software (FOSS) has facilitated advancements in spa tial data management\, offering robust tools for data collection\, analysi s\, and fieldwork. Software solutions such as PostgreSQL with PostGIS\, QG IS\, GRASS\, and QField facilitate efficient geospatial data management an d data collection. The accurate location determination of caves is of para mount importance\, given their significant ecological\, historical\, and c ultural value. In Brazil\, the implementation of rigorous environmental le gislation has resulted in the establishment of a 250-meter buffer zone sur rounding caves. This regulatory measure is designed to ensure the protecti on of these vulnerable ecosystems and to regulate activities within their vicinity. The radius may be modified based on the findings of environmenta l studies\, thereby ensuring the preservation of caves while facilitating socio-economic development. This study presents EspeleoVale\, a software a s a service (SaaS) solution hosted on AWS. It employs open source scriptin g languages and frameworks\, including AngularJS\, PostgreSQL with PostGIS \, and MapStore2 integrated with Geoserver\, to effectively manage and vis ualize speleological data for a mining company in Brazil. By employing SQL queries and spatial functions\, users can visualize cave locations and th eir restricted areas\, thereby facilitating the assessment of project impa cts on these geomorphological features. In this study\, examples of visual ization and spatial analysis are presented for five hypothetical caves and a hypothetical project\, returning intersections\, differences and merged areas\, which are vital for the environmental protection of cavities and for knowing project restrictions. Thus\, the integration of an RDBMS for s patial analysis and FOSS tools for data visualization fosters new developm ents and promotes more efficient speleology data management. DTSTAMP:20260422T231510Z LOCATION:Room I SUMMARY:From cave buffer zones to protected areas: speleology data manageme nt with free and open-source software (FOSS) - Alexandre Assuncao URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/8BGPQA/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-VQK7SK@talks.osgeo.org DTSTART;TZID=-03:20241204T143000 DTEND;TZID=-03:20241204T150000 DESCRIPTION:OGC Standards and OSGeo Projects have been widely applied to di fferent kinds of geospatial data and extended for the implementation of ge ospatial data science environments. However\, there’s no review comprehe nsively summarising and discussing the progress of these open source techn ologies for publishing geospatial databases on the Web. The proposed Syste matic Technology Review is a stylized version of the Systematic Literature Review\, covering the documentation of OGC Standards and OSGeo Projects. The search strategy consisted of screening OGC and OSGeo websites for the latest version of OGC Standards' implementation (or community) specificati on and OSGeo Projects' developers manual. This review considered the techn ologies published until June 2024. A total of 80 OGC Standards and 52 OSGe o Projects were identified. To recognize the main topics of each technolog y in detail\, the documentation was analysed by Latent Dirichlet Allocatio n - LDA using the Scikit-learn package in Python. Grid-search was used to find the optimal hyperparameters for the number of components and the deca y of the learning rate. With the maximum number of iterations set to 100\, the best model was obtained with 8 components and 0.1 learning decay. The n\, the most probable topic was predicted for each documentation. The netw ork of similarities arising from LDA was exported to Gephi for visualisati on\, where ForceAtlas2 layout algorithm was used to create a weighted undi rected graph\, keeping only edges with weight greater than 0.33. The lates t developments in terms of the OGC Standards for data encoding took place in the GeoPackage standard. For accessing\, processing or visualising data \, the trend was the development of OGC API related standards. However\, G ML is the most implemented OGC Standard for data encoding in OSGeo Project s\, along with Web Services like WMS\, WFS\, WCS and WPS for accessing\, p rocessing and visualising the data. Community Standards represented less t han 10% of the OGC Standards\, while Community Projects represented more t han 50% of the OSGeo Projects. The adoption of these technologies were eva luated based on the number of Github forks and stars\, as well as Docker p ulls. With more than 100 million pulls\, PostGIS is the most downloaded OS Geo Project\, followed by GeoNetwork and Open Data Cube\, with more than 5 million pulls each. But many of the analysed technologies lacked an offic ial Docker image. In terms of Github forks and stars\, the most shared and favoured OSGeo project is OpenLayers\, followed by QGIS and GDAL. The Lat ent Dirichlet Allocation analyses found eight topics underlying the OGC St andards and OSGeo Projects. The keywords of the top four topics were confo rmance\, layer\, tile and response. Based on the analysis of the Implement ation Standard and Community Standard documentations\, the most similar OG C Standards were OGC API - Tiles and Two Dimensional Tile Matrix Set. On t he other hand\, based on the analysis of developer manuals\, the most simi lar OSGeo Projects were GDAL and MDAL. The strongest relationship of an OG C Standard and an OSGeo Project occurred between WPS and ZOO-project\, fol lowed by WPS and PyWPS. Overall\, the OSGeo Project most closely related t o the entire set of OGC Standards was rasdaman\, followed by MapServer and deegree. Notably\, a large group of standards and projects showed scarce connections\, mainly those that are domain specific\, like PubSub\, LAS an d PipelineML among the OGC Standards and like Giswater and MobilityDB amon g the OSGeo Community Projects\, or those that are the basis of the other technologies\, like Simple Features\, WKT and Coordinate Transformation st andards and like PROJ and PostGIS projects. The presented Systematic Techn ology Review can promote the evolution of the current OGC Standards and OS Geo Projects\, as well as the development of new technologies. It can also support developers of new solutions in the geospatial community. Specific ally\, this review is the basis for the proposal of a new library for the integrated access of INPE’s environmental databases. An important limita tion of this systematic review is that it was not possible to find any PDF documentation for almost 20% of the existing technologies\, which were ex cluded from the analysis. DTSTAMP:20260422T231510Z LOCATION:Room V SUMMARY:Systematic Technology Review of OGC Standards and OSGeo Projects - Luiz Fernando Satolo URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/VQK7SK/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-BFMPHD@talks.osgeo.org DTSTART;TZID=-03:20241204T143000 DTEND;TZID=-03:20241204T150000 DESCRIPTION:Semantic interoperability is essential for integrating open geo spatial collaborative and official data. While geosemantics has long been a topic of discussion\, recent research has explored automated semantic in tegration without fully leveraging the capabilities of large language mode ls (LLMs) in artificial intelligence. This study investigates using chatGP T-4 to semantically associate OpenStreetMap (OSM) tags with the Brazilian topographic mapping model\, the Technical Specification for Structuring Ve ctor Geospatial Data (ET-EDGV). Focusing on five classes within the buildi ngs category\, the study tested three data structuring methods: spreadshee ts\, OWL ontology\, and XML. Results indicated that ontology and XML forma ts produced more accurate semantic associations than spreadsheets\, with O WL yielding the most coherent results. These findings underscore the impor tance of properly structured data to capture hierarchical relationships be tween concepts better. The study also noted the need for precise and detai led queries\, highlighting some limitations in chatGPT's ability to unders tand complex geospatial model inputs. Further research is recommended to e nhance LLMs' potential in facilitating semantic interoperability and to ex plore the role of prompt engineering in optimizing these interactions. DTSTAMP:20260422T231510Z LOCATION:Room II SUMMARY:Advancing Geospatial Data Integration: The Role of Prompt Engineeri ng in Semantic Association with chatGPT - Fabíola Andrade URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/BFMPHD/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-TWFHZB@talks.osgeo.org DTSTART;TZID=-03:20241204T150000 DTEND;TZID=-03:20241204T153000 DESCRIPTION:1. Introduction & Related Work\nPedestrian mobility is crucial in urban environments\, and its promotion can contribute to the achievemen t of many UN SDGs (Adriazola-Steil et al.\, 2021). Mapping\, which enables public scrutiny and long-term optimized planning\, is indispensable in th is context. \nWith the widespread availability of a large set of Open Stre et-Level Imagery\, such as Mapillary\, there is now a significant opportun ity that presents significant challenges for data extraction (Ma et al.\, 2019). The richness of detail in these urban landscape representations can help us better understand the peculiarities of urban environments. The s cope of this project is to make use of them for the study of pathways\, fo cusing in particular on the verification of their existence\, their catego rization (road\, sidewalk\, or general footpath)\, and the identification of their surface material. Since pedestrian crossings are part of the car and pedestrian network\, and road characteristics (such as material and wi dth) significantly impact pedestrian safety\, it is worth noting that the study of roads is also fundamental to pedestrian infrastructure (Mesfin & Denbi\, 2022). However\, the central aspect remains sidewalks\, often the most ubiquitous type of pedestrian thoroughfare (Kim\, 2019)\, a valuable space for sociability (Osman\, 2016)\, whose "health" is symptomatic of ho w pedestrian-friendly the city is (Mesfin & Denbi\, 2022). \nDespite the importance of knowledge about them for understanding the urban environment \, pavements are often poorly mapped (Vestena et al.