This workshop offers a comprehensive introduction to utilizing Python programming for geospatial analysis and visualization. Geospatial data is essential in various domains such as environmental sciences, urban planning, agriculture, and disaster management. This workshop aims to equip participants with foundational skills to harness the power of Python libraries and tools for handling, analyzing, and visualizing geospatial data.
By the end of the workshop, participants will have a solid grasp of the core principles of geospatial data handling using Python. They will be empowered to create their own geospatial projects, capable of ingesting, analyzing, and visualizing spatial data to derive meaningful analysis.
This workshop will provide an introduction to performing common GIS/geospatial tasks using Python geospatial tools such as OWSLib, Shapely, Fiona/Rasterio, and common geospatial libraries like GDAL, PROJ, pycsw, as well as other tools from the geopython toolchain.
In the era of cloud computing, it is important to have a good understanding of cloud-optimized geospatial technologies in order to carry out earth observation tasks effectively. This workshop, led by SI Analytics' experienced engineers, will provide valuable insights about the current status of cloud-native earth observation technologies.
During the workshop, we will delve into the details of cloud-optimized GeoTIFF (COG), spatio-temporal asset catalog (STAC), TiTiler, and Leafmap. These technologies have the potential to simplify earth observation workflows and make them more efficient. Specifically, we will cover the efficient storage and access of earth observation data using COG and STAC, dynamic tiling using TiTiler, and interactive map visualization using Leafmap.
This workshop is designed for researchers and engineers who are interested in improving their earth observation tasks by using cloud-native geospatial tools. While a basic understanding of Python programming, remote sensing, and GIS is assumed, attendees will have the opportunity to learn and enhance their skills throughout the workshop.
In this workshop we will first show you how to get started with plugin installation and plugin development using PyQGIS and related Python packages in QGIS for automatic map generation.
If the participants can follow me, we will follow these instructions.
1. Plugin installation and plugin builder configuration
2. Write simple Python code to upload a shape or geo package or PostGIS layer to QGIS desktop and add it to the plugin with the appropriate PyQT design.
3. some symbols and map creation and creating a special button in plugin to do such actions automatically.
I will consider this workshop for beginner and intermediate level, but understanding of QGIS is necessary.
In the era of geospatial data-driven insights, the ability to efficiently retrieve and visualize environmental data is crucial for informed decision-making. This session, titled "Environmental Data Retrieval (EDR) for Visualizing Coverage JSON on Maplibre-gl," aims to delve into the process of creating EDR APIs, generating Coverage JSON files, and seamlessly visualizing them on the Maplibre-gl platform.
The Learning Objective of this session is "understand how to create your own high quality artisinal point clouds, pixels, and triangular meshes with low-cost hardware and open-source software".
OpenDroneMap is free (libre) and open source photogrammetry toolkit targeted at low flying aircraft and other photogrammetric imagery processing. For the first half of the workshop, we will go over drone mapping basics: hardware, software, and best practices.
The second half of the workshop focuses on software processing of images from the drone in order to make the images usable as geospatial datasets. We'll take users through installation and use of OpenDroneMap, WebODM, and the processing of datasets, settings, and options therein, and discuss and demonstrate options for hosted and scalable OpenDroneMap solutions.
GeoServer is a much loved open-source project and one of the most popular web mapping services in the world. This workshop provides a gentle hands-on introduction in setting up and enjoying GeoServer.
This workshop covers the advantages of using GeoServer; looking at the abilities of this open-source technology.
We will start with a demonstration of geoserver installation and touch on system requirements and installation of extensions
Hands-on publication of spatial data (vector, raster and database).
GeoServer styling and web mapping use
Preflight check-lists making sure your datasets, and web services, are ready for use.
Demonstration of one-click-publishing from Desktop GIS using geocat-bridge tool.
For those migrating from ESRI software this workshop offers an opportunity to explore foss4g similarities and differences.
This session is a great way to get started, geared towards those with no prior open source experience. Familiarity with GIS concepts is recommended for attendees, and you are welcome to bring your own data.
This is a hands-on workshop: If you are comfortable installing software on your laptop you are encouraged to do so. If you can not install it is okay - GeoCat will provide a hosted GeoServer (so only a browser is required to attend).
Moving feature data can represent various phenomena, including vehicles, people, animals, weather patterns, and more. Conceptually, Moving Features are dynamic geospatial data that change location and possibly other characteristics over time.
OGC API – Moving Features (OGC API – MF) provides a standard and interoperable way to manage such data, with valuable applications in transportation management, disaster response, environmental monitoring, and beyond. OGC API – MF also includes operations for filtering, sorting, and aggregating moving feature data based on location, time, and other properties.
This workshop will get you started with OGC API – MF and its implementation in MF-API Server that is based on pygeoapi and MobilityDB, covering the following questions:
- What is the core concept of OGC API – MF (and OGC MF-JSON format)?
- How to implement OGC API – MF with pygeoapi and MobilityDB?
- How can we visualize its results with STINUUM (with CesiumJS)?
- How can we implement a new feature that hasn't been implemented yet?
The below open sources will be used in this workshop:
- MF-API Server based on pygeoapi: https://github.com/aistairc/mf-api
- OGC API – Moving Features official GitHub repository: https://github.com/opengeospatial/ogcapi-movingfeatures
- MobilityDB (and its Python driver, PyMEOS, and MEOS): https://github.com/MobilityDB
- STINUUM: https://github.com/aistairc/mf-cesium
The installation of each program will use a Docker file.
Lastly, you can check many helpful information about OGC API – MF here: https://ogcapi.ogc.org/movingfeatures/
FOSS4G-Asia 2023 Seoul Opening Ceremony
- Opening Remark by Co-chairs: Ki-Joune Li and Maria A. Brovelli(5 minutes)
- Welcoming Address by Seoul Metropolitan Government, Miyeon Seo(5 minutes)
- Welcoming Address by Ministry of Defense, Korea
A talk for UN Open GIS Joint Workshop
Working with data has many challenges on it’s own and when you add a geospatial factor into makes it even harder. In my belief data never lives in isolation, it always exists in relation to something (sorry NoSQL) and PostgreSQL provides a great starting point as our not just geospatial database but database for everything.
In this talk we’ll talk briefly about long history of postgres and how it’s ecosystem can help you build a database for all your needs. In particular we’ll look at extensions like:
- PostGIS (obviously)
- pg_raster (for handling rasters)
- timescaleDB (for handling time series data)
- uber h3 (h3 indexing for your geospatial data)
- ogr_fdw (OGR FDW allows you to connect to any OGR supported data source.)
- pg_eventserv, pg_cron, pg_ivm and many more will be referenced
Moreover we’ll look at how you can combine some extensions like (postgis[spatial] + timescaleDB[temporal]) to create something that fits your use case.
Now we have designed our database but it’s time to deploy it on the cloud, let’s look at available solutions which includes from self-managed to service based and everything in between.
- Managed services like - AWS RDS. Aurora, CrunchyData, EDB, Timescale
- Deploying your own using Linux Instances
- Deploying on K8s clusters using postgres operators
We’ll use OSM data as our sample datasets plus more as needed.
North Nashville, historically marginalized, faces pronounced health disparities, especially in hypertension and diabetes. Socioeconomic factors intensify these health challenges, with food accessibility emerging as a pivotal determinant. This FOSS4G 2023 presentation underscores the role of community mapping in addressing these disparities.
While food accessibility—defined by the availability and affordability of nutritious options—shapes community health, traditional USDA methods, like food desert mapping, might not fully encapsulate challenges in areas like North Nashville.
Our solution is the North Nashville Community Mapping Project, a participatory initiative leveraging QGIS and Google Maps, local insights, and community engagement. This project, detailed at https://www.communitymap.net/nashville-food, integrates geospatial data with community feedback, offering a holistic view of North Nashville's food landscape. Conducted on April 15th, 2023, participants engaged in mapping, workshops, and surveys.
The data will guide strategies to enhance food accessibility and health in North Nashville. In essence, we aim to spotlight community mapping's potential in mitigating health disparities and championing health equity in marginalized regions.
Welcome to GeoNetwork and FOSS4G! GeoNetwork is a leading open-source web catalog for keeping track of the spatial information. For those transitioning to FOSS4G from an integrated environment we help explain how everything fits together.
This orientation session introduces GeoNetwork (as a software component), providing a high-level view to explain how foss4g software is intended to work together. If you are transitioning from an ESRI environment this critical talk to attend as open source technology is often presented in isolation.
This presentation covers:
We look at what GeoNetwork is for, the business challenge it is faced with, and the amazing technical approach taken by the technology.
We will show the the publishing workflow to see what is required, and look at how harvesting can jump start your catalog contents
We peek under the hood at how the editor works, and discover the central super-power of GeoNetwork
Look at examples of how GeoNetwork has been extended by organizations to see what is possible with this technology
GeoNetwork is an established technology - recognized as an OSGeo project and member of the foss4g community for over a decade. We would love to welcome you to the conference and share what this project has to offer.
A talk for UN Open GIS Joint Workshop
In the past, the installation of GIS applications often encountered challenges regarding the flexibility of services, which were unable to be scaled to accommodate a growing number of users. The interconnection and exchanging of data across services were constrained, and service separation was not feasible. These issues had a significant impact on overall usability.
The design and deployment of applications in the form of microservices are gaining popularity and widespread adoption. This approach aims to provide flexibility to the installation process, allowing services to be added or reduced as needed to align with usage requirements. It can subdivide services into smaller units to facilitate installation, following the principles outlined in The Twelve-Factor App (https://12factor.net/).
Nowadays, GIS application development has OGC Standards, which are standardized guidelines that define the process of storing and providing geospatial data. These standards encompass a multitude of aspects of geospatial information interoperability. Therefore, the principles of The Twelve-Factor App can be adapted to the design and deployment of GIS applications, ensuring compliance with OGC Standards.
This session will elaborate on how the principles of The Twelve-Factor App can be harmonized with OGC Standards, as well as the various technologies selected for the design and deployment of applications.
OpenStreetMap (OSM) is a well-known crowdsourced map collaboratively created by global communities. The data production of OSM relies on a geographical data model enabling volunteers to collaboratively contribute their data on the single platform. The data production mechanism of OSM actually gets attention from map authorities which attempt to move towards a collaboration-based framework. However, the data production of map authorities is required to be compatible with institutional policies and standards. The top-down approach of map authorities is actually different from the bottom-up approach of OSM. There is a gap between OSM’s collaborative model and the institutional model of map authorities. To embed the OSM framework into the production mechanism of map authorities, the research starts with investigation of the differences in feature models between OSM and the map authority of Taiwan. According to the investigation of the feature models, data transformation tools have been designed to transfer institutional data to OSM. The data transformation enables the institutional data providers to contribute their data on the OSM-based map platform. The institutional data providers can be surveyors of the map authority or other agencies taking charge of geographic data such as river, land covers, forest, etc. This OSM-based collaboration model is actually created for instructional data contribution but not for crowdsourced data contribution. Moreover, the insertion of the OSM mechanism in map authorities certainly has impacts and conflicts with institutional data production. This research analyzes the possible impacts and conflicts. It would be valuable for map authorities to adopt the collaborative model for their data production.
GEOS WASM project (https://github.com/chrispahm/geos-wasm) was started by @chrispahm from the following issue.
- geos-wasm - Is it worth the effort? · Issue #2 · tmcw/geos-wasm
I noticed the project from the following @kylebarron's blog article,
- Thoughts on GEOS in WebAssembly | Kyle Barron
then started some contribution.
I am just one contributor, but I will explain the project status and current my trial of tester app (https://sanak.github.io/geos-wasm-tester/, now), then talk further topic about WebAssembly power for geospatial analysis.