\, 2023). Even fewer w orks delve into the problem of pathway surface identification: Zhou et al. (2023) used conventional Convolutional Neural Networks (CNN) to identify pavement classes limited to asphalt\, gravel\, and cement\; Zhang et al. ( 2022) used a similar approach to identify asphalt-only damage such as "pot holes" and "patches"\; only Mesquita et al. (2022) and Hosseini et al. (20 22) made pixel-level identification\, albeit the first one was limited to only "paved" and "unpaved" categorization\, while the second one notwithst anding having a more wholesome approach has its categorization focused on a New-York centered classes and only classifies sidewalks. There is still a gap in approaches considering standardized surface types and generalized path detection.\n2. The Framework\n We propose the Deep Pavements Framewo rk to address these issues. It is a modular project\, with each part contr ibuting to the solution of the different challenges. The first module is t he Surface-patches Dataset\, labeled following the OpenStreetMap surface=* tags standard\, supporting the categories of "asphalt"\, "cobblestone"\, "compacted"\, "concrete plates"\, "concrete"\, "grass"\, "gravel"\, "groun d"\, "paving stones"\, and "sett" currently\, The second module is the Run ner\, to process the data for a given region. The third is the Sample-pick er\, which generates random samples for dataset generation. There is also the Sample-labeler to label samples interactively and a central module to guide the potential user into the project's usage. Each module relies upon a different set of dependencies\, thus reducing runtime issues. It is imp ortant to highlight the primary usage of containerizing engines\, i.e.\, D ocker.\nBeyond modularity\, Deep Pavements has as core design principles: 1) complete openness\, meaning that all its dependencies must have a broad ly permissive license that enables even for commercial usage\; 2) the ease of reproducibility by a straightforward setup with an as such and well-do cumented\, command line interface (CLI)\; 3) evolvability\, the State-of-T he-Art (SOTA) algorithms are constantly changing\, then at each new releas e of runner/sample-picker images a new set of tools can be employed while keeping the same CLI\; nevertheless the user would still be able to use a previous release\; 4) Standard-anchored with classes that had been agreed upon by the broad crowd-sourced knowledge base constituted by OSM communit y (Rahmig & Kludge\, 2013\; Mooney & Minghini\, 2017). \nThe implementatio n of the main modules (Runner/Sample Picker) uses open-vocabulary AI algor ithms to perform the data extraction\, following this workflow: 1) Groundi ng Dino (Liu et al.\, 2023) based on a free-input\, detects the bounding b ox of the detections\; 2) Segment Anything (Kirillov et al.\, 2023) transf orms it into a mask\; 3) 3 different versions of the CLIP algorithm (Radf ord et al.\, 2021) tests if the detection is not a hallucination\; 4) If c onfirmed\, a specialized version of CLIP is used for finally check the sur face material using the cheaply clipped biggest rectangle in the detection \, assuring the usage of the patch whose texture got less hindered by the effects of perspective (Lederman & Klatzky\, 1995) being no-data pixels\, free\, which is another potential source for classification jeopardy (Kang et al.\, 2019). \nThe workflow results from experiments for the presented design mainly point out the need for hallucination testing. This procedur e acts as a shield for one of the main drawbacks of open vocabulary algori thms (Ben-Kish et al.\, 2024). The use of this particular type of algorith m was essential due to its flexibility (Wang et al.\, 2024) and the potent ial for better semantic understanding of the scene due to its embedded lan guage model (Eichstaedt et al.\, 2021). There is also the possibility of a llowing the user to opt out of some or all of OSM standardized classes\, w hich can be helpful in some scenarios with regional uniqueness. \n3. Fina l Remarks\nDeep Pavements is an innovative and comprehensive toolset under continuous development with all modules maintained at Github\, with the c entral module available at . The framework enables creating pavement data that is seamlessly plugable into OSM.\n As future challenges\, we plan to filter lousy qual ity images that can happen on the primary data source (Ma et al.\, 2019) t o detect other visually-identifiable pavement traits such as its decay\, s tandardizing with OSM tags such as smoothness=*\; to integrate photogramme tric tools for obtaining additional modeling of pavements\, having as main interest in measuring pavement width\, one of the most relevant info for accessibility assessment (Kim et al.\, 2011). DTSTAMP:20260422T231510Z LOCATION:Room III SUMMARY:Deep Pavements Framework: Combining Ai Tools And Collaborative Terr estrial Imagery For Pathway Mapping - Kauê de Moraes Vestena URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/TWFHZB/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-CUVPXC@talks.osgeo.org DTSTART;TZID=-03:20241204T150000 DTEND;TZID=-03:20241204T153000 DESCRIPTION:Most historical sources\, available in multiple formats (e.g.\, tabular and analog data)\, contain valuable geographic information. This data can be transformed to generate both quantitative and qualitative insi ghts\, enabling the creation of digital maps and unlocking significant pot ential for scientific analysis. However\, the use of historical data prese nts several challenges: 1. Sources need to be digitized\; 2. Collections a re often spread across multiple archives\; 3. Metadata is often unavailabl e\; 4. Standardizing diverse sources and quantitatively reconstructing dat a from various periods is difficult\; 5. The reliability of historical dat a can be uncertain\; 6. There is limited spatial resolution\; and 7. Inacc uracies and text legibility issues are common. These challenges underscore the need for novel methodologies aimed at enhancing the quality and quant ity of such sources. This paper presents the findings of the exploratory p roject AgroecoDecipher (2022.09372.PTDC) dedicated to extracting a compreh ensive database from historical textual records and analogue map files to trace agroecological patterns. Employing an exploratory methodology ground ed in artificial intelligence (AI) and Geographic Information Systems (GIS )\, the projected solutions include the establish-ment of routines based o n AI tools that combines GIS\, machine learning (ML)\, and Large Language Models (LLMs). Approxi-mately 271 survey books from the 1950s were digitiz ed at the municipal level\, with a total sheet count exceeding 42\,000. Ad di-tionally\, more than 100 analogue maps were digitized\, processed\, and vectorized\, resulting in a detailed geodatabase map ar-chive. The result s are promising and demonstrate that the integration of AI and geospatial tools has proven essential in trans-forming raw historical data. DTSTAMP:20260422T231510Z LOCATION:Room II SUMMARY:The Use of GeoAI Techniques for Gathering\, Storing\, and Analyzing Historical Agroecological Data - Cláudia M. Viana URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/CUVPXC/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-BBBU3X@talks.osgeo.org DTSTART;TZID=-03:20241204T150000 DTEND;TZID=-03:20241204T153000 DESCRIPTION:The National Institute of Colonization and Agrarian Reform (INC RA) is a body of the Brazilian federal government\, linked to the Ministry of Agriculture\, Livestock and Supply (MAPA). Its main mission is to exec ute national agrarian policy\, promoting agrarian reform and land planning in Brazil. INCRA works on several fronts to guarantee territorial regular ization\, sustainable development and social justice in the countryside.\n \nThe Amazon is a strategic region for INCRA due to its unique characteris tics and specific challenges. The body works on land regularization to gua rantee the legal security of squatters and combat land grabbing\, thus con tributing to environmental preservation\, and thus\, favors the control an d monitoring of rural properties\, helping to combat illegal deforestation and environmental degradation.\n\nFor regularization to become a reality\ , INCRA has signed Decentralized Execution Terms in several Brazilian stat es. In Rondônia\, the partnership was established with the Federal Instit ute of Education\, Science and Technology of Rondônia (IFRO)\, through TE D 20/2021/INCRA-SEDE/IFRO\, called GeoRondônia Project. The project aims to serve and document more than 25\,000 families and rural properties. To achieve this\, the necessary steps are the georeferencing of properties\, rural environmental registration and occupational supervision.\n\nThe geor eferencing of rural properties is a complex process that requires knowledg e in surveying\, legal aspects\, precision and efficiency. With the growin g demand for land regularization in Brazil\, especially in large areas suc h as the Amazon\, it is essential to seek solutions that reduce costs\, au tomate tasks and ensure data quality. This article presents the productivi ty gains achieved by the GeoRondônia project\, which uses QGIS and the Ge oINCRA plugin to automate tasks. The methodology developed involves data v alidation and quality control\, with automation implemented in Python\, en suring accuracy in property certification and the large-scale generation o f .ODS spreadsheets (certification document).\n\nThis is an innovative met hodology\, unavailable in any other Geographic Information System\, open o r private\, whose steps were developed to meet the highest technical quali ty standards for georeferencing projects with large volumes of data\, such as in GeoRondônia\, which has worked in settlements that have more than 2\,000 properties.