The SpatioTemporal Asset Catalogs (STAC), introduced in 2017, is an open standard for organizing and sharing geospatial data, and its usage also conforms to FAIR principles for data. Consequently, many organizations have adopted the STAC specification as a standard for implementing and providing their data in STAC format. As a means for spatial temporal asset indexing, STAC is presented in JSON or GeoJSON formats, enabling interoperability with various services and facilitating comprehension for both machines and humans. STAC is not constrained to specific types of geospatial data; it can be utilized to catalog a diverse range of assets, from satellite imagery to environmental datasets. Through a well-designed structure, it can be integrated into various applications, allowing organizations to harness existing datasets for diverse purposes. STAC also possesses the capability to search for metadata describing assets based on time and location, rendering spatial temporal assets easily discoverable and accessible.
In Thailand, STAC is applied in various scenarios, including the GISTDA Decision Support System for Disaster Management Platform. In the platform, STAC catalogs vector data related to flooding areas, thermal activities, and drought indices. Therefore the implemented application can efficiently browse and retrieve data from the STAC catalog, enhancing data retrieval speed and user experience. Similarly, the GISTDA Satellite Platform utilizes STAC to catalog satellite imagery. In the implemented application, users can search for desired satellite images based on time, location, and conditions. This streamlined approach simplifies the process, allowing users to find the images they need quickly and easily.
11/29 Seoul Geospatial Forum
A talk for UN Open GIS Joint Workshop
We created a REST API to register, search, and delete spatio-temporal data based on OGC API-Features, an international standard specification of the Open Geospatial Consortium (OGC), an international standardization organization for geospatial information, and the Moving Feature Encoding Extension-JSON (MF-JSON) specification, an international OGC standard specification developed mainly by AIST.
To create the API, we built an OGC-API server using pygeoapi and PyMEOS.PostgreSQL was used as the DB, and mobilityDB was used as an extension library for storing spatio-temporal data.For spatio-temporal data were stored using MobilityDB-specific type formats (TBool, TText, TInt, and TFloat).When converting MF-JSON for spatio-temporal data using PyMEOS functions, some tag names were not supported for conversion.Therefore, we implemented an additional process to convert the tag name to one that can be successfully registered before executing the PyMEOS function.
Also, at the time of this implementation, there were no source code modification guidelines for pygeoapi, so the API implementation was realized by directly modifying the source code of the lib directory that handles the API request processing.
Hydrological models play a pivotal role in mathematically simulating the complexities of real-world hydrological processes. However, accurately capturing the intricate physical nature of basin hydrology through parameterized representations remains a formidable challenge, particularly in regions with limited or unavailable gauge sites, a prevalent scenario in developing nations and remote locales. A notable trend has recently emerged where remote sensing inversion products or reanalysis data, such as evapotranspiration, soil water, and leaf area index, are increasingly used as substitutes for ground truth observations in the calibration of hydrological models. But this introduces a considerable risk when directly employing them for hydrological modeling as these products inherently harbor unknown errors.
This study intends to address the above challenges by proposing an adaptive procedural fitting algorithm to support multivariate calibration, employing an iterative stepwise approach. The algorithm simplifies the hydrological model into a directed graph, with each node representing a subprocess in the hydrological model. This allows for quantifying the impact of a parameter on each subprocess by considering the traversal relationship between the subprocess to which the parameter is attached and other subprocesses. Consequently, the iterative calibration process enables the updating of parameters at different levels or learning rates. Furthermore, two categories of methods were introduced to evaluate the outputs of subprocesses relative to their calibrated datasets: ground truth observations and non-observation data with unknown errors. One category employs standard metrics to evaluate the simulated data against observations, while the other utilizes multiple collocation methods to evaluate the simulation with the data with unknown errors. The proposed multivariate calibration framework offers an innovative approach to hydrological modelling, facilitating the effective utilization of increasingly abundant remote sensing products and reanalysis data to enhance the accuracy and reliability of hydrological models.
The presented approach has been realized through the utilization of an open-source GIS solution, integrating tools such as Python, xarray, geopandas, multiprocessing and SWAT to form an efficiently processed parallel program. The demonstration was applied in a case study conducted within the Malian river Basin situated in Gansu Province, China, involving the integration of remotely sensed ET products and gauged streamflow datasets to showcase the applicability and effectiveness of the proposed method.
Ecosystems in desert areas are very sensitive and their protection is necessary. Nebakhs are one of these ecosystems in many parts of the world. These phenomena have a special importance in preventing desertification and their proper monitoring and management can be a way to preserve them. However, the difficulty of conducting field surveys and measurements in desert areas can hinder the collection of accurate, up-to-date and comprehensive information from these areas. Volunteered geographic information (VGI) from cheap drones and their operators can be very useful in this regard. But on the one hand, these drones usually only have RGB sensors, and on the other hand, the people who work with these drones do not have much access to commercial software. Therefore, using RGB-based indices and open source software can help a lot. In the current research, QGIS open source software is used to calculate RGB indices including NRI and RBD, NBI, MGRVI, GRRI, RGBVI, NGI, EXG, GLA, etc to extract information related to Nebkhas including canopy area and so on. The results show that UAV images and RGB indices, especially RGBVI, NGI and EXG, can help to extract properties of Nebkhas. The use of VGI data in the form of cheap UAV images and RGB indices in the framework of open source software has the ability to improve the process of monitoring and managing sensitive desert ecosystems such as Nebkhas in a good and accurate way.
The OGC SensorThings API is one of the OGC API Standards introduced in 2016. Its primary goal is to standardize and provide a unified approach to managing and interacting with Internet of Things (IoT) sensor data within a geospatial context. This standardization is essential for applications that rely on location-based data, including environmental monitoring, smart cities, precision agriculture, and asset tracking.
Nowadays, there is an increasing adoption of IoT (Internet of Things) technology which facilitates the connection of devices to the cloud, making it more convenient to monitor information originating from these devices, thereby unlocking the value of incoming data over time. The OGC SensorThings API serves as an optimal choice for managing IoT data, enabling seamless interaction and analysis of observations.
In the context of the SensorThings API, an IoT device or system is conceptualized as a "Thing," which has a specific "Location" and can link to one or multiple "Datastreams." Each "Datastream" observes an individual "ObservedProperty" through a corresponding "Sensor," generating numerous "Observations." Each "Observation," measured from the "Sensor," pertains to a distinct "FeatureOfInterest." Collectively, these interconnected relationships establish a flexible and standardized approach for describing and modeling diverse sensing systems.
The SensorThings API contains a comprehensive Create-Read-Update-Delete (CRUD) endpoint, facilitating a user interface where users can effectively manage device data. To enhance convenience and facilitate streamlined management of device-related information, a management interface, whether in the form of a website or an application, significantly contributes to the ease of data management.
This talk is about the current state of MilMap and its ongoing development. MilMap is a military geo-portal system widely and successfully used in every sectors of Korean military. The system is now undergoing major upgrade from geo-portal to military digital twin system.
MilMap is developed on top of numerous open source projects such as PostGIS, GeoServer, GeoWebCache, Cesium, OpenLayers, mago3D, OpenGXT. The system provides several functionalities like POI search, geospatial data search, layer control, satellite image search and download, spatial terrain analysis, coordinates reading, and map notes, to the military officers through the intranet. Although the system provides geospatial analytics functions through OGC WPS(Web Processing Service), the current system is basically a web based 3D GIS for data viewing and printing. Thanks to MilMap, military officers can now access the huge amount of geospatial data(maps, imagery, 3D, POI, and others) in their browser without installing additional software.
MilMap is now undergoing major development to be a more customized, automated, and analytical system. The future MilMap will support user data uploading for intelligence sharing, more bespoke battle field analysis and others. In the long run, MilMap is expected to be a cloud based military digital twin system for geospatial intelligence sharing and battle field analysis & simulation.
This research is financially supported by the Institute of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government under grant No. UM22402RD4.
We, GSI, are distributing the web map data "GSI Tiles" as open data and are also releasing the web map site "GSI Maps" using FOSS as open source under the open policy in order to make our national geospatial information available to as many people as possible.
In this effort, while we have so far continued to provide a stable supply of map tiles in image format mainly, we are also interested in promoting map tiles that can be flexibly used at a lower cost. To this end, in recent years, we have released vector tiles on a test basis and tried building a scheme from production to consumption of vector tiles with the use of FOSS.
Remote sensing evapotranspiration (RS-ET) products have been the ubiquitous datasets of echo-hydrology, increasingly employed in precise hydrological modeling. However, these diverse ET products often offer considerable uncertainties, posing substantial challenges when incorporating them into hydrological models. This study aims to investigate the effects of integrating remote sensing evapotranspiration (RS-ET) products into the calibration of hydrological models. Specifically, we address two main questions: (1) among those RS-ET products considered, which offers the highest hydrologic predictability for hydrological modeling? (2) What is the optimal scale for incorporating RS-ET datasets into the hydrological model to achieve maximum benefits for enhancing hydrological modeling accuracy?
We conducted an in-depth exploration of these questions by implementing a hydrological modeling experiment using the Soil and Water Assessment Tool (SWAT) within the Meichuanjiang Basin in Jiangxi Province, China. This research employed four distinct RS-ET products: MOD16, GLASS, SSEBop, and ETMonitor, along with a merged product. Additionally, we examined three distinct scales for aggregating RS-ET datasets: subbasin (SUB), land use (LU), and hydrological response unit (HRU).
Our findings revealed discernible variations in the evapotranspiration (ET) simulations of SWAT models across the study area, upon the calibration with five distinct products. Notably, GLASS and the fusion datasets demonstrated superior ET simulation performance with best scores, evaluated by a Kling-Gupta Efficiency (KGE) both surpassing 0.6. However, SSEBop yielded the lowest KGE of approximately 0.5. This study further highlighted that while the accuracy of ET simulations was enhanced, this improvement incurred a marginal cost in terms of a slight reduction in streamflow precision. However, this compromise was deemed acceptable due to its role in contributing to a more accurate spatial representation of catchment behavior while mitigating equifinality.
Similarly, at different scales of the same ET dataset used in calibration, although the differences in simulation results might be minimal, each dataset seems to have a specific optimal input scale. Notably, during the simulation of the evapotranspiration hydrological component, there is a particularly significant observation: the land use (LU) or subbasin (SUB) scale often proves to be more effective. The rationale for selecting the LU scale lies in the strong correlation between ET and various land use types. By analyzing each land use type separately, we can more accurately capture their distinct ET patterns, leading to improved simulation outcomes. Conversely, the enhanced effectiveness of the SUB scale can be attributed to the scale aggregation effect.
Considering that different RS-ET datasets and aggregation scales introduce uncertainty in observation data for calibration, we infer that the uncertainty in observation data gives rise to simulation uncertainty in inversing SWAT, subsequently backpropagating into parameter uncertainty. These findings shed light on the critical significance of carefully considering the quality of RS-ET datasets and the chosen aggregation scale in the context of hydrological modeling.
In this talk we discover various aspects of styling in Geoserver. It allows us to style using various languages such as SLD, YSLD, CSS, etc. along with ability to create style using no-code way ( Geostyler ) .
In this talk we explore how to style Point, LineString, Polygon, Rasters. We'll cover following tasks
1. Attribute based Styling
2. Zoom based Styling
3. Variable Styling
4. Labelling and it's optimisation
This presentation introduces Scan data to Building Information Modeling (BIM) in 3D Geographic Information System (GIS). The focus of the methodology is on automatically generating building mass and facade infor-mation on the GIS platform using 3D Point Cloud Data (PCD). Advanced scanning techniques capture detailed geometry from the physical site and generate high-resolution point clouds, which are processed to create 3D models for GIS integration.
A talk for UN Open GIS Joint Workshop
Air pollution poses a significant health threat worldwide. As per a 2022 Lancet report (Pollution and health: a progress update), it accounts for 1 in 6 premature deaths, making it the largest environmental risk factor for disease and premature mortality. This impact is especially severe for residents of low and middle income countries. Despite being pervasive, air pollution is often accepted as an unavoidable reality, forcing us to endure its detrimental health effects.
How can we effectively bridge this gap and nudge both public and policy makers alike to take action on the silent killer that is air pollution?
The AQLI tool is a free and open source interactive tool that attempts to address this global health crisis by estimating the relationship between PM2.5 and Life Expectancy allowing anyone to interactively visualize and explore the average gain in life expectancy they could experience if their community met the WHO PM2.5 guideline. AQLI also publishes an Annual Report and various regional factsheets. All of these resources, alongside our methodology, can be found on our website (https://aqli.epic.uchicago.edu/).
So far, the AQLI tool has been used in 216 countries by over 300,000 users. This user base includes (but is not limited to) policy makers (e.g. Indian Member of Parliament, Vandana Chavan used the AQLI tool to demand a change in the India Air Act, 1981), air pollution activist groups (e.g. the Thailand Clean Air Network using AQLI to push for Clean air law in Thailand), environment conservation societies, medical professionals, geospatial community, academic researchers, teachers, students from both schools and colleges. In 2022 alone, AQLI has been cited in the New York Times, The Guardian, The Washington Post, Reuters, the Times of India (the largest selling English-language daily in the world), notices of National Human Rights Commision in India and many other outlets globally.
The AQLI geospatial data processing pipeline combines three free and open source datasets using the R programming language. It overlays high-resolution population (landscan: https://landscan.ornl.gov/) and pollution rasters (ACAG: https://sites.wustl.edu/acag/datasets/surface-pm2-5/) onto a rasterized global shapefile (GADM: https://gadm.org/). The resulting data is collapsed to generate regional datasets for further analysis..
Over the last year in our move towards fully open source, we have worked on a complete transition of our current codebase to R, all of which can be accessed via our organizational GitHub page (https://github.com/aqli-epic). It’s easier now more than ever for anyone to explore both AQLI’s underlying geospatial data pipelines and its finished results (e.g. aqverse R package: https://github.com/aqli-epic/aqverse).
We have witnessed first hand the ability of a publicly available geospatial tool in raising awareness about a major health crisis. In the coming months, we are planning to add additional layers, show higher resolution data among other things and in doing so both contribute towards and leverage from the wider foss4G community.
The tropospheric delay is a source error for positioning using the Global Navigation Satellite System (GNSS). The signals of GNSS transmit the troposphere to the receiver, which are usually represented by zenith tropospheric delay (ZTD). ZTD can be estimated by using the Precise Point Positioning (PPP) method. Currently, there several positioning software packages to calculated PPP and estimated ZTD values by using GNSS data. This study aims to estimate ZTD value using PPP technique and analyze different results. Bernese, goGPS, and Net_Diff GNSS processing software are used to process GNSS data. To analyze the positioning PPP values, we compare between N/E/U components of coordinate estimated and referent values. Different ZTD values are investigated by using statistical basic and Pearson correlation analysis. The positioning result show PPP values calculated by Bernese software is very stability with standard deviation about < 1 cm. The Net_Diff packages can estimate PPP coordinate of N/E/U components are least error values which RMSE are <13 cm, < 4 cm, and <9 cm respectively. The different ZTD values from three software packages at DSNI and LSN2 stations show very high relationship in near perfective value that is (R > 0.90).
In community-sourced data platforms like OpenStreetMap (OSM), the significance of localizing map data transcends language—it's about fostering inclusivity, preserving cultural identity, and amplifying local voices on a global canvas. This talk explores the pivotal role of map data localization, focusing on the inspiring context of OSM Nepal, and introducing OSMLocalizer as a game-changing solution.
Localization breathes life into maps, making them relatable and accessible to diverse communities. At the heart of this concept lies OSM Nepal's campaign to localize place names, which exemplifies the essence of this endeavor. However, the process posed challenges as there were no dedicated tools specifically designed for map data localization. Contributors had to rely on existing tools like MapRoulette, causing inefficiency as they had to juggle multiple tabs between editing tools, Project Management, and translation engines. Here, OSMLocalizer emerges as the beacon of efficiency and integration.
OSMLocalizer's approach is simple yet revolutionary. With a built-in editor, contributors can seamlessly edit and localize map features within the same interface, eliminating the need to switch tabs. Further streamlining the process, OSMLocalizer integrates translation engine APIs, making translations accessible within the tool. This holistic approach transforms map data localization from a tedious chore to an engaging and intuitive experience.
The context of OSM Nepal accentuates the relevance of such a tool. As contributors passionately pursued localized place names, OSMLocalizer bridged the gap between editing, translation, and seamless contributions. This talk will present OSMLocalizer as a catalyst for efficient and community-centered map data localization.
In a world where maps connect people and places, this presentation delves into the heart of map data localization and its profound impact. By weaving OSM Nepal's narrative into the equation and introducing OSMLocalizer's innovation, we aim to inspire a new era of map contributions that resonate deeply with local communities worldwide.
Of India's 1.4 billion people, 64.3% live in rural areas. Under the Svamitva Scheme (https://svamitva.nic.in/), the Govt. of India has set up an ambitious target to map India's 568k villages using drones. Based on the orthomosaics prepared from the drone surveys, the government is going to distribute property cards to India's millions of rural residents. Aereo (previously Aarav Unmanned Systems Private Limited https://aus.co.in/) is playing a pivotal role in implementation of this scheme, having completed drone surveys for more than 60k villages. The Aereo Cloud platform allows for scalable orthomosaic generation, QC and digitization using the open-source PostGIS database and the Cloud Optimised GeoTIFF format. Building Aereo Cloud using open-source technologies such as PostGIS, COG, geodjango and gdal has allowed Aereo Cloud to build scalable, robust and secure solutions for drone-based mapping quickly as per market demand.
The question “How can Mongolia, which has a huge amount of renewable energy resources, develop “green” sources of energy in accordance with the specifics of its’ climate and energy consumption?” has become a pressing issue in the energy sector. There is an urgent need to take comprehensive measures to reduce air pollution in Ulaanbaatar, where half of the country's population lives and which has a coal-dependent energy supply. Within this, the research to create sustainable and environmentally friendly renewable energy has become a priority.
About 48 percent of air pollution in Ulaanbaatar comes from households in ger districts. To reduce this amount, the need to use solar energy for their heating is required, so it is practical to estimate the potential amount of solar energy based on digital surface models.
In this study, we used Geographic Information System (GIS) to calculate the potential amount of solar energy resources in 6 districts of central Ulaanbaatar using a digital elevation model (DEM). The DEM was calculated from a topographic map with a scale of 1: 25000. In addition, objects such as buildings and trees were added to the Ulaanbaatar city database, and a numerical surface model was developed digital surface model (DSM) and used for further processing. The results were compared with the results of previous research. The results of this work are in line with previous estimates from meteorological data surveys. In the latitudes of Ulaanbaatar, the sunshine lasts 2,800 hours a year, which is relatively longer than in the highlands. The sun shines 145-170 hours in winter, 276-300 hours in spring for 3-4 months, 290-300 hours in summer, and 225-250 hours a month in autumn and summer.
According to the study, the potential amount of solar energy resources was calculated on a total area of 206,705 hectares, and the maximum area that could store 1,200-1,500 kWh / m 2 of energy per year was 47,313 hectares. Excluding areas where it is not possible to build renewable energy facilities, such as sanitary restrictions, forests, cemeteries, farmland, special needs areas, reservoirs, waste disposal sites, and airport protection zones, 1100-1500 kWh per year. * 17,000 ha of potential energy storage area is within the boundaries of the construction restriction. This research is an unprecedented work in our country, as it calculates the potential amount of solar energy resources using a digital model of the surface. In addition, it provides an opportunity to use renewable energy for future planning and research.
In the policy of Mongolian green development main target is to reduce greenhouse gas and supporting to new friendly-environment energy sources. According to this policy, renewable energy will reach 30% in 2030 including all energy industries. This research has estimated solar radiation in Ulaanbaatar city using the Digital Elevation Model (DEM), Digital Surface Model (DSM), and building height information
Vector tiles are changing the way we create maps. Client-side rendering offers endless possibilities to the cartographer and has introduced new map design tools and techniques. Let’s explore an innovative approach to modern cartography based on simplicity and a comprehensive vector tiles schema. Take a tour of vector tiles cartography basics and learn about the latest trends through a number of examples illustrated with the MapTiler maps. Get an overview of best practices and learn about simple open-source recipes, towards advanced combinations of fills, patterns, fonts, and symbols. Selected layer parameters and style expressions will be discussed in a visual way and explained with basic syntax that you can take away.
The Malacca Strait region is a strategically crucial maritime chokepoint between Asia, the Middle East, and Europe, and North America. This makes it a frequent area of cross-border criminal activity, illegal trade, and ship piracy. In recent years, piracy in the Straits of Malacca has increased significantly. To address this problem, it is necessary to strengthen security activities through military-civilian joint operations using geospatial open-source data.
This study proposes a concept for military-civilian interoperability based on geospatial open-source data to support defense systems in the Straits of Malacca region. The study uses a literature review of military and civilian interoperability and examines the use of GEE (Google Earth Engine) for maritime surveillance.
GEE is a cloud-based platform that provides access to a massive library of satellite imagery, weather data, and other geospatial data. This data can be used to track the movement of ships, identify potential threats, and plan security operations.
DigiAgriApp is a client-server application to manage different kinds of data related to farming fields. It is able to store information about crops (specie, farming forms/system...), any kind of sensor data (included sensors and device hardware, weather, soils...), irrigation information (system type, openings...), field operations (pruning, mowing, treatments...), remote sensing data (taken from different devices as mobiles, drone, satellites), evidences and thesis in experimental field, and production quantities.
The DigiAgriApp server is composed of a PostgreSQL/PostGIS database and a REST API service to interface with it. The server is developed using Django and the Django REST framework extension with other minor extensions are used to create the REST API. This service plays the key interface between the database and the clients.
The DigiAgriApp server is composed of a PostgreSQL/PostGIS database and a REST API service to interface with it. The server is developed using Django and the Django REST framework extension with other minor extensions are used to create the REST API. This service plays the key interface between the database and the client.
The main client instead is developed using Flutter, an open-source UI software development kit based on dart, a programming language designed for client development. Flutter is able to create cross-platform applications and it was chosen precisely because of its ability to realize cross platform applications.
A QGIS plugin is also developed to manage geographical and remote sensing data in a simplified way. It is able to synchronize all the vector data (fields, subfields, rows, plants) from the server to your desktop, furthermore it is able to upload and download remote sensing data, for example upload data got from a drone or download remote sensing data got from the server it self.
All the code is released as Free and Open Source software with a GNU General Public License Version 3 license; it is available in the DigiAgriApp repository on GitLab.
UNVT Portable, powered by RaspberryPi, offers offline web map access essential during disaster events. Integrating drone imagery, OpenStreetMap, and open data tiles, this tool is a game-changer for real-time disaster response.
A talk for UN Open GIS Joint Workshop
Experience a swift immersion into the heart of Re:Earth, the groundbreaking open-source WebGIS platform. This brief session highlights its core architecture, efficient data management, and seamless visualization capabilities.
Currently, map application development has been undergoing continuous transformation due to advancements in technology and evolving user needs. This evolution has led to a diverse range of functionalities and an increased variety of visual representations. Furthermore, modern technology like Augmented Reality (AR) and Virtual Reality (VR) have been harnessed to enhance user experiences. Moreover, present-day map applications are tailored to function across a broad spectrum of devices, including Desktops, Mobiles devices, Tablets, Watches etc. So the display on the screen must be designed to be suitable for use in each device as well.