\n\nInitially\, all georeferenced data processing dynami cs were carried out manually. In this way\, the Space Research Group (GREE S/IFRO)\, together with other collaborators from the Paraíba Institute (I FPB)\, has supported the project's actions for the development of innovati ve technology\, in order to optimize actions involving certification of ru ral properties\, using free tools.\n\nThe biggest challenge of the project is time and qualified labor\, therefore\, aiming to increase productivity \, the Free and Open Source Software Solution was developed\, called FOSSS GeoRondônia\, which integrates features from QGis and\, mainly\, the Geo INCRA Plugin . This set of functions enabled temporal gains\, professional qualification of employees and finances\, as it is completely free\, and can -be replicated to any other large data volume project involving georef erencing.\n\nTo optimize demands\, the main steps of FOSSS GeoRondônia\, after adjusting the observations tracked in the field to meet INCRA's posi tional accuracies\, are: a) elimination of topological errors in the geome tries of the settlement database\; b) elimination of errors when filling i n vertices and limits in the settlement database\; c) elimination of error s in the name of the property and .ODS spreadsheet\; d) generation of .ODS spreadsheets in an automated way and launch in INCRA's Land Management Sy stem (SIGEF).\n\nThe results obtained with FOSSS GeoRondônia are notable: Process Automation with the assembly of the database with greater securit y\, precision and practicality\; Automation in product generation\, where ODS spreadsheets that were previously done manually are now generated auto matically\; Reduction of spreadsheet preparation time by around 70%\; Elim ination of topological and writing errors\, ensuring accurate and consiste nt data\; Cost savings using QGIS and the GeoINCRA plugin\, eliminating th e need for expensive licenses\, allowing for a more efficient allocation o f public resources.\n\nCurrently\, the GeoRondônia Project has already or ganized the databases for 8 Settlement Projects\, which make up around 1\, 500 properties. A very productive result was the field collection\, proces sing and launch of 3 new Settlements (574 properties) in the state of Rond ônia in record time\, for launch by the Federal Government\, all carried out between April and May 2024.\n\nThe processing of georeferenced data on a very high scale\, using FOSSS GeoRondônia\, allowed the generation of products with precision and the consequent launch in SIGEF with reliabilit y\, which can be replicated for any project in Brazil. This has promoted g reater efficiency in the services performed\, and helped INCRA in meeting the large volume of Agrarian Reform demands\, to transform rural settlemen ts into true agents of sustainability and productivity.\n\nBased on this\, we are interested in presenting this new functionality to the national an d international public present at FOSS4G\, and who are looking for free so lutions for different demands\, in order to promote the dissemination and replication of the use of FOSSS GeoRondônia. DTSTAMP:20260422T231510Z LOCATION:Room I SUMMARY:Free and Open-Source Software Solutions in the GeoRondônia Project : Efficiency in Georeferencing of Rural Settlements with QGIS and GeoINCRA plugin - Leandro França\, Dra. Ranieli dos Anjos de Souza\, Valdir Moura \, Marcelo Vinicius Assis de Brito\, Bárbara Laura Tavares URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/BBBU3X/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-AJ7NJ8@talks.osgeo.org DTSTART;TZID=-03:20241204T153000 DTEND;TZID=-03:20241204T160000 DESCRIPTION:## Building an AI Dataset to Estimate Vegetation Carbon Using a QGIS-Based Annotation Tool\n\n- Yoon Jeongho\, Korea Environment Institut e\, Korea\n- Lee Sanghyuk\, Korea Environment Institute\, Korea\n- Son Seu ng-woo\, Korea Environment Institute\, Korea\n\n## Violence: Types\, Distr ibution\, and Frequency. A Study on the Re-concentration of Homicides in t he City of Rosario\, Argentina (2007-2023)\n\n- Silvina Meritano\, Nationa l Scientific and Technical Research Council (CONICET)\, Argentina\; Centre for Research and Studies on Culture and Society (CIECS-UNC)\, Córdoba\, Argentina\n\n## Use of Vegetation Indices for Monitoring Forest Cover in a Conservation Unit in the Far West of the State of Acre\n\n- Ananda Kellen Silva Rocha\, Federal University of Acre\, Cruzeiro do Sul\, AC\, Brazil\ n- Anelena Lima de Carvalho\, Professor\, Federal University of Acre\, Cru zeiro do Sul\, AC\, Brazil DTSTAMP:20260422T231510Z LOCATION:Main Hall SUMMARY:Poster Session II - URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/AJ7NJ8/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-WLKZ3Y@talks.osgeo.org DTSTART;TZID=-03:20241204T154500 DTEND;TZID=-03:20241204T161500 DESCRIPTION:With the advancement of technologies applied to Global Navigati on Satellite Systems and the popularization of Remotely Piloted Aircraft\, aerial photogrammetry has experienced significant advances\, especially i n geographic and environmental research. Drones equipped with high-resolut ion sensors have revolutionized data collection\, essential for topographi c mapping and vegetation analysis. However\, many technologies for digital processing and spatial analysis of original images are proprietary and ex pensive\, making access difficult for institutions and researchers\, espec ially in Brazil. This scenario makes free and open-source software\, such as WebODM\, an important alternative to democratize access to high-quality tools. In view of this\, this article analyzes the applicability of WebOD M in academic works\, through bibliometric analysis in scientific reposito ries. The search included variations in the software nomenclature and the data were analyzed quantitatively. The Elsevier and Scopus databases led w ith 59 and 39 publications\, respectively\, while the national Scielo Bras il database had only one article hosted. There has been a gradual increase in the number of publications involving WebODM since 2016\, with a peak o f 38 papers in 2023. In comparison\, the term "Agisoft Metashape"\, a prop rietary solution for digital aerial photogrammetric processing\, returned 1\,595 publications in the same search portals. As an initial contribution to the understanding of the state of the art\, it was observed that the t hematic axes involving remote sensing\, photogrammetry\, precision agricul ture and agricultural management were those that concentrated the largest number of scientific productions on WebODM in the investigated period\, ex ceeding 30 papers. It is concluded that WebODM has stood out as a relevant tool in scientific research\, especially for the processing of images der ived from drones. Future studies should qualitatively evaluate the results obtained with the use of WebODM in comparison with proprietary software. DTSTAMP:20260422T231510Z LOCATION:Room III SUMMARY:WebODM free software as a tool for digital aerial photogrammetric p rocessing: employability in scientific productions - Fabrício Lisboa Viei ra Machado URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/WLKZ3Y/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-KPYFTX@talks.osgeo.org DTSTART;TZID=-03:20241204T154500 DTEND;TZID=-03:20241204T161500 DESCRIPTION:Innovations\, such as voice recognition and natural language pr ocessing (NLP)\, have significantly impacted various fields by enabling mo re natural interactions between humans and machines (Mahmoudi et al.\, 202 3). In geoinformatics\, these advances are crucial for visualising geospat ial data\, allowing the creation of interactive and dynamic maps (Craglia et al.\, 2012). Online mapping applications\, like OpenStreetMap (OSM)\, h ave democratised spatial information by enabling public participation in i ts creation and maintenance (Haklay\, 2010). Geolocation is essential in c ontemporary applications\, such as navigation\, emergency services\, and l ocation-based services. Google Colaboratory (or Colab) Notebook Environmen t stands out in promoting open science due to its accessibility\, ease of use\, and collaborative capabilities\, and enabling the embodiment of the FAIR principles (Camara et al.\, 2021). This study aims to develop a voice interaction application in Google Colab Notebook Environment to answer th e question: "Is it possible to develop a voice command application for geo location and visualisation of geospatial data within the Google Colab envi ronment?" The methodology includes FOSS libraries and tools such as geopy\ , speech_recognition\, ffmpeg\, librosa\, and flask\, subdivided into six stages: Audio Data Acquisition\, Audio Processing\, Speech Recognition\, G eocoding\, Visualization\, and Interface Development. The complete code\, under an open license\, and how to reproduce this work are available on Gi tHub. Audio capture is performed using the Web Speech API in JavaScript (J S)\, which allows real-time voice recognition and integration with the Med iaDevices API to access the user's microphone. This method provides an int erface for high-quality audio recording\, essential for speech recognition and geocoding accuracy. Audio processing involves converting the ".webm" format to ".wav" using ffmpeg\, efficiently maintaining the original audio quality. The Librosa library loads the audio\, adjusts the sampling rate\ , and extracts relevant features from the audio signal\, such as spectrogr ams (Bisong\, 2019). Speech recognition is performed with the SpeechRecogn ition library in Python\, which provides an interface for various speech r ecognition services\, including the Google Web Speech API. This choice is due to its high accuracy and support for multiple languages\, ensuring the system's flexibility and accessibility to a diverse audience (Nassif et a l.\, 2019). Geocoding transforms textual descriptions of locations into ge ographic coordinates\, allowing the visual representation of these locatio ns on an interactive map. The geopy library and the Nominatim service from OSM are used to convert addresses into latitude and longitude coordinates (Mooney & Corcoran\, 2012). For the visualisation of geocoded data\, a we b server was implemented using Flask\, a microframework for Python that al lows the creation of lightweight and efficient web applications. The user interface was developed with HTML\, CSS\, and JS\, providing an intuitive and interactive experience. The results show that the user and machine int eraction occurred satisfactorily. The first message displayed to the user instructs them to slowly state the name of the city\, state\, or country t hey wish to geolocate. The use of JS and the Web Speech API allowed the sy stem to detect specific voice commands to start and stop recording\, as in dicated by the interface colors and states. This step is crucial for subse quent steps to ensure that the captured audio is clear and understandable. When the start command is recognised\, the interface changes to indicate that the recording is in progress. The message "Command recognised: starti ng recording" confirms that the command was detected correctly. If the voi ce command is not recognised\, the interface displays a message asking the user to repeat the command. After recording\, the audio is saved in ".web m" format. If a previous audio file exists\, it is automatically overwritt en. This approach simplifies file management and avoids the accumulation o f unnecessary data. Next\, the audio is converted to ".wav" format using t he ffmpeg library. Then\, the audio is transcribed using the Web Speech AP I and the SpeechRecognition interface for the recognised language\, along with the confirmation of the geocoded location and its respective latitude and longitude. The visual feedback proved essential for the user to confi rm that the entered information was recognised\, improving the system's us ability. The displayed information includes city\, region\, country\, lati tude\, and longitude. The interactive map allows the user to visualise and interact with the located area\, altering the zoom level and receiving a voice message informing the map's current zoom level. This work presented the integration of tools that assist in advances in human-computer interac tion in geoinformatics\, offering an intuitive and accessible interface fo r users of different technical proficiency levels. The results confirm the feasibility of voice command geolocation in Google Colab\, a platform tha t can be used for education\, research\, collaboration\, and sharing in sc ience\, enabling this work's reproducibility. Future research can improve voice interaction features\, explore geolocation methods such as bounding boxes\, and reduce dependence on JS and Flask. Improving the requirements for peripheral devices could further increase the system's accuracy\, acce ssibility and user experience. The importance of geospatial accessibility lies in enhancing service provision\, urban planning\, and social inclusio n\, facilitating mobility for people with disabilities\, and improving urb an infrastructure (Han et al.\, 2020). DTSTAMP:20260422T231510Z LOCATION:Room II SUMMARY:Natural Language Processing and Voice Recognition for Geolocation a nd Geospatial Visualization in Notebook Environment - Nathan Damas URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/KPYFTX/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-YWFDM8@talks.osgeo.org DTSTART;TZID=-03:20241204T154500 DTEND;TZID=-03:20241204T161500 DESCRIPTION:The Amazon faces significant logistical challenges due to its s ize and complex geographical features\, such as the dense rainforest and v ast hydrographic network. In Brazil\, Amazonas stands out for its ecologic al diversity and difficulties of access\, requiring innovative solutions f or educational development. The Amazonas Education Media Centre (CEMEAM) u ses In-Person Teaching with Technological Mediation (IPTTM) to overcome th ese obstacles\, promoting digital inclusion and democratizing access to ed ucation. Implemented in 2007 by the Amazonas State Secretary of Education and School Sports (SEDUC-AM)\, it combines face-to-face classes with satel lite videoconferences and other media\, reaching more than 25\,000 student s in an area of 1\,571\,000 km². To help with spatial management\, CEMEAM was presented with a proposal for a Geographic Information System in a we b environment (WebGIS) using free software and plugins (QGIS and QGIS Clou d) as well as freely accessible products. The WebGIS enabled a variety of analyses and proved it could contribute to more efficient spatial manageme nt\, adapted to regional specificities\, such as the isolation of localiti es and the dependence of students on river transportation. Although it is not yet a formalized institutional tool\, SIG Web demonstrates the potenti al of geotechnologies in educational management in the Amazon\, serving as an important experiment to support CEMEAM in its needs. DTSTAMP:20260422T231510Z LOCATION:Room I SUMMARY:The Web GIS as an Auxiliary Management Tool for In-Person Teaching with Technological Mediation in the Amazon Rainforest: The Case of CEMEAM\ , Amazonas\, Brazil. - Alexandre Donato da Silva URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/YWFDM8/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-HMEXFV@talks.osgeo.org DTSTART;TZID=-03:20241204T161500 DTEND;TZID=-03:20241204T164500 DESCRIPTION:Public participation is of utmost importance for community mobi lization and engagement\, so that through their networks and relationships \, both within and outside the community\, they create space through socia l action. According to Goodchild (2007)\, there is a demand to generate in formation that helps vulnerable communities to strengthen relationships wi th the government responsible for promoting important interventions to bri ng about change. It is possible to use the sensitivity and ability to info rm the needs of each resident\, from their perception\, and understand the needs of their community. Therefore\, it is important to use open and fre e mapping tools that represent the community's demands\, producing informa tion that allows collective autonomy to carry out strategies that involve the public authorities through co-optation and coalitions aimed at communi ty-based interventions (Silva\, 2014).\n\nIn this sense\, OpenStreetMap (O SM) stands out as a tool for collaborative mapping and community intervent ions in highly vulnerable urban areas\, such as favelas. Given OSM's parti cipatory and open nature\, it allows the creation of maps with records of features of various kinds\, but it is also of great value as a platform fo r community training and the development of geospatial skills (Bortolini a nd Camboin 2019).\n\nThe objective is to analyze from the literature how O penStreetMap has been used for training and empowering local actors in pro jects aimed at community-based interventions in favelas.\n\nA systematic l iterature review was carried out with the support of artificial intelligen ce tools (Chat GPT-4\, Elicit\, Semantic Scholar\, Chat pdf)\, bibliograph y management software (Zotero)\, and software for visualizing bibliometric networks (VosViewer) combined with other research methodologies (P.I.C.O. \, Bardin) to assist in the overall evaluation of the literature. Although AI tools have great power to aid the review\, they do not replace the nee d for critical judgment and human expertise\, demanding confidence in the knowledge of the contents and scientific methodology.\n\nThe following key words were used in the platforms of WoS and Scopus collection lists in the first search: [Collaborative mapping\, Community intervention\, OpenStree tMap\, Community empowerment\, Community mobilization\, Citizen participat ion]. In this first search\, some filters were established\, such as the p ublication date within the last 10 years and articles that were open acces s. Based on these filters\, 43 articles were found that fit these specific ations in the Web of Science\, with the vast majority in English. From thi s first literature search\, there also arose the need to increase the numb er of articles that most closely aligned with the theme. The keywords were adjusted based on these articles. In this second search\, other databases were also included for the research\, such as Scopus and Google Scholar. The use of artificial intelligence tools Elicit and Semantic was essential to find articles using the keywords that were most repeated in the main a rticles. Still in this second search\, these adjusted keywords were entere d into Chat-GPT 4 to generate search strings under the acronym P.I.C.O (Po pulation\; Intervention\; Comparison\; Outcome) for use in the Web of Scie nce.\n\nKeywords for the second search: [Collaborative mapping\, informal settlements\, urban slums\, OpenStreetMap\, public participation\, communi ty engagement\, community-based intervention\, community intervention].\nF inal search string: [("Collaborative mapping" OR "participatory mapping") AND ("community intervention" OR "community-based intervention") AND ("Ope nStreetMap" OR OSM) AND ("community empowerment" OR "empowerment") AND ("c ommunity mobilization" OR "community engagement") AND ("citizen participat ion" OR "public participation") AND (favelas OR "informal settlements" OR "urban slums")]. Finally\, twenty articles were identified in the Web of S cience\, Google Scholar\, and Scopus databases.\n\nBased on these 43 artic les\, a synthesis framework is being built that aims to systematize inform ation about the works found\, informing: 1) Source/Base/Collection\, 2) Re ference according to ABNT\, 3) Name of the Journal\, 4) Contact of the mai n author - email\, 5) Country of affiliation of the authors\, 6) Country o f the mapped community\, 7) Problem/Objective/Hypothesis\, 8) Methodology\ , 9) Materials used\, 10) Techniques used\, 11) Main results\, 12) Does it work with Favela? 13) What is the nature of the community-based intervent ion? 14) Did favela residents operate the OpenStreetMap? 15) Where is the favela and/or intervention? 16) If mapping in a favela\, what features and attributes were mapped? 17) Were integrated digital and analog cartograph ic technologies used? Which ones? 18) Were methods used for community appr opriation of cartographic tools and data? 19) Was educational material pro vided? Indicate link\, 19) Was a method for evaluating the tools and proce sses used implemented? 20) Were community impact indicators used? 21) Are effective impacts felt by the community reported? Which ones?\n\nBy constr ucting this framework\, we are evaluating aspects such as the geographic d iversity in the use of OSM\, indicating the platform's flexibility and ada ptability\, or the still limited participation of local actors in mapping their communities. By compiling data on the nature of community-based inte rventions\, techniques and methodologies used\, and community impact indic ators\, we aim to identify common patterns in the types of interventions t hat have been most effective. Furthermore\, the analysis of reported impac ts can indicate tangible benefits of these projects.\n\nThe analysis of ho w projects addressed training and education\, including the provision of e ducational materials and methods for community appropriation of cartograph ic tools and data\, can indicate strategies used to empower local communit ies. We are also analyzing the features and attributes mapped specifically in favelas\, to identify the main challenges and specific needs of these areas\, supporting the indication of demands for improvements in methodolo gies and mapping tools in these urban contexts.\n\nThus\, we are building a comprehensive framework on the current state of the use of OpenStreetMap for training and intervention in favelas\, identifying gaps\, challenges\ , and opportunities for future research and projects. DTSTAMP:20260422T231510Z LOCATION:Room I SUMMARY:OpenStreetMap in the Training of Local Actors in Projects Aimed at Community-Based Interventions in Favelas: A Systematic Review Supported by AI - Patricia Lustosa Brito\, Pedro Melhado URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/HMEXFV/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-NKNMCD@talks.osgeo.org DTSTART;TZID=-03:20241204T161500 DTEND;TZID=-03:20241204T164500 DESCRIPTION:Advances in spatial and spectral resolution in private sector s atellite imagery\, together with geography aware algorithms\, have created new venues for the use of Artificial Intelligence (AI) in geospatial appl ications\, sometimes referred to as AI4Geo. However\, these advancements a re accompanied by significant costs in the procurement of data\, computing resources\, communication infrastructure and human expertise. We describe a case study in central Bali in which we developed multiple AI4Geo approa ches to assist the WISNU foundation\, a Non-Governmental Organization in B ali\, Indonesia\, in their ongoing efforts to manage community resources a nd to perform land mapping across small villages in Bali. \n\nConcepts \nT he concept we explore here is multipath AI4Geo that seeks to find the “b est” approach to AI4Geo for resource constrained environments. The assum ption that larger models are always better does not hold where AI4Geo\, tr ained on data from dominant western institutions\, is applied in the major ity world. Some of the most ambitious AI4Geo models are trained for land c over categories that are mostly of interest to the Northern Hemisphere. Gi ven this imbalance\, we ask how participants from low-resourced environmen ts can best make use of AI4Geo.\n\nMethodology\nBased on field data from a study site in Bali\, Indonesia\, we have developed multiple open source A I4Geo land cover approaches to find the best way to represent agroforestry \, a key indicator of sustainable and robust food production. We compare t he image segmentation results from small models such as Random Forests (RF ) and Support Vector Machines (SVM) with large models such as U-Net and ResNet152 not only along established model performance metrics such as f-s core\, but also in terms of their suitability for use in low-resource cond itions. This generally includes limited ability to collect large data sets \, limited computational infrastructure\, limited AI expertise and limited internet connectivity. We then describe a mixed-method multi-pathway appr oach to produce good AI4Geo results while building capacity for the NGO to continue the integration of AI4Geo into its operations while planning fo r an even more challenging AI4Geo future dominated by large homogenizing A I models. \n\nHere are links to code experiments and instructions on gener ating the required input data for the U-Net model from geospatial shapefil es.\n\nSmall models (RF\, SVM based on the Orfeo library)\nhttps://github. com/realtechsupport/cocktail/tree/main/code \n\nLarge models (Custom desig ned U-Net and SATLAS based ResNet models)\nhttps://github.com/realtechsupp ort/cocktail/tree/main/satlas_test\nhttps://github.com/realtechsupport/coc ktail/blob/main/sandbox/working_model/working_model_inference.ipynb \n\nRe sults\nWhile RF\, SVM and U-Net approaches were all able to detect agrofor estry in 8-band\, 3-meter spatial resolution datasets provided by Planet L abs\, we found that the SVM algorithm was most responsive to the limitatio ns of our dataset while producing useful results that we could verify in t he field. SVM was furthermore painless to update with additional field dat a. Figure 1 summarizes the results from the image segmentation after model training.\n\nWhile U-Net’s f1 accuracy for agroforestry exceeds that of RF and SVM\, it is likely an overestimate of the actual extent of agrofor estry. We believe this to be the case because the U-Net architecture inges ted patches of 16 x 16 pixels\, and these dimensions exceed the size of t he smaller agroforestry plots detected in the field. The choice of the inp ut patches was in turn a function of the dimensions of the U-Net architect ure selected for its ability to minimize loss during training across all l and cover categories. \n\nAs opposed to the three other models listed abov e\, the large ResNet152 model was not trained on data Planet Lab satellite imagery but on Sentinel-2 imagery. Because Sentinel-2 only has a maximum spatial resolution of 10m/pixel it is not able to distinguish small scale landscape features\, agroforestry that typically utilizes small plots in r andom arrangements. While the ResNet algorithm was trained on the largest dataset\, with over 300 distinct labels across 137 classes represented acr oss 64 million images\, the class labels are not tuned to the spectral sig natures of agroforestry and deliver only crude results in our selected stu dy area\, as Figure 2 shows. Moreover\, The ResNet152 model that supports multi spectral Sentinel-2 input has over 80 million trainable parameters\, exceeding our bespoke U-Net model by more than an order of magnitude\, th us making its use more costly.\n\nWhile we have not fine-tuned the Resnet 152 model with our own highest resolution Planet Lab data due to spatial r esolution mismatches\, it seems clear that the effort would exceed the cap acities of our partner organization WISNU. Our dilemma is that the most pr omising large models are unwieldy and not adapted to our land cover condit ions while the smaller models we have end to end control over can be tuned with smaller dataset but run the risk of becoming obsolete in the AI arms race over time\, where larger and more powerful models become standard-be arers. While the agroforestry specific results we observe are characterist ic of our study area and the constraints our project operates under\, the homogenizing forces of large models pose a condition all AI4Geo operations are faced with. For that reason\, the territory of this project is signif icant beyond the immediate results we produce.\n\nOur solution to this dil emma is two-fold. We deploy multi-pathway AI4Geo across various technical complexity levels while retaining agency for local stakeholders. The Wisnu foundation does preliminary studies of Sentinel-2 satellite imagery throu gh the QGIS environment to survey sites and build simple datasets. They th en use QGIS integrated small model approaches such as Random Forests to bu ild baseline segmentation maps of a given area. The research team will the n collect Planet Labs based higher resolution data and use the cocktail su ite of models\, including U-Net\, to deepen the study results. Parallel to this approach\, we together use the SATLAS ResNet models to find synergie s in those results. Across the approaches\, we build land cover analysis r esults that optimize limited resources while producing solid analytical re sults. DTSTAMP:20260422T231510Z LOCATION:Room III SUMMARY:GeoAI in resource-constrained environments. - marc böhlen URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/NKNMCD/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-38D7T8@talks.osgeo.org DTSTART;TZID=-03:20241204T164500 DTEND;TZID=-03:20241204T171500 DESCRIPTION:Abstract: \nGlobal Gap: Updated population estimates and total households (HHs) per area\, typically obtained through a census\, are used to construct unbiased sampling frames necessary for accurate estimates in health surveys. These population and HH estimates are used to select a sm aller representative sample of enumeration areas (EAs) to visit for a publ ic health study. Several methods exist to select which HHs to visit at the EA level including systematic sampling (every nth HH)\, geographically sa mpling structures using satellite imagery\, segmenting an area\, and mappi ng and listing all HHs in the selected EA. Accurate estimates are essenti al for public health programming and response\; however\, forty-two countr ies have not conducted a census for over a decade (United Nations Statisti cs Division 2024). Instead\, programs often generate an accurate sampling frame by enumerating HHs within selected EAs obtaining answers to eligibil ity criteria\, drawing a sample from this enumerated list\, and navigating back to selected HHs. This requires considerable resources. To date\, no free mobile-based application exists to streamline these processes.\nSolut ion: The GPSSample application is a user-friendly sampling solution to sel ect HHs within EAs. The study administrator makes a configuration in GPSSa mple creating the eligibility screening questions and specifying the numbe r or percent of HHs to return to in each EA. An example screening question for an immunization coverage survey question is: “Are there any childre n living in this HH between six and fifty-nine months old?”. In GPSSamp le\, teams can rapidly enumerate HHs in an EA and collect answers to these screening questions. Teams send encrypted data to a supervisor via new lo cal-only mobile hotspot QR codes. Next\, the supervisor presses a button\, easily generating a simple random sample from the sampling frame of enume rated HHs. The selected HH list is sent to teams. Using GPSSample\, teams navigate back to selected HHs to conduct surveys. GPSSample integrates sea mlessly with survey applications\, including ODK Collect and Kobo Toolbox\ , opening the second app’s designated form. Users send unique HH ids an d cluster data from GPSSample to the specified HH survey form. Upon saving the HH survey\, teams are returned to the GPSSample app to mark the statu s of the HH. Teams use a map and a list view of selected HHs for monitori ng field work. Supervisors can view EA and study level summary statistics in GPSSample to monitor field work.\nFurthermore\, the GPSSample app can b e used in surveys lacking any advanced information on population or areas of concern. It is not necessary for a country to have conducted a census. Supervisors can draw an area within GPSSample onsite\, segment the area\, and assign to field teams to rapidly enumerate locations before sampling t hem for the assessment or survey. This novel capacity in GPSSample highlig hts the flexibility and potential for use in outbreak investigations and e mergency responses where HHs may be damaged or destroyed. Additionally\, f ood outbreak investigations conducted at market stalls or stores may not b e collated in a central list. While designed in the public health context\ , GPSSample is useful for other disciplines. \n\nGPSSample is a free Andro id 8+ application available in six languages: English\, French\, Spanish\, Portuguese\, Russian\, and Bahasa. It is designed for field practitioners with limited mobile networks or Wi-Fi. The app was developed using the Ko tlin open-source language and it uses the open-source SQLite database. Use r guides\, GPSSample Decoder application decrypting data\, demonstration v ideos\, and Quarto analyses are available through the GPSSample GitHub sit e.\nUse Cases and Road Map: Development lessons learned will be presented from two public health GPSSample application pilots in India and Kenya. We aim to engage the FOSS4G development community to enhance GPSSample’s g eospatial functionalities and learn best practices on maintaining and upda ting open-source code. GPSSample currently uses Mapbox. Ideally in a futu re version\, users will also be able to select OpenStreetMap for the base map and the app will include navigation using offline turn-by-turn instruc tions. \n\nReferences\nUnited Nations Statistics Division\, 2024: 2020 Wor ld Population and Housing Census Programme. Available at: https://unstats. un.org/unsd/demographic-social/census/censusdates/. Accessed 6/24/2024\, 2 024. DTSTAMP:20260422T231510Z LOCATION:Room IV SUMMARY:Bridging the Gap: GPSSample – An Innovative Tool for Enumeration and Sampling in Health Surveys - Amber Dismer\, Joel Adegoke URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/38D7T8/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-JEVCDC@talks.osgeo.org DTSTART;TZID=-03:20241204T164500 DTEND;TZID=-03:20241204T171500 DESCRIPTION:Forest registration is essential for effectively managing natur al resources\, enabling improved tree management (Kattenborn\, Eichel and Fassnacht 2019). This process simplifies urban planning\, allowing for a m ore conscientious approach and significantly contributing to the preservat ion of green areas. The proposal to reduce environmental impact\, survey t ime\, and required effort (Barbosa et al. 2018\; Li et al. 2015\; Beloiu e t al. 2023) has motivated the growing use of computer vision for these tas ks. Today\, this represents a true cartographic revolution. These innovati ons enhance the quality of life in cities by providing accurate and up-to- date data to support critical decisions (Barbosa et al. 2018).\nThis work aims to detect\, classify\, and georeference trees in urban environments u sing image segmentation algorithms applied to aerial and street-level imag es. Several studies use aerial images (Beloiu et al. 2023\; Wäldchen and Mäder 2018\; Mlenek\, Dalla Corte e Santos 2020)\, but our approach seeks to improve the detection and identification of tree species by combining street-level images with aerial images. Our model will be developed with t he algorithms that present the best metrics for species segmentation and c lassification based on related studies. The project also prioritizes using free and open-source software in its development. This not only democrati zes access to robust monitoring and analysis tools but also encourages col laboration and innovation in the geospatial community\, aligning with the values of FOSS4G.\nWe will apply pre-processing techniques to the images t o enhance the model’s accuracy\, including geometric and atmospheric cor rection with QGIS software. Gaussian filters will also be applied to reduc e noise and contrast adjustments to make edges and textures more distinct. After this step\, we will proceed to the feature extraction stage for aut omatic species identification using a machine-learning model. Given the in creasing need for environmental preservation and sustainable management\, identifying and classifying tree species have become solid allies for ecol ogical conservation\, positively impacting urban quality of life.\nTo map the urban area of Rio Paranaíba\, an unmanned aerial vehicle (UAV) drone equipped with a high-resolution camera was used\, capturing images with a 3.5 cm resolution. The UAV was operated autonomously\, flying in parallel strips over the city. A 70% overlap between the images was used\, resultin g in the creation of an accurate orthomosaic of the region\, favoring more accurate georeferencing of the trees. OpenStreetMap software was used to create the orthomosaic. GPS performed georeferencing during the flight. St reet-level images were obtained with a camera that provides 360º coverage . For species classification\, a training dataset was created from samples collected in the field\, both aerial and ground-level. Various machine le arning algorithms\, such as Random Forest\, Support Vector Machine (SVM)\, and Convolutional Neural Networks (CNN)\, were researched and evaluated f or their accuracy in species classification.\nTree identification through images of trunks and leaves presents significant challenges due to high in traclass variability and high interclass similarity. High intraclass varia bility refers to the substantial differences between images of trunks or l eaves of the same tree species caused by lighting variations\, capture ang le\, and tree condition. On the other hand\, high interclass similarity re fers to the very similar visual characteristics between different species\ , making it difficult to distinguish one from another based solely on appe arance. Additionally\, improper color balance adjustments by cameras can i ntroduce unwanted shades\, such as a greenish tint\, further complicating accurate classification. These combined factors make using deep learning f or tree classification a complex and challenging problem (Cotrim et al. 20 19). This technique\, which combines remote sensing with aerial and ground -level images and advanced machine learning techniques\, is expected to pr esent a significant advance in tree species classification. This approach allows for detailed analysis of trunk and leaf textures\, potentially sign ificantly improving species identification accuracy. Studies such as those by Kattenborn\, Eichel and Fassnacht (2019) have demonstrated that CNN-ba sed segmentation (U-net) can achieve an 84% accuracy in vegetation classif ication using high spatial resolution RGB images. The U-net is widely reco gnized for its effectiveness in image segmentation tasks\, especially in h igh-precision and detail scenarios. Its architecture captures complex feat ures\, making it ideal for detecting and classifying specific elements in high-resolution images. Additionally\, the U-net has shown consistent resu lts in various remote sensing applications\, making it a reliable choice f or geospatial data analysis projects. Therefore\, adopting the U-net in th e project can ensure superior tree species identification and mapping perf ormance. This work aligns closely with the themes addressed at the FOSS4G event\, as it demonstrates the practical application of free and open-sour ce software tools in an environmental monitoring context. QGIS\, OpenDrone Map\, and OpenStreetMap exemplify how open technologies can be integrated to solve complex georeferencing and species classification problems. Furth ermore\, the focus on urban areas and the combination of drone and street view data provides valuable insights for the geospatial community\, showin g the feasibility and benefits of free software for urban and environmenta l applications. DTSTAMP:20260422T231510Z LOCATION:Room I SUMMARY:Georeferencing of Urban Trees Using Drones and Ground-Level Imaging \, and Classification of Their Species by Machine Learning - Paulo Roberto Ferreira Maciel\, Rodrigo Smarzaro URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/JEVCDC/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-TNEYZW@talks.osgeo.org DTSTART;TZID=-03:20241204T171500 DTEND;TZID=-03:20241204T174500 DESCRIPTION:This article presents the case of the mapping of the informal s ettlement Erizo Juan Santamaría. The neighborhood went from being an empt y space on digital maps to be part of the official cartography of Costa Ri ca. The mapping was carried out using technologies based on free/open soft ware and participatory cartography methodologies\; the work was done joint ly between the people who live in the community and Laboratorio Experiment al (LabExp) a research and extension project of the public university Inst ituto Tecnológico de Costa Rica. The active participation of the communit y in the process was key for the Municipal Council of Alajuela\, where the neighborhood is located\, to make official the traces and names of the st reets and alleys of Erizo Juan Santamaría for municipal purposes. Further more\, at the request of the municipal council\, the National Nomenclature Commission approved the names at the national level.\n\nThe informality o f the Erizo Juan Santamaría neighborhood lies in the fact that the people who live in the space do not own the land. The territory where the neighb orhood is located belongs to two public institutions\, one part to the Mun icipality of Alajuela and the other to the National Institute of Housing a nd Urbanism (INVU). In the 1970s\, the first families began to occupy the territory where the settlement is currently located. Since then\, the inha bitants of Erizo Juan Santamaría have solved their basic common infrastru cture needs\, as well as managed access to public services. The two public institutions\, owners of the land\, as well as the neighboring neighborho ods have assessments and interests in the informal settlement\, which are manifested in a tense relationship that includes marginalization\, manipul ation\, stigmatization\, and invisibility.\n\nIn 2017\, LabExp and represe ntatives of the neighborhood agreed to work together on a 4 years universi ty extension project aiming to make the informal settlement visible to dec ision-making institutions and neighboring neighborhoods through maps. Unti l then\, the neighborhood was not represented on commercial digital maps o r on the free OpenStreetMap map. LabExp proposed a work plan based on part icipatory processes\, the use of free software and open geospatial data.\n \nIt was determined to prioritize two elements to be mapped\, considering the relevance for the community in its relationship with the different dec ision-making actors. The first was the houses\, since INVU was interested in developing a project to improve the neighborhood's housing infrastructu re\, the institution would carry out a census. Through a number in each ho use\, the map could be linked to the census data. The second were the stre ets and alleys\, with the intention that neighbors improve the way in whic h they gave their home addresses when requesting services. At all times\, OpenStreetMap was considered as the repository where the collected data wo uld be stored. The mapping process was carried out with free and open tool s from the OSM ecosystem: OSMTracker to capture GPS data in the field\, Fi eldpapers to collect data in workshops and conversations with neighbors\, JOSM to edit the OSM map and QGIS both to create maps to capture data and to create maps to disseminate the mapping process. The mapping activities and dynamics included: free cartography workshops with students at the loc al school\, field trips and unstructured playful dynamics with children in the neighborhood.\n\nIn addition to the mapping\, two activities were key to foster a feeling of ownership of the process by the residents of the n eighborhood and to disseminate the partial and final results. The first wa s the production of short videos in order for the community's inhabitants to narrate their reality about infrastructure\, show the neighborhood\, an d describe the relationship with the decision-making institutions\, in suc h a way that they linked these experiences with the process of mapping. Th e second activity was a voting process to choose names for streets and all eys. Each person in the neighborhood had the opportunity to make name prop osals for the mapped transit spaces. Subsequently\, the residents of the n eighborhood were called to elections. One Sunday morning\, each person had the opportunity to express their will\, voting for the names of their str eets and alleys together.\n\nThe mapping process was completed by 2021\, E rizo Juan Santamaría appeared on the digital maps. In OSM\, the houses we re included with their respective numbering according to the needs of the INVU\, the streets and alleys with the names selected by the inhabitants\, elements of public infrastructure\, trees and the proper name of the neig hborhood. The community was also represented on other commercial maps. Tha nks to the dissemination of the short videos and press releases in the Uni versity's and national media\, the mapping process of Erizo Juan Santamar ía was known to the members of Municipal Council of Alajuela. The Counci l dedicated an entire session to heard about the project and agreed to mak e official the names of the streets and alleys decided in the voting proce ss by the neighbors. In addition\, the Council managed to make official th e names before the National Nomenclature Commission of the National Geogra phic Institute.\nThe case of Erizo Juan Santamaría is a unique example in the country where\, through participatory cartography\, the production of free geospatial data is contributed to official cartography. The visibili ty of the neighborhood on digital maps makes it easier for the inhabitants to access services that were previously denied or restricted due to the i nsecurity that people offering the service felt about visiting the neighbo rhood\, partly due to stigmatization and partly because the location led t o an empty space on the digital map. Given the increasing use of digital m aps to access services and make decisions\, it is important to discuss the right of communities to appear on digital maps. DTSTAMP:20260422T231510Z LOCATION:Room I SUMMARY:Study Case of Erizo Juan Santamaría: from free map to official car tography - Jaime Gutiérrez Alfaro URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/TNEYZW/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-WRGQFQ@talks.osgeo.org DTSTART;TZID=-03:20241204T174500 DTEND;TZID=-03:20241204T181500 DESCRIPTION:Marine mammals occur in low densities and usually in areas that are difficult to access. One of the main sources of information for marin e mammals are stranded animals. However\, strandings are rare events and t o be biologically meaningful they need to be accumulated over large distan ces\, long times\, or both. This work describes SIMMAM (Sistema de Apoio a o Monitoramento de Mamíferos Marinhos)\, a project aimed at organizing a database of marine mammal sightings and strandings along the Brazilian coa st and available at https://libgeo.univali.br/simmam. It began as an inter nal research project by UNIVALI but now is used by IBAMA and ICMBio. Its i nitial implementation has already been described [Moraes\, 2005\; Barreto et al.\, 2006]. However\, it was almost completely rewritten since its ini tial implementation and SIMMAM 3.0 now conforms to the DarwinCore (DwC) st andard\, which is an international scientific initiative of the Taxonomic Database Working Group - TDWG. The data architecture adopted is compatible with GBIF\, which allows SIMMAM to become a data publisher of marine mamm al occurrences.\nFor the development of SIMMAM 3.0 free source code tools were adopted. On the server side\, PHP 7.4 was used with Symfony 5.x frame work. For the web client side\, the site is rendered on the server side an d delivered to the browser as an HTML + JavaScript page with Bootstrap 5. The data exchange API was also implemented in PHP\, following the XML stan dard of DwC. Data is stored in PostgreSQL 11.x with PostGIS 3.x which allo ws manipulation of geospatialized data. Tables were structured according t o the DwC standard\, to reduce the complexity of the communication API.\nA s all occurrences in SIMMAM need to have a geographic position\, the main interface for users to view the data is through an interactive WebGIS. The implemented WebGIS has filters by taxon and by type of occurrence (sighti ng\, stranding\, incidental capture) to allow users to focus on specific d ata. To better display areas with high density of records without generati ng visual clutter\, the occurrence layer was clustered\, grouping and ungr ouping records according to the zoom level. Leaflet Map [Agafonkin\, 2020] was used with the OpenStreetMap base map\, as it is a modern map engine\, has functionalities optimized for mobile devices\, and does not have any external dependency. Leaflet supports multiple layers and is compatible wi th the Open Geospational Consortium (OGC) standard such as support for map mosaics\, georeferenced images\, WMS [Leaflet\, 2020] and GeoJSON [IETF\, 2021].\nOne key aspect of biological information is the taxonomic identif ication. To avoid taxonomic instability in SIMMAM\, it uses the taxonomic list provided by the Integrated Taxonomic Information System – ITIS (www .itis.gov). As the taxonomic classification of mammals is very stable\, it was decided to keep a copy of the ITIS database locally to reduce latency \, being updated on demand. \nThe types of occurrence records currently su pported by SIMMAM are stranding\, incidental capture and sightings. All th ese occurrence records have fields for defining the best taxonomic level\, geo-referencing the occurrence\, information on biological material colle cted and the person responsible for the data. Stranding and incidental cap ture records contain information regarding the state of the animal (alive or dead)\, the condition of the carcass (decomposition stage)\, sex and le ngth. For sightings it is possible to inform environmental parameters such as weather condition\, sea state\, wind speed\, as well as if it was a si ngle animal\, part of a group and group size.\nThe first version of SIMMAM was made available in 2007 to the Centro Mamíferos Aquáticos - CMA\, th at started to use it as the main tool to integrate data for the Brazilian Stranding Network of Aquatic Mammals (Rede de Encalhes de Mamíferos Aquá ticos do Brasil\, REMAB). On the same year\, SIMMAM was presented to the t hen General Coordination of Oil and Gas (Coordenação Geral de Petróleo e Gás – CGPEG)\, current General Coordination of Marine and Coastal Ent erprises (Coordenação-Geral de Licenciamento Ambiental de Empreendimento s Marinhos e Costeiros – CGMAC)\, that used it to aggregate and organize marine mammal sighting data generated by marine mammal observers [Barreto et al.\, 2019\; Britto\; 2009]. Presently\, sighting data are regularly u ploaded to SIMMAM directly by the licensed companies. \nAs of June 2024\, SIMMAM has 423 active users and holds 75\,340 aquatic mammal records. Of t hese\, 61% records are private\, but this proportion is very different dep ending on the type of record. For strandings\, that are in most part submi tted by research institutions\, 91% are private as they are the results of individual efforts. But for sightings 61% are public\, as they come mostl y from the oil industry as part of the environmental licensing of their op erations\, and they mirror the public reports that have been delivered to IBAMA. As mentioned before\, all the data held in the SIMMAM database\, re gardless of its public availability\, can be seen by Brazilian environment al agencies (IBAMA and ICMBio). \nThe option to allow government agencies to use the whole dataset is extremely important for management purposes\, as it enables environmental agencies to use even unpublished data generate d by research institutions. But as the data is not available for the gener al public\, it does not compromise their future use in academic publicatio ns. Also\, a limited visualization of private data in the WebGIS\, where d etails of the record such as species and date are not shown\, serves as an indication for other researchers that a specific institution has data on marine mammals in a specific area\, fostering collaborations among institu tions.\nWe believe that presenting this work at FOSS4G 2024 will allow us to discuss SIMMAM with the geospatial community and receive input to furth er improve the system. It shows a successful implementation of open geospa tial technologies that is being used both by government and the academic c ommunity. DTSTAMP:20260422T231510Z LOCATION:Room V SUMMARY:SIMMAM 3.0 – Updating the Toolbox for the Conservation of Marine Mammals - Alencar Cabral URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/WRGQFQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-2024-academic-track-XMAGHX@talks.osgeo.org DTSTART;TZID=-03:20241204T174500 DTEND;TZID=-03:20241204T181500 DESCRIPTION:## 1. Introduction\nUrban transportation is transforming with a focus on sustainability and smart city initiatives. Cycling\, a key eleme nt of sustainable urban mobility\, needs robust infrastructure and reliabl e data for growth and integration into city planning. Despite advancements in sensor technology and (geo)data analytics\, there is a gap in comprehe nsive collection and use of cycling-specific environmental\, safety\, and pathway data.\n\nOne major deterrent for citizens using bicycles are the p erceived dangers in traffic. Identifying insecure sections is crucial to i mproving cycling infrastructure. Safe countermeasures can change negative perceptions and promote cycling as a safe and sustainable mode of transpor t. Traditionally\, only actual crashes are included in official data\, inf orming city planning decisions. However\, analysing high-risk occurrences like near-miss incidents\, which greatly impact the perceived danger\, can provide a more accurate understanding of cycling safety.\n\nThere already exist a number of projects using different technologies to gather and pro vide data on bicycle safety and urban mobility\, but combining environment al and road safety aspects is unique. Projects examining cyclist safety\, particularly dangerously close overtaking manoeuvres\, often involve remot e data processing with machine learning or human analysis. Using live vide o or images from bike-mounted smartphones is effective but creates data ov erhead and privacy concerns. Additionally\, microcontroller-based sensing systems can be complex to assemble\, requiring technical skills and specia l equipment.\n\nThe objective of this work is to address the aforementione d gap by developing an innovative bicycle sensor system that leverages emb edded artificial intelligence (AI) to process sensor data on the device. T his approach has the potential to reduce data overhead and address privacy concerns while simultaneously providing actionable insights. Our work has the potential to make significant contributions to traffic and transport planning by providing valuable insights into traffic patterns and road saf ety concerns using extensive spatial datasets gathered by citizens.\n\n## System Design\nAt the core of our system is a microcontroller unit (MCU) o f the senseBox family. The senseBox is a versatile\, open hardware electro nics kit specifically designed for citizen science projects and educationa l initiatives\, with an emphasis on environmental monitoring and data coll ection.\n\nThe following environmental sensors are used:\n- Temperature & rel. Humidity (HDC1080)\n- Particulate Matter (SPS30)\n- Acceleration (MPU 6050)\n- Time-of-Flight (ToF) ranging (VL53L8CX)\n\nMoreover\, battery man agement\, Bluetooth Low Energy (BLE)\, and OLED-Display modules are includ ed for connectivity and user feedback. All parts fit into a custom designe d\, 3D printed enclosure which is attached to the seat post of a bicycle.\ n\nThe device is communicating with an open source smartphone app using BL E which receives sensor data and combines them with geolocation data. Data sets are recorded and saved on the smartphone\, but can also be uploaded t o openSenseMap as open data during the ride. Users can control levels of p rivacy (e.g. by setting privacy zones) to foster digital sovereignty. \n\n ## 2.1. Machine Learning on the Bike\nWe are introducing two approaches to utilise machine learning capabilities using Tensorflow Lite on the sensor device: overtaking detection and road surface / quality classification. B y processing the data directly on the device instead of sending it to larg er servers\, bandwidth and energy consumption is kept minimal.\nIn the low resolution depth images recorded by the 8x8 multizone ranging ToF sensor\ , overtaking vehicles can be detected using shallow neural networks. This has already been described and implemented as a standalone solution in (Sc harf et al.\, 2024)\, but for integrating it into the mobile sensor system some considerations for available processing capacities\, suitable infere nce times and necessary accuracies will be addressed as part of this work. \nTo classify the road surface and its quality\, the acceleration sensor will be used. While raw acceleration values can identify the roughness of a road\, surface classifications and quality estimations can reveal deviat ions from intended surfaces to actual surfaces. Using acceleration values and geolocation data\, we will explore training a machine learning model u sing OpenStreetMap Surface information as ground truth data.\n\n## 3. Work shops\nEngaging citizens in data collection\, problem identification\, and the construction of sensor stations empowers them and fosters the generat ion of new ideas. Our solution is a solder-free\, easy-to-assemble mobile sensor device. We conduct a workshop in São Paulo\, Brazil\, where 20 par ticipants build and mount their own mobile sensor device on bicycles. Afte rwards\, they collect environmental and bicycle-specific sensor data. Foll ow-up workshops in Münster\, Germany will allow the comparison of the con trasting bicycle infrastructures in these cities\, as well as the general urban environment differences\, and will provide valuable data and insight s into participants' perceptions.\nThis collaborative effort enhances part icipants' understanding of scientific methods and urban mobility challenge s while ensuring that the collected data reflects cyclists' authentic expe riences. By involving citizens as active contributors\, we aim to bridge t he gap between scientific research and community needs\, fostering a more inclusive and participatory approach to urban mobility solutions.\nAfter t he workshops we conduct user studies with the workshop participants on the following topics:\nUsability: Through surveys at the end of each session and interviews\, participants provide feedback on assembling\, mounting an d connecting the bicycle sensor device. \nTrust in Data: Participants revi ew the data of their recorded dangerous takeovers and road surface types a nd compare its accuracy with their own perceptions.\n\n## 4. Conclusion an d Future Work\nThis comprehensive evaluation aims to provide a thorough un derstanding of both the user experience and the technical performance of t he system\, ultimately guiding the data-driven foundation for improvements in urban mobility solutions. Insights gained from this work will inform f uture iterations of the project\, ensuring the system collects high-qualit y data and meets the needs of cyclists\, thereby effectively enhancing urb an mobility and road safety not only for cyclists but for all users of the urban mobility system. Future works will include the development of an op en source bike-related data analysis platform as a recommender system for bike infrastructure measures in cities. DTSTAMP:20260422T231510Z LOCATION:Room I SUMMARY:Urban Cycling: Intelligent Bicycle Sensors for Road Safety and Sust ainability - Felix Erdmann\, Luis Fernando Villaça Meyer\, Beatriz Gonça lves URL:https://talks.osgeo.org/foss4g-2024-academic-track/talk/XMAGHX/ END:VEVENT END:VCALENDAR