Designing User Experience (UX) and User Interface (UI) in map application is a crucial aspect that impacts the user's interaction. This makes map data available and complicated to be displayed in a simple, clear, efficient and respond to user needs. Get along with the times and changes in technology. Map Interface design is a map display design. Consists of various important elements, including Layout, Basemap, Symbology Icon, Legend, Map detail display, Search box, Map view, Map display changes, connection to other applications etc. Apart from ensuring user-friendly map display, aesthetics are also essential, creating a map-related work that is modern and aligned with the current trends.
Currently, the development of Map Applications in Thailand emphasizes the significance of designing User Experience (UX) and User Interface (UI). In the past, certain organizations used to create maps solely focusing on functionality. However, when the principles of UX/UI are adapted and applied to map-related tasks, it leads to improved presentation on platforms or map applications. This includes enhanced interaction with users, aesthetics, and contemporary design that caters to a diverse range of devices. For instance, various projects undertaken by the Geo-Informatics and Space Technology Development Agency (Public Organization) – GISTDA in Thailand, such as the Disaster Management Platform, Satellite, Spatial Data Service, Agriculture Platform, and Digital Data Service Platform, embrace these principles. As a result, UX/UI Designers have begun developing Design Guidance as a framework for crafting user-friendly, efficient, and user-responsive map applications. These efforts aim to create exceptional user experiences and aesthetics in accordance with the ever-evolving of the maps and technology trends.
In session, we will present a Map Interface that applies the principles of User Experience (UX) and User Interface (UI). This interface will be standardized and visually appealing, suitable for the modern mapping, so that developers can use it as a guide to create Map Applications.
Individual mobility is a crucial indicator of personal health and behavior. To assess individual mobility and activity, mobile sensing using global positioning systems (GPS) and surveys on smartphones are increasingly being employed, providing high spatial and temporal resolution data. This advancement has facilitated in-depth studies into the interactions between individuals' real-life behaviors, health, personal characteristics, and environments at a micro spatiotemporal scale. For such studies related to personal health and mobility, several open-source software packages (e.g., R/Python libraries) and open trajectory data (e.g., GPS data) have been developed.
In this presentation, we will introduce our R package development project for individual mobility analytics, focusing on GPS-based trajectory data processing and analysis at an individual level. The open-source package includes: (1) GPS data preprocessing, (2) construction of semantically enriched trajectory data from GPS data using automated methods for home detection, stop-move detection, and transport mode detection, and (3) computation of mobility indicators at two different aggregation levels (daily vs. by-person). We will also showcase case studies using open GPS data (such as Geolife) and datasets from other research projects. Furthermore, we will discuss the challenges faced during this project and outline its future directions.
GeoTools is the “Java Geospatial Toolkit” responsible for powering a wide range of applications, tools and utilities!
A quick update from the Project Team on this popular Open Source Geospatial Foundation project.
The growing availability of free geospatial data and satellite imagery has unlocked numerous opportunities for applying geospatial information in climate resilience, disaster risk reduction, and environmental monitoring. The United Nations Satellite Centre (UNOSAT), which is part of the United Nations Institute for Training and Research (UNITAR), utilises these satellite imagery and geospatial data to enhance the capabilities of Member States in evidence-based decision-making for peace, security, and resilience building. UNOSAT, under its partnership with the Norwegian Development Agency (NORAD), is currently strengthening the capacities of eight partner countries: Bangladesh, Bhutan, Fiji, Lao PDR, Nigeria, Solomon Islands, Uganda, and Vanuatu by providing geospatial solutions for improved decision-making in the fields of Disaster Risk, Climate Resilience, Environmental Preservation, and Food Security. The different open-source tools, algorithms, and web solutions play key roles in the implementation of the capacity development interventions of the project. In this project, UNOSAT has been delivering technical trainings in the use of open-source software such as QGIS, OpenDroneMap, and CloudCompare for processing different types of spatial data, including drone imagery and LiDAR datasets. In the application of knowledge gained through training, UNOSAT frequently receives technical support requests from various key stakeholders in each country. To address these requests, the project team prioritises the use of open-source tools and methodologies. This approach aims not only to deliver products but also to facilitate knowledge transfer to ensure the sustainability of technical capacities. In addition, web solutions have been developed using GeoNode, Terria, and WebODM to further support the need for services using geospatial technology. Co-creation is at the core of developing these solutions so that partner countries will have the ability to autonomously maintain and operate these solutions beyond the project lifetime. By using open-source software, this project allowed UNOSAT to help countries find ways to work together more effectively despite their limited resources. This approach of prioritising open-source solutions for capacity development has been contributing to transformational change in the partner countries by improving climate resilience through effective and efficient disaster preparedness and response and supporting evidence-based decision-making for a more systematic climate change adaptation response by the government and the local communities.
A talk for UN Open GIS Joint Workshop
The LH Urban Digital Twin Platform is a comprehensive solution for new town planning and development that utilizes open source digital twin technology. The platform combines real-world data with spatial information context to offer a three-dimensional sharing/collaboration integration support system.
Developers will appreciate the platform's flexibility and scalability, which are based on a microservice architecture that connects multiple modules independently and loosely. The platform utilizes open standards WMS, WFS, WCS, WPS OGC Web Service standard features through GeoServer and GeoWebCache, a tile cache server that accelerates map delivery built into GeoServer. Additionally, the platform supports visualization of data in various formats using mago3D, F4DConverter, and Smart Tiling.
The platform offers a range of services, including automatic apartment building placement, construction site safety management, 3D urban landscape simulation, environmental planning simulation, and underground facility visualization simulation. The platform also features real-time monitoring and visualization of IoT-based data, which is of particular interest to developers interested in smart city development.
Firstly, the presentation will show how open source based digital twin visualize the complex 3D city models in a web browser. Secondly it will showcase the platform's features and data, including actual system's functions and service UI/UX through a video. Attendees will gain insights into how the platform can be used to support rational decision making during complex urban planning, design, development, and operation stages.
This presentation is of interest to developers working in the field of urban planning, design, and development, as well as those interested in open source digital twin technology.
(FYI) LH Corp is one of the largest public companies in Korea providing land and housing for public purpose. They are owned and controlled by the Korean government. They’ve played a large role in new town development and housing welfare.
In the majority of cases, I find that deep learning of point cloud data, e.g., in the case of classification, learning with datasets available on the Internet is not accurate enough when trying to solve our specific practical problems. We are then forced to manually label the point cloud, which can be time consuming and difficult to achieve accuracy in detail when processing on a 2D display, as point cloud data is generally not highly visible.
Therefore, I am trying to speed up the labeling of point clouds using VR. I would like to share my idea with you this time.
Nowadays, the demand for services that facilitate precise and dynamic location finding has increased exponentially. To meet this need Thus came the idea of developing efficient Place APIs. This session focuses on the conceptual design and development of Place APIs using OpenSearch, a powerful and versatile search engine technology. Its purpose is to develop an efficient API. To facilitate access to location information, locations or other interesting information. as required by developers
This session will begin by introducing the concept of the Place API and its importance in modern applications. It focuses on the core features and functionality that these APIs should have. including collecting geolocation data search by keyword, filtering and sorting the results. OpenSearch, known for its flexibility and scalability, is then introduced as a suitable framework for implementing Place APIs due to its ability to handle large datasets and support a wide range of search operations.
The implementation section demonstrates a step-by-step process for creating Place APIs using OpenSearch. Code snippets and examples illustrate how to set up the necessary infrastructure, index location-based data, and implement various search functionalities.
Evaluating active Place APIs focuses on performance such as response time, query volume, and performance. and the ability to scale Real world scenarios are simulated to illustrate the API's ability to handle concurrent requests and evolving datasets. Comparison analytics with existing location-based APIs Demonstrates the strengths of the OpenSearch-based approach in terms of speed, flexibility, and cost-effectiveness.
In conclusion, the session underscores the significance of well-designed and implemented Place APIs in enriching location-aware applications. It emphasizes the power of OpenSearch as a versatile platform for creating such APIs, offering developers the tools needed to create robust, dynamic, and accurate location-based services. By following the guidelines and insights provided in this session, developers can contribute to the advancement of location-based technologies and meet the increasing demand for innovative digital experiences.
This Talk is about a novel method for visualizing geospatial data using integrated charts using OpenLayers, an open-source mapping library. The integration of various chart formats in maps allows users to interactively explore complex data sets, and gain insights into spatial modeling and development Usage Popular chart libraries such as D3.js, Recharts, and OpenLayers integrate seamlessly, enabling dynamic modification of chart parameters and spatial details Analyzed data from different sources to reveal hidden relationships Statistics. The integrated system provides a powerful tool for practitioners and researchers to make informed decisions based on a comprehensive view.
A talk for UN Open GIS Joint Workshop
As the maritime traffic increases, maritime accidents are on the rise. Drones are being used to detect distressed vessels quickly in the open ocean. The current system does not have a plan to operate drones according to the search target, which means it is inefficiently operated and the search time is delayed, reducing the chance of survival. The operation plan should be developed based on tests in various environments and conditions. However, real-world implementations of distress situations have limitations. In the real world, implementing human distress in the ocean is limited by safety issues.
In order to find the optimal way to operate the drone for marine search and rescue, this study aims to construct a simulation environment with various environments and conditions.
To realize the simulation environment, Unity Engine was used. The environmental aspects of the simulation include the sea (sea colour, wave height), objects (vessels, people, etc.), and the environment (day, night, sunny, rain, snow, fog, etc.). In terms of functionality, camera angles can be set, and adding a drone function allows the drone to move and shoot. In this paper, the environment factor was fixed to daytime clear weather, and the focus was on the object to be searched. The simulation was developed with five types of distress vessels (2-tonne fishing vessel, 5-tonne fishing vessel, cargo vessel, passenger vessel, and motorboat) and six types of distressed persons (baby, adult, and with or without life jackets). The distress vessels can be set to sink, capsize, or drift, and the distress persons are drifting on the surface of the water. The altitude of the drone was limited to a maximum of 150m to comply with Korean law, and the camera angle could be set to (-30︒, -45︒, -60︒, -90︒). To reflect real-world coordinates, Cesium was integrated into Unity to simulate a marine environment. To do this, Cesium's coordinate system was adapted to be compatible with Unity's coordinate system, and the positions of the distressed person and the marine police in the distress scenario were expressed in terms of actual Earth longitude, latitude, and height. This maintains the scale and realism of the simulation environment.
It is expected that future simulation results will be used to develop a drone operation plan to effectively search for various distress ships and distressed person to minimize human casualties.
It is often difficult to expand the use of FOSS4G softwares and business solutions due to lack of promotional materials and introductory resources. This talk introduces ways to expand the use of FOSS4G softwares through experiences with providing scheduled systematic QGIS workshops and publishing of introductory videos on the use of FOSS4G solutions.
Environmental problems have been exacerbated, including the issue of plastic waste. Regarding practicability, the plastic waste issue can be tackled with collaborative acts and participation from all to exhibit strategic solutions. Mapping of plastic waste is one of the strategic solutions where effective management and monitoring can be adhered. This study analysed the inclusivity of open-source spatial data processing for plastic waste management governance. We addressed the three main components of the monitoring process of plastic waste, such as crowdsourced data, replicability of the GIS methodology, and geocoding in translating information into spatial analysis. The overall analysis is incorporated in the model builder to enhance the practicability and enable the flow data manipulation to integrate with the other data analysis result. The main analysis included data extraction, proximity analysis, and spatial join analysis. The result has been showcased in the interactive hotspot maps and addressed the vulnerable exposure to plastic waste leakage. The obtained results are practicable for translating the report and regulation in managerial based into the spatial context, which potentially complements the national action plans in plastic waste management.
GisHosting2 is the revamped version of the cloud GIS solution based on QGIS created by the GTER company. Users only need to set up the correct folder where to save their QGIS and Lizmap data and projects to immediately have their work published on the web on a Lizmap-based client.
One of the most important innovation brought in the new version is the introduction of a docker infrastructure. This new version is completely container-based, which allows for almost immediate activation of new instances as well as easier management and updating by system administrators. In addition, an administration system has been developed to automate and simplify user management (creation, change of plan, disabling, etc.) and to provide certain user information.
During the presentation, an overview will be given of the software that makes up the infrastructure and how they communicate with each other. It will also show how to try out the cloud service using a trial version and how to register in case you are interested in using the service.
Last but not least, we will show you some use cases of our clients who have been using the product for several years.
PyQGIS: the Python environment within QGIS that empowers your geospatial journey. Let's explore how it streamlines workflows and enhances QGIS with Python libraries. With PyQGIS, you can automate things. How? By writing simple Python scripts. No more manual clicking – PyQGIS scripts can do the heavy lifting for you. This means more time for creativity and exploration. Even if you're not a coding expert, no problem. I will walk you through the basics, so you can use Python in QGIS without any stress. You'll learn how to make your own solutions and automate tasks, all with the magic of PyQGIS.
This talk is relevant to anyone interested in automating multiple processes using QGIS. Attendees will come away with a deeper understanding of the basics to advance the level of PyQGIS and how to use it for their own individual projects. Discover how PyQGIS transforms your workflow, turning repetitive tasks into automated processes.
A talk for UN Open GIS Joint Workshop
In recent years, it has become fashionable in Japan to take a historical walk based on old maps. For map lovers (like us!), old maps are a lot of fun!
However, for the general public, cartographic decoration of old maps is somewhat difficult.
Therefore, I created an easy-to-understand historical map of the 19th century (Edo period) by applying a modern style design.
I have created line strings and POIs for roads, rivers, post towns, temples, shrines, etc. using old maps and various documents. I created and applied modern style designs in QGIS and published them as WebGIS.
The identification of critical infrastructure facilities is critical for climate and disaster risk assessments that forms part of a resilient land use plan. This study utilized the application of a geo-analytics platform using free and open source software for geospatial analysis, namely Quantum GIS (QGIS) in generating multi-hazard exposure maps of critical infrastructure facilities in Taguig City, Philippines. The study was conducted as a necessity for planners looking for an alternative viable solution for resilient land use planning using non-proprietary Geographic Information Systems (GIS).
The study developed a workflow for geospatial analysis with direct application in hazard exposure mapping for floods, active fault lines, and waterway easements. A review of open data portals was first performed which discussed the limitations and opportunities of existing official data repositories. A supplementary online survey was likewise conducted with local planners to understand their knowledge, skills, attitudes, and perceptions to GIS and its application to disaster risk assessment. The survey responses from local planning and development offices showed that they are open to improving institutional capacity and professional capabilities in GIS, particularly in QGIS. A comparative review of different spatial decision support systems for climate and disaster risk assessment was also carried out that revealed important ways forward in further developing open tools for disaster risk assessment. The case study was able to identify the multi-hazards exposure profile of Taguig City using open data while using the free and open-source software QGIS. The data used were accessed from online data portals that have free public access, and data formats useful for independent study.
The online survey also provided invaluable insights into the knowledge, skills, awareness, perceptions, and preferences of the respondents with regards to GIS-related topics. The results of the survey showed that there is a need for GIS training programs and in particular an expressed openness to learning QGIS. Also, the review of existing spatial decision support systems available in online and standalone platforms showcase the limitations and opportunities of adapting graphical models to automate the process of disaster risk assessments.
The study was able to demonstrate a viable alternative geo-analytics platform using free and open-source software for geographic information systems and open data. Results of the comparative review of existing data portals and spatial decision support systems helped in the development of the proposed open resilience framework. Said framework was able to produce the required exposure maps of critical infrastructure in Taguig City.
In this paper, we explore innovative and practical ways of equipping youth and young academic experts with skills related to effective data collection, curation, sharing, and visualization. We present a case study of Resilience Academy, a successful initiative implemented in Tanzania, to highlight the creative approaches used to teach these skills. Resilience Academy conducted a series of training sessions that employed community-led data collection, data visualization, and participatory mapping to engage youth and provide them with hands-on experience in the use of geospatial data. The initiative not only equipped the youth with practical skills but also empowered them as change agents who could address local challenges. This case study underscores the importance of providing creative and participatory approaches to teaching and learning geospatial skills to empower youth.
2023 conference of KSGIS Opening
I will introduce the activity of wheelchair mapping.
OpenStreetMap & OSGeo community will gather together to have a fun mapping party!
OpenLayers makes it easy to put a dynamic map in any web page. In this talk we'll discover how Openlayers works, what is the methodology behind adding new data to it. We'll understand how to spin up new openlayers map under 5 minutes using React.
Openlayers allows us to put data on map, interact with it depending upon whether you use it on computer or mobile, export data with map, etc. We'll explore examples few examples from https://openlayers.org/en/latest/examples/ .
Apart from this we'll also take a look at https://viglino.github.io/ol-ext/ this is extension to the openlayers using which we can add more types of interactions, visualisations, controls, etc.
Seoul's subway route map is complex and it is not easy to intuitively understand it. In this presentation, we introduce a web application that allows you to check the real-time movement of the Seoul subway on a 3D map through a web browser by utilizing subway route, station, and timetable data.
The data used is based on public data provided by the ‘Korea Transport Database’ and the ‘Seoul Open Data Plaza’ API service, and has been appropriately processed to optimize the system. The subway timetable data changes frequently, so we built a data pipeline to reflect it in real time, so that even if the data changes, it can be quickly applied to the system to provide the latest information.
Various open sources such as GeoTools, CesiumJS, and turf.js were used to create the application, and the real-time movement of the subway was expressed in various colors and icons on the 3D map. Click on a particular subway to see the change in travel time and the train's travel information.If you click on a specific subway, you can check the change in.
To improve the safety of autonomous driving, ample data is required. However, obtaining driving data takes considerable manpower, and driving an autonomous vehicle is dangerous. Recently, vehicle driving simulations have been efficiently researched in the field of autonomous driving as a solution to this problem. However, these simulations are difficult to obtain for public users, and users must pay expensive license fees. Moreover, existing vehicle driving simulations can only be used in a limited environment because users have limited modification rights. Therefore, we want to develop a simulator with better accessibility than paid simulators and establish a simulation each user can acquire data in the customized environment. It will include the core functions of commercial simulators, such as setting the vehicle's route, creating a realistic environment, and acquiring the vehicle's sensor data. This simulator, implemented as a "toolchain," incorporates open-source platforms.
In this study, we will integrate the open-source platforms 'Vehicle Route Planning Simulation Platform', '3D Vehicle Driving Simulation Platform', and '3D Road Modeling Tool'. The 'Vehicle Route Planning Simulation Platform' is specialized in planning the route of a vehicle, the '3D Vehicle Driving Simulation Platform' is developed to support autonomous driving systems so it is easy to obtain vehicle sensor data, and the '3D Road Modeling Tool' helps to build a realistic environment. Therefore, we adopted these platforms. Vehicle driving simulation have essential elements.
We need a Vector Map the vehicle references to drive and a 3D model to visualize it. Based on the vector map, the scenario of the vehicle is determined and the environment similar to the real world is created with the 3D model. To implement the actual vector map, we first download the road network in OSM format from Openstreetmap. And the road network is converted to a road network used by the 'Vehicle Route Planning Simulation Platform'(network). The network generates vehicle scenario(route) and 3D road data with road shape and properties through the 'Vehicle Route Planning Simulation Platform'. The 3D road data has a real-world environment by creating buildings and structures, vegetation, and the slope of the road using the 3D Road Modeling Tool. Import the previously created network file, route file, and 3D road data into the '3D Vehicle Driving Simulation Platform', and you can see the vehicles driving on a set route in the 3D simulator. Additionally, We define Camera, LiDAR, and GNSS sensors and assign the location and properties of the sensors. And we can obtain customized data. Using the method described above, we implemented a simulator based on the environment near the main gate of Seoul National University and obtain a camera, LiDAR, and GNSS data from 1000 vehicles at a time. The vehicle driving simulator creates a virtual mirror world of the real road. Moreover, simulators with open source can interact with various developers and improve rapidly, which is an opportunity to expand the simulator market. Furthermore, this research is expected to help develop algorithms to improve the location information of vehicles.
In 2022, global autism rates increased, affecting 1 in 100 children. In the Philippines, the prevalence rate is 1 in 122, totaling almost 350,000 affected Filipino children.
Children with Autism require 10 to 40 hours of therapy per week, encompassing various sessions like speech-language, occupational, and physical therapy, to foster crucial social and learning skills for integration. However, obtaining these interventions proves challenging due to high demand, uneven spatial distribution of resources, and a lack of centralized credible information source. Given these, tracking a child's milestones and progress often takes a back seat.
Empowering families in their search for suitable therapy or special education places is essential. Location emerges as a key factor after service availability, warranting an updated, comprehensive, and credible map.
Ausome Maps, a She Leads and She Inspires grant project funded by the Humanitarian OpenStreetMap Team via the Open Mapping Hub Asia-Pacific. It aims to map schools with special education classes, therapy clinics, and institutions aiding differently abled Filipinos on OpenStreetMap. Initially, out of over 5000 identified school points of interest (POIs), only 12 were tagged as 'special needs', with fewer than 10 POIs tagged as places for therapy.
Ausome Maps conducted events to raise autism awareness, elucidate map importance, teach about open data and mapping on OpenStreetMap, and offer hands-on training on data gathering, engineering, and other related activities. To date, over 500 students, professionals, and families have been part of Ausome Maps’ journey through these sessions.
Ausome Maps has completed the tag updating of schools with special education classes with existing feature in OpenStreetMap. Remaining schools to be tagged – 40%, predominantly private schools, will be on OpenStreetMap following further data completion and integrity check. For the places of therapy, Ausome Maps have successfully completed and validated 70% of the national datasets provided by the Philippine Association of Speech Pathologists and Philippine Academy of Occupational Therapists.
Using Map Roulette, Ausome Maps was able to conduct beginner friendly mapathons. Kobo Toolbox allowed us to reach out to our community and solicit support in gathering primary-sourced information or data validation about either the schools or therapy centers.
Moving forward, Ausome Maps continues to create a replicable activities framework that will enable for the same impact to be recreated in other regions. A part of the project’s bid for continued impact, a platform called TherapEase is under development.
TherapEase will incentivize users – families and intervention providers – to ensure data about schools and therapy centers on the platform is up to date. Data which in turn will be used to update corresponding mapped feature on OpenStreetmap. Currently, TherapEase is one of the 35 projects undergoing investment-readiness development through the U.S.-ASEAN Science, Technology, and Innovation Cooperation Program startup incubator.
Ausome Maps remains dedicated to its mission, aiming to bring about positive changes in various regions and communities, fostering a more inclusive and supportive environment for individuals with Autism Spectrum Disorder.
In this presentation, I will talk about data visualization using the web library "deck.gl" to display vector data, 3D data, and more.
I will introduce some points and tips that you should keep in mind while using examples.
I will also discuss ways to further enhance the visual representation of data using deck.gl.
The Los Angeles County Metropolitan Transit Authority (Metro) is creating an API based on open technologies and would like to share our learnings and how to replicate our work flow. We will discuss the current technology stack, (FastAPI, Python, docker, Github Actions, and postgresql). The only closed source portion is the usage of AWS Lightsail to deploy our docker image. The key takeaways from this talk will be the following: 1) what was done with open source tools 2) how to replicate the technology stack 3) lessons learned/how to improve for the future.
For Someone who unfamiliar with multidimensional spatio-temporal data like netcdf or grib, I'd like to share how to convert them to points or grid polygons using Python's xarray and geopandas packages
In recent years, 3D city models have been instrumental in advancing urban planning, stimulating citizen engagement, and fostering research. With advancements in technology and infrastructure, a growing number of cities and nations have come to rely on 3D models to tackle urban challenges, bolster public participation, and steer informed decision-making processes.
The Japanese government, notably through the Ministry of Land, Infrastructure, Transport and Tourism's Project PLATEAU, has been an advocate for open 3D city models and 3D point cloud data. As of February 2023, over 100 cities are in the process of curating and disseminating open digital twin data in the CityGML format. The outcomes of these endeavors have been documented by Binyu et al., and further spotlighted in the 3D City Index benchmarking report. Notably, this report indicates that an impressive seven out of 40 cities (18%) examined were located in Japan.
This presentation sheds light on the present status of open digital twin data in Japan, which aligns with the open database license ODbL. This data is adaptable to widely-used platforms like OpenStreetMap, and specific converters have been crafted to facilitate this transition. Since 2022, importation efforts have been executed on a trial basis, orchestrated in conjunction with both national and global communities. This year, after several rounds of import operations, we have successfully integrated significant portions of the data into OpenStreetMap. Our results show improved urban planning accuracy and heightened public engagement in the areas where the data was implemented. By sharing both our accomplishments and the obstacles we encountered, we aspire to spur the worldwide utilization of 3D city model data.
Title : Revitalizing Urban Voids in Coimbatore, India: A Remote Sensing Approach for Sustainable Development and Community-driven Regeneration using Open source software
The study on urban void regeneration in Coimbatore, India, utilizing remote sensing applications is an essential and promising endeavor. Urban voids, characterized by underutilized or abandoned spaces, are prevalent in many cities and pose both challenges and opportunities for urban regeneration and development.
The integration of satellite imagery and GIS using open source software in this research allows for comprehensive mapping and analysis of the distribution and extent of urban voids in Coimbatore. The use of spectral and spatial analysis to classify these voids based on their characteristics, such as size, location, and land use patterns, provides valuable insights for understanding the current state of these spaces.
The focus on sustainable development and community-driven interventions is crucial for ensuring that the regenerated urban voids contribute positively to the city's overall fabric and cater to the needs and aspirations of the residents. By considering socio-economic indicators and involving the community in decision-making processes, the study can propose strategies that align with the local context, thereby increasing the chances of successful and inclusive urban regeneration.
The identification of suitable functions for each void, such as green spaces, recreational areas, or mixed-use developments, based on remote sensing data and socio-economic indicators is a commendable aspect of the research. By selecting appropriate functions, the regenerated spaces can serve as valuable assets for the community, contributing to their well-being and enhancing the urban environment.
The advocacy for integrating remote sensing technology into urban planning practices is well-founded. Evidence-based decision-making, enabled by remote sensing data, can significantly improve the efficiency and effectiveness of resource allocation and urban development initiatives. This data-driven approach ensures that urban regeneration efforts are well-informed and targeted, leading to better outcomes for the city and its residents.
The discussion on the broader applicability of remote sensing in urban regeneration efforts is highly relevant, as it encourages other cities facing similar challenges to embrace these tools and methodologies. Leveraging remote sensing technology can empower cities to make informed decisions, identify opportunities for regeneration, and create more vibrant, resilient, and sustainable urban environments.
In conclusion, the study on urban void regeneration in Coimbatore demonstrates the potential of remote sensing applications in addressing urban challenges and unlocking opportunities for sustainable development. By combining technological advancements with community engagement, the research offers a holistic approach to urban regeneration that can serve as a model for other cities seeking to revitalize underutilized spaces and create thriving urban environments.
Keywords : Urban Voids, Open source software, Spatial Analysis, Urban Renewal, Geographic Information System (GIS)
The Uber H3 library is a powerful geospatial indexing system that offers a versatile and efficient way to index and query geospatial data. It provides static indexing scheme that allows for fast and accurate calculations of geospatial distances, as well as easy partitioning of data into regions. In this proposal, we suggest using the Uber H3 indexing library in Postgres for geospatial data analytics.
Postgres is an open-source relational database management system that provides robust support for geospatial data processing through the PostGIS extension. PostGIS enables the storage, indexing, and querying of geospatial data in Postgres, and it offers a range of geospatial functions to manipulate and analyze geospatial data.
However, the performance of PostGIS can be limited in some cases when dealing with large datasets or complex queries. This is where the Uber H3 library can be of great use. By integrating Uber H3 indexing with Postgres, we can improve the performance of PostGIS, especially for operations that involve partitioning of data, distance calculations and zonal statistics
This talk will demonstrate the use of Uber H3 indexing library in Postgres for geospatial data processing through a series of examples and benchmarks. The proposed presentation will showcase the benefits of using Uber H3 indexing for geospatial data processing in Postgres, such as improved query performance and better partitioning of data.
The proposed presentation will be of interest to developers, data scientists, and geospatial analysts who work with geospatial data in Postgres. It will provide a practical guide to integrating Uber H3 indexing with Postgres, and offer insights into the performance gains and applications of this integration.
Local Climate Zone (LCZ) maps represent a valuable informative tool for several applications including outdoor thermal comfort assessment, building energy consumption, and microclimate modelling. Typically, these maps are produced by taking advantage of multispectral satellite imagery and multiple geospatial layers (e.g. topographic databases and land use/land cover maps), according to established workflows. Nonetheless, cutting-edge data and technologies leave large room for improvement. Furthermore, different features in terms of map accessibility, format and resolution may be required for the different applications depending on the end-users' needs. This is why user requirements should be gathered and addressed during the mapping workflow’s design and implementation.
In view of the above considerations, the LCZ-ODC project (agreement n. 2022-30-HH.0), funded by the Italian Space Agency (ASI), aims to produce multi-temporal LCZ maps for the Metropolitan City of Milan, in northern Italy, using innovative open data sources and Free and Open Source Software (FOSS) tools. The project is being carried out from a user-oriented perspective to achieve high levels of usability while fulfilling different end-user informational needs. Stakeholders, including public administration, professional associations, foundations, and researchers, are actively involved in the project development and validation phases through dedicated workshops and educational events aiming to consolidate user needs, guide product implementation, and assess the usability of the project outcomes.
The LCZ-ODC project uses a hybrid remote sensing/Geographic Information System (GIS) based approach for LCZ mapping, utilizing both open geospatial layers and high-resolution satellite imagery. The latter include multispectral Sentinel-2 images distributed by the European Space Agency (ESA) and hyperspectral data acquired by the PRISMA (Hyperspectral Precursor of the Application Mission) satellite, provided by ASI. Additionally, in-situ weather measurements sensed by authoritative monitoring networks are used to assess the statistical correlation between the LCZ classes and air temperature.
The Open Data Cube (ODC) technology serves as the project’s backend system, facilitating multi-source data integration, pre-processing, and the distribution of ready-to-use data. In particular, geospatial layers contained in the ODC include multi-temporal and multi-resolution LCZ maps, preprocessed satellite images, and static layers describing the LCZ morphological properties, like sky-view factor, building height, and impervious surface fraction. A preconfigured ODC instance will be published in a Docker container, equipped with libraries and tools needed for interacting with and downloading the available datasets. Moreover, Jupyter Notebooks will be made available as a project outcome, enabling non-expert users to easily visualize, query, and download the data from the ODC. These Notebooks also offer documentation and ready-to-use code for expert users, streamlining the data analysis and exploration process.
The ongoing work focuses on developing interactive and user-friendly interfaces to allow users with different levels of expertise to directly access and interact with the data contained in the ODC. Additionally, training sessions are planned during the project's validation phase to demonstrate the products' operational use while tailoring outputs to the specific needs of users.
The "AIST 3DDB Client" is a web application designed to utilize the 3D database developed and operated by the National Institute of Advanced Industrial Science and Technology (AIST) in Japan.
This app, which allows for the viewing, searching, and downloading of various 3D data, is released as an open-source software: https://github.com/aistairc/aist_3ddb_client
In this presentation, along with an introduction to the app, we will discuss the current state and challenges of 3D data visualization on the web, as well as provide tips when using Vue.js and CesiumJS.
The point cloud is becoming a valuable resource for building Digital Twins, the virtual representation of a real-world physical feature, with the LiDAR developments. The semantic information of each point (such as walls, floors, doors, windows, etc.) is essential to handle and analyzing point clouds. However, the raw point cloud captured from the LiDAR sensor doesn’t have semantics, and the annotation task for adding semantics is time-consuming and needs expert experience with commercial software. Therefore, we set up two goals for accelerating the 3D point clouds annotation task:
- Machine-based assistants, such as AI techniques, and
- Task-based collaboration.
Recently, deep learning has been used to derive semantic classes by automated classification and segmentation. Therefore, it can be used to address the manual task (bottleneck) in the traditional annotation process. Also, the data format for deploying is essential for collaboration. It should be easy to understand and efficient to share.
PCAS (Point Cloud Annotation System) is a 3D point cloud annotation system that enhances the quality and speed of annotation tasks with deep learning techniques. This system can be summarized as four key points:
- This system mainly develops based on Potree, an open-source library for point cloud visualization. It also utilizes various open sources, such as open3d and torch-points3d, for handling point clouds with deep learning modules.
- This system supports three tools to accelerate annotation work: semi-automatic labeling with a deep learning module, 3D shape object-based labeling, and normal vector-based cluster labeling.
- The annotation task can be easily shared and tracked, similar to GitHub.
- The annotation task results are stored as an HDF5 file based on a predefined profile for the labeled point clouds, easily sharing and managing semantic information.
This talk will introduce the four main points above, including the structure of PCAS with other open sources, the design purpose, and problems and solutions encountered during development.
 Potree, WebGL point cloud viewer for large datasets, https://github.com/potree/potree
 torch-points3d, Pytorch framework for doing deep learning on point clouds, https://github.com/torch-points3d/torch-points3d
 Open3D, A Modern Library for 3D Data Processing, https://github.com/isl-org/Open3D
 The HDF5 profile for labeled point cloud data, https://docs.ogc.org/dp/21-077.html
UNVT Portable is a RaspberryPi-based package that enables users to access a map hosting server via a web browser, primarily for offline applications during disaster events. By integrating aerial drone imagery, OpenStreetMap, and open data tile datasets, this tool significantly enhances disaster response capacities.
Dive into the technological foundations of Re:Earth, the open-source WebGIS platform. This session delves into the intricacies of core architecture, data management, visualization techniques, and plug-in development. Secure an in-depth understanding of Re:Earth's revolutionary approach to geospatial data interaction.
The name fAIr is derived from the following terms:
f: for freedom and free and open-source software
AI: for Artificial Intelligence
r: for resilience and our responsibility for our communities and the role we play within humanitarian mapping
fAIr is an open AI-assisted mapping service developed by the Humanitarian OpenStreetMap Team (HOT), designed to enhance the efficiency and accuracy of mapping efforts for humanitarian purposes. By utilizing computer vision techniques and open-source AI models, fAIr detects crucial map features from satellite and UAV imagery, starting with buildings. The service fosters collaboration with local communities, enabling them to create and train their own AI models, ensuring relevance and fairness in mapping. Through a constant feedback loop, fAIr progressively improves its computer vision models, contributing to the continuous advancement of humanitarian mapping.This talks will talk about our journey and vision for using AI
Smart Seoul Map is Seoul's representative map portal service.
Smart Seoul Map is operated based on the newly created geospatial information platform as open source SW in 2017-2018.
Smart Seoul Map is an open-source software that is equipped with new technologies every year, and the government introduces various policy projects to citizens. In particular, in the past COVID-19, we made a map of corona medical information that citizens need to know, such as screening clinics and respiratory treatment centers, to communicate with citizens as quickly as possible. Today, we introduce Smart Seoul Map made with open source GIS SW.
2023 Conference of Korea Spatial Information Society
2023 Conference of Korea Spatial Information Society
The Open Mapping Hub Asia-Pacific's Disaster Services Project aims to support, assist and connect the local OpenStreetMap communities and partners in the disaster management sector, particularly through disaster mapping-related activities using free and open source tools to increase resilience and reduce impact through data-driven decision-making in all stages of disaster phases. This poster is a 2-year review of what has been accomplished in the project and the impact of the hub in becoming the go-to open mapping data provider for the disaster management cycle of several local/national emergency management agencies and local government partners.
Contrails potentially influence the Earth’s energy balance by altering the radiative forcing (i.e., heating or cooling), particularly in middle latitudes where a cold and sometimes moist upper-troposphere (UT) intersects the mean jet-flight path altitude. Such contrail radiative forcing should be amplified when multiple occurrences of these anthropogenic clouds are grouped spatio-temporally as ‘contrail outbreaks’ and contrail cirrus on at least 1 × 103 km2, extending over approximately 104~105 km2, and persisting for approximately 1-6 h. To more definitively determine the net radiative forcing of contrails and their role in contemporary and future climate changes, and to establish appropriate climate change mitigation strategies (e.g., jet flight activity regulation), it is essential to reliably detect contrail incidence and assess the spatio-temporal distribution of contrail outbreaks as well as understand the underlying mechanisms of contrail formation.
Thanks to recent advanced technologies, one can obtain flight tracking data as well as automate an individual contrail occurrence detection on the satellite or aerial images and alleviate the burden of labor-intensive manual contrail detection tasks. In this context, our COVID-Contrail project aims to (1) assess the changes of contrail occurrence patterns and relevant atmospheric factors in the continental United States (CONUS) before and during COVID-19 and (2) develop an automated data processing and analysis system to construct and publish a massive contrail incidence database in high spatio-temporal resolution. This project is expected to make significant advances in diverse aspects of contrail and climate studies. In the poster presentation, we will introduce our COVID-Contrail project and present work in progress of our project.
A greenhouse is an important infrastructure for protecting out-of-season plants against extreme cold or hot weather. Remote sensing datasets are useful for identifying the greenhouse locations, measuring the greenhouse sizes and accessing the greenhouse directions. In this research, the greenhouses were detected from the Planetscope satellite imagery using the YOLO algorithm through the following steps. First, the training samples of the greenhouses were made using the Planetscope satellite imagery . Second, the YOLO model was trained using the generated training samples. Finally, the greenhouses were detected from the given Planetscope satellite image using the trained YOLO model. The trained YOLO model had the outstanding performance for detecting the greenhouse features from the given Planetscope satellite imagery with the medium spatial resolution (3m).
Acknowledgment: This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ0162342022)" Rural Development Administration, Republic of Korea.
In recent years, aging infrastructure facilities has become an issue in various regions around the world. According to Japan's Ministry of Land, Infrastructure, Transport and Tourism, the lifespan of infrastructure is 50 years after construction, but the number of infrastructure facilities that have exceeded their lifespan is increasing every year. On the other hand, inspections are still conducted visually by facility managers, and the inability to properly conduct detailed inspections due to a lack of engineers has become a social issue. Furthermore, conventional visual inspections lack consistency due to the large variation in results from one inspector to another. To address these issues, we developed a web system that automatically detects cracks, one of the inspection items, by object detection, and manages and visualizes the results using location information. In this study, we focused on cracks located on the apron's top of a quay wall, one of the port facilities. In the inspection process, we first used a small general-purpose drone to automatically photograph the inspection target and create orthophotos. The orthophotos are then uploaded to a crack detection web system. The system segments the uploaded orthophoto into smaller sections and uses object detection to locate cracks in the segmented image. Using the detection results and the orthophoto's location information, the system calculates the detected cracks' location. The results are displayed on the orthophoto and output in GeoJSON format, which has the advantage that it can be easily combined with other geographic information data using QGIS for analysis and comparative analysis based on time series. In addition, the system can perform everything from crack detection to display and download of results centrally on the Web, making it easier and faster for inspectors to proceed with their inspections. This new method, which involves detecting cracks through object detection and acquiring consistent quality aerial images through automated drone flight, improves inspection efficiency while reducing the variability of inspection results.
Recently, in the midst of increasing flood damage due to torrential rain in downtown areas, interest in urban underground disaster prevention is increasing as there is a case of reducing flood damage due to water barriers installed in buildings. In this study, the Unity engine was used to build a 3D model based on a digital map, a digital twin model that combines CCTV, weather forecast, and river water level information. In addition, a parking lot model to be interlocked was directly produced and interlocked with IoT MQTT.
Recently, the rapid advancement of autonomous driving-related technologies has led to swift progress in the development of various sensing devices and application methods. In particular, plans to produce highly precise maps to support this technology are being promoted, and research and data production projects related to this are being conducted continuously.
On the other hand, the nature of maps, which must maintain a certain level of accuracy, makes them difficult to update frequently. This is due to the substantial manpower and financial resources required not only for initial production but also for continuous updating of the maps.
I believe that the most fitting solution to this problem is AI technology. It has already been actively applied in other fields and can provide both speed and cost-effectiveness. However, since the deep learning models currently available have limitations in terms of accuracy when producing maps, a separate AI model that can guarantee this accuracy must be developed. In this study, we aim to introduce a method that can automatically update road maps by enhancing previously announced deep learning models and integrating various spatial data.
Buildings in urban environments, especially large public buildings with relatively high numbers of occupants pose significant risks during any disaster like fire or other conditions necessitating an evacuation of all these people. There is a need to assess these spaces with respect to evacuation strategies and exits available for improving people's safety and ensuring a smooth flow of people out of the building within permissible or safe periods of evacuation. This research proposes INSroute - an indoor path planning algorithm to address building evacuation challenges by integrating spatial, temporal, and path constraints, specifically tailored for 2D building plans, within a geospatial framework using pgRouting over IndoorGML formatted data.
Generating the geospatial network is a vital component in evacuation planning and for a building it needs to be derived from the building floor plans based on the indoor space semantics. The first part of the work involves the use of QGIS and Python to extract these spaces and locate the nodes within these spaces and at its exits or doors - indicating the node connectivities through the building to its evacuation point outside the building. We utilize libraries like NumPy, pandas, and matplotlib for creating these nodes. A Python code is created to establish the connecting points and generate the network, while ensuring a proper coordinate reference system alignment. The resulting network visually portrays node connections thus aiding effective evacuation planning. The network is further augmented by a time-distance metric and edge capacity using functions in PostGIS/PostgreSQL. The INdoor Space routing algorithm, named INSroute, is then implemented in pgRouting to efficiently determine the optimal evacuation paths integrating the spatial characteristics of the nodes, path capacity and temporal aspects of people flow. It allows for optimal resource utilization, and route optimization.
The developed algorithm was implemented across three different scenarios - (i) free flow with single exit - a building floor plan with no furniture in the rooms and spaces with only one main exit; (ii) free flow with multiple exits - alternate exits are possible; and (iii) convoluted flow - due to presence of furniture or other objects as obstacles. The results reveal that INSroute algorithm can enhance evacuation planning and an understanding on the need to replan indoor spaces with multiple exits based on occupancy levels and potential obstructions. This research introduces a subspace model for geometric spaces containing obstacles for use with the proposed algorithm, thus improving both the computation of the path lengths and computation time across various geometric spaces. The study demonstrates that the presence and distribution of objects can lead to considerable increases in evacuation time, ranging from 2x to 6x - a significant impact on evacuation efficiencies. The study highlights how spatial arrangement in a floor plan significantly influences evacuation strategies, enhancing safety and efficiency.
The registration of multi-modal satellite images (e.g., SAR, EO) is essential in fields using multiple images such as time series analysis, change detection, and image fusion. The usability of satellite images acquired from various sensors can be increased only when the images are accurately registered to each other. However, multi-modal satellite image registration is still challenging due to the different characteristics of the images. Therefore, in order to explore an effective way for multi-modal satellite image registration, this study intends to apply a matching method using feature extractors of pre-trained deep neural networks and compare the matching performance of the models.
This work was supported by the Agency For Defense Development by the Korean Government.
With the development of drone technology, multi-channel image sensor cameras are being installed along with general color imaging cameras. In particular, thermal image sensors are increasingly interested because they are widely used in various ways in urban areas. However, it is not easy to create ortho-images because most thermal images have a very low resolution compared to color images and it is difficult to determine the control point in the image. In this study, a thermal image geometric correction based on ortho-images was performed and its utilization was analyzed.
The MapLibre Organization is an umbrella for open-source mapping libraries with the two central rendering projects being MapLibre GL JS and MapLibre Native which are surrounded by an ecosystem of plugins and tools to generate maps.
MapLibre started as a fork of Mapbox GL JS 1.13 in December 2020, after Mapbox announced a second version of the library that was no longer open-sourced.
In this talk we'll showcase Maplibre with a few examples, state of libraries in the ecosystem including native sdks, current development process and more.
Testing is one of the stages in software development. It is a process to identify errors that may occur in the system, ensuring that the developed software is correct, secure, of high quality, and efficient. This involves checking various aspects such as the user interface, APIs, business logic, data, as well as the integration of different functions to ensure proper alignment and compliance. This is crucial to instill confidence that the tested system will have minimal errors. Developers must conduct testing before deploying a system, whether it's a small feature, a mobile application, a machine, or various types of software, including web mapping.
In this presentation, we will explore a testing process specifically for web applications, which is the most popular approach for creating applications that can be used on both computers and mobile devices. This aims to ensure that users can comprehend the information being conveyed, especially in the context of online maps, involving location and map data to be presented on a website. The essential task is to establish a systematic testing process that aligns with the designed functionalities.
Testing the system involves not only manual processes but also technology frameworks that enable us to create automated test scripts. These scripts repeat tests on various functions within the web application, such as invoking the web application, navigating between pages, entering text, clicking buttons, and performing actions related to maps, such as setting zoom levels, panning, and toggling layers. This testing will be presented using an Automated Test Script Framework, which will guide further development and enhancement of testing for online web map applications, ensuring efficiency and maximum accuracy.
This is an interesting area of applying application testing processes to online web map applications, inviting those interested in enhancing and ensuring the accuracy of these applications to participate. The presentation will outline the steps of creating a testing process using Automated Test Scripts and encourage interested individuals to collaborate and further develop the testing process in this session.
The proliferation of high-resolution satellite imagery has dramatically transformed urban planning, disaster response, and environmental monitoring domains. A persistent challenge, however, is the precise extraction and delineation of building footprints, as traditional methods often yield results with irregular shapes and misalignments. To address these limitations, we present CornerRegNet, a pioneering deep neural network designed to predict oriented building corners and subsequently regularize building footprints derived from satellite imagery.
Central to CornerRegNet's design is its unique ability to harness oriented building corners as primary anchoring points. This approach significantly streamlines and refines the shape of the extracted building footprints. Using advanced multi-modal autoencoders, CornerRegNet adeptly embeds a blend of corner features, their inherent orientations, and overarching semantic context. This intricate fusion of features allows the network to capture micro-level details and macro-level building structural context.
Enhancing the accuracy of corner prediction, the model incorporates active rotating filters. These filters, combined with uniquely devised equivariant convolutions, ensure that the predicted corners are not only spatially correct but also maintain their intended orientation concerning the building's overall layout.
A pivotal component of CornerRegNet is the integrated Graph Convolution Network (GCN). This GCN is instrumental in iteratively refining the positions of corners from the preliminary footprints. Further, the in-built shape regularizer within the GCN aids in achieving architecturally consistent and coherent footprints, effectively minimizing typical extraction anomalies.
In comparative evaluations, CornerRegNet stands out, consistently outperforming contemporary models. The network's resilience in managing and regularizing building shapes is a distinguishing attribute, even under challenging scenarios marked by occlusions.
In conclusion, CornerRegNet represents a unique advancement in building footprint extraction from satellite imagery. Its unique combination of corner orientation prediction and shape regularization techniques sets new standards for accuracy, efficiency, and architectural interpretability.
Driven by the interest in efficient monitoring and maintenance, digital twins are being established across various domains such as architecture, manufacturing, and urban planning. Constructing a digital twin requires diverse forms of data based on the target area and purpose. To visualize vast and complex regions, data acquisition is partitioned into aerial and ground levels. In the aerial level, aircraft and UAVs are utilized, while on the ground, methods like Mobile Mapping Systems (MMS) and Terrestrial Laser Scanners (TLS) are employed. However, the registration of cross-view images, such as aerial-ground images, is challenging due to differences in field of view, hindering smooth image matching. While manual registration through tie points established by humans is possible, it comes at a high cost and time, thereby impeding the efficiency of digital twin construction. Recent advancements in deep learning-based image matching are enhancing the potential for the registration of cross-view images. In this study, deep learning image matching is employed to automate the registration of cross-view images, contributing to efficient digital twin construction. Tie points are generated through deep learning image matching between nadir/oblique UAV images acquired via autonomous flights and ground images captured by terrestrial laser scanners. Utilizing these tie points, bundle adjustment is performed, and a subset of tie points is extracted based on reprojection error criteria. By incorporating these extracted tie points, image registration is performed again to produce the result. Experimental results demonstrate successful registration of previously unregistered ground images, and the addition of ground-exclusive areas enhanced the performance of the 3D model. This research is anticipated to contribute to the enhancement of efficiency and precision in digital twin construction across diverse domains, including spatial data analysis, 3D modeling, and national geospatial information systems.
This presentation delves into the fundamental characteristics, capabilities, and real-world usage of RGeo.
- Core concepts and data models of RGeo
- Basics of manipulating and querying geospatial data
- Integration with Ruby on Rails and real-world application examples using RGeo
This talk introduces a new open-source fast Memory-Efficient Flow Accumulation (MEFA) algorithm using OpenMP parallelization. Flow accumulation is one of the most important parameters for any hydrologic and/or environmental analyses. There have been many studies that focus on the improvement of its computational efficiency using OpenMP, MPI, and CUDA, but most of benchmark algorithms are not memory-efficient in terms of the number of write operations and the amount of bytes read/written. This memory-inefficient nature of those algorithms deteriorates their computational performance during parallel processing of the input flow direction matrix. The new MEFA algorithm has reduced its memory requirements in both writes and bytes by eliminating a need for intermediate output matrices and writing its final value to each cell only once deterministically. The new algorithm performed 45% and 19% faster than its OpenMP and MPI benchmark algorithms, respectively, using 20% less memory asymptotically.
The web browser is the most widely adopted open technology platform, and has powerful localization capabilities. For maps, however, these technologies can be inadequate. FOSS web map technology has often focused on commercial use cases in North America or Europe, and internationalization has not been a priority.
For FOSS4G Asia let’s take a high level tour of challenges that remain for Asian web mapping, including:
Data availability: the prevalence of localized data such as place names and tagging schemes in OpenStreetMap
Data preparation: Approaches to locale-specific vector tile generation using the Protomaps open source basemap generation tools
Map rendering: challenges for map display in Asian languages using Mapnik and MapLibre GL
We’ll look at specific examples of cartographic applications in CJK (Chinese, Japanese and Korean) locales, South Asia and Myanmar.
GeoServer is a web service for publishing your geospatial data using industry standards for vector, raster and mapping, as well as to process data!
GeoServer widely used throughout the world by organizations to manage, disseminate and analyze data at scale. GeoServer is an established open-source components used projects like GeoNode, geOrchestra and MapStore.
This presentation provides an update on our community as well as reviews of the new and noteworthy features in GeoServer. Attend this talk whether you are an user, a developer, or simply curious about what GeoServer can do for you.
Attend this talk for a cheerful update on what is happening with this popular OSGeo project, whether you are an expert user, a developer, or simply curious what GeoServer can do for you.
The interdisciplinary nature of forest ecology requires a seamless integration of traditional ecological methods with advanced computational tools. With this in mind, we introduce "skye-vision," an open-source collection of Python libraries specifically curated to bridge the gap in the Python ecosystem for forest ecologists. By leveraging remote sensing, photogrammetry, and machine learning, these libraries offer researchers an extensive toolkit for in-depth forest structure analysis.
Noteworthy functionalities of Skye-Vision are:
1. Advanced Visualization: Capability to generate and visualize detailed point clouds and meshes, offering insight into forest morphology.
2. Structural Analysis: Tools to quantify forest attributes, including leaf area index, leaf area density, plant volume density, foliage height diversity, and canopy cover.
3. Hemispherical Image Processing: Techniques for converting 360-degree images into hemispherical photos, enriching the study of forest canopies and their defining metrics.
4. Data Access & Processing: Simplified workflows for downloading and processing LiDAR data from GEDI and street-level imagery from Mapillary.
While the primary focus of Skye-Vision is forest ecology, the flexibility of these libraries ensures applicability in a wider context. For instance, their utility extends to ground truthing in various ecosystems or analyzing urban environments for optimizing shade-rich pathways, thereby aiding urban planning and conservation.
In this presentation, we'll provide a comprehensive exploration of Skye-Vision’s capabilities. Beyond its primary application in forest ecology, we will discuss potential extensions into other research domains. Attendees will gain insights into the library’s functionalities, its contributions to current research methodologies, and its potential for advancing both academic and practical projects. As we emphasize the spirit of FOSS4G, Skye-Vision exemplifies the collaborative and transformative power of open-source endeavors in the scientific community.
The quality of wine grapes is heavily influenced by environmental conditions such as geology, meteorology, geomorphology, and more. Therefore, it's necessary to consider these factors when selecting the location of a vineyard and the specific variety of wine grapes to be cultivated. However, these selections are often based on the experience of the farmers, making it a barrier for newcomers and raising concerns about potential economic losses from inappropriate choices of vineyard locations and varieties. In this research, we attempt to estimate suitable areas for wine grape cultivation using machine learning, utilizing the distribution of vineyards as supervised data. The evaluation areas include the surroundings of Ueda, Toumi, and Saku City in Nagano Prefecture. The locations of vineyards were identified through field surveys and interview surveys, and the data were aggregated on a 3rd level Japanese Standard Grid. The environmental factors used for evaluation include geology, slope angle, slope direction, average annual temperature, lowest annual temperature, and highest annual temperature. These factors are publicly available in Data PNG format, and a Python script was created to aggregate these data at the 3rd Standard Grid. By incorporating the spatial distribution of existing vineyards as the supervised data and analyzing various environmental factors, this approach aims to provide a more systematic and accessible way to identify optimal vineyard locations, thereby reducing reliance on individual farmer experience.
ZOO-Project is a WPS (Web Processing Service) platform which is implemented as an Open Source project and following the OGC standards, it was released under an MIT/X-11 style license and is an official OSGeo project. It provides a WPS compliant developer-friendly framework to easily create and chain web services. This presentation gives a brief overview of the platform and summarizes new capabilities and enhancement available in the new version. A brief summary of the Open Source project history with its direct link with FOSS4G will be presented. The new release comes up with a brand new ZOO-Kernel Fast Process Manager and, with the approved standard OGC API - Processes part 1: core. The new functionalities and concepts available in the latest release will be presented and described, also highlight their interests for applications developers and users. Apart from that, various use of OSGeo software, such as GDAL, GEOS, PostGIS, pgRouting, GRASS, OTB, SAGA-GIS, as WPS services through the ZOO-Project will be presented. Then, the ongoing developments and future innovations will be explored.
2023 Conference of Korea Spatial Information Society
2023 Conference of Korea Spatial Information Society
FOSS4G-Asia 2023 Business to Business Talk
FOSS4G-Asia 2023 Business to Business Talk
FOSS4G-Asia 2023 Business to Business Talk
The insecure environment in which slum households reside exposes them to many vulnerabilities as they struggle to protect their livelihood. Vulnerability is a dynamic and context-specific phenomenon. "livelihood" refers to a means of gaining a living, livelihood capabilities and tangible and intangible livelihood assets. People are less vulnerable if they possess more livelihood assets. Although, the relationship between people's livelihood assets and vulnerability remains largely untested. The sustainable Livelihood Framework overlooks the interactions and transformations between different livelihood assets. The paper aims to identify the underutilize dimensions and indicators to analyze the livelihood vulnerability in the urban poor context. The paper identifies 11 dimensions with 99 indicators through the literature review. The expert's opinion and the empirical study will help finalize the indicators list. An integration of livelihood capital can help develop a framework for assessing the vulnerability of urban livelihoods.
FOSS4G-Asia 2023 Business to Business Talk
A talk for digital twin special session
FOSS4G-Asia 2023 Business to Business Talk
In the present research the land surface temperature (LST) has been calculated from Landsat 8 data of Jeju island to understand the spatial distribution of temperature on island which would be strongly related with ecological characteristics of the island. The dates were selected based on seasons (Winter and Summer) March 12 and September 19, 2020, respectively. Results calculated from thermal band of Landsat 7 revealed the temperature variations in the island. Winter temperature varies from -3.7 to 31 oC degree Celsius while minimum temperature in Summer season was 20.6 and maximum 43.1 degree Celsius. The surface temperature was also calculated on different elevation for both the seasons. It was found that on 100 m elevation, in winter North and South directions have same temperature, comparatively, in Summer the North direction has 0.97 oC higher temperature over South. East direction showed 0.55 oC higher temperature as compared to West in Winter but in Summer season the case was opposite and West has 1.46 oC higher surface temperature from East. Interestingly on 100 m height in Summer, North and West has also same temperature (34.25 oC). In second case, on 500 m height for all four directions the LST was compared and results showed that South has 0.58 and 1.52 oC higher temperature in Winter and Summer season respectively from North. On the contrary, West has 3.33 and 0.5 oC higher temperature over East in Winter and Summer season respectively. It revealed that seasons and altitude have great impact on the LST in different cardinal directions of the island. From the results it can be assumed that the island ecological diversity would be strongly related to the geographical distribution of temperature.
FOSS4G-Asia 2023 Business to Business Talk
A talk for digital twin special session
Linear geomorphic features such as valleys and ridges tend to represent geologic lineaments and contribute important information in targeting natural resources, evaluating hazard risk and elucidate surface deformation caused by tectonic forces. Several methods have been proposed for automatic lineaments detection and are mainly implemented using proprietary software. As a result, refinement of algorithm and optimization of parameter selection remains unresolved.
The focus of this research was to investigate the process of extracting lineaments from Digital Elevation Models (DEM). The entire workflow was implemented and tested using the Free and Open Source GRASS GIS framework using existing functions and addons.
As a preliminary step, DEM was used to calculates terrain forms and associated geometry using the GRASS r.geomorphon function. As an alternative approach, application of Convergence Index that depicts the structure of the relief as channels and ridges was also applied. Test data covering parts of the Rokko mountain range in southeastern Hyōgo Prefecture, Japan, was used to hightlight valley systems used as input for the Canny edge. The Canny filter generates one-pixel-wide line(s) representing the most probable edge position and combines several steps to produce an edge and an angle map.
In the final step, the Hough Transform algorithm used to extract significant lines features by elimination the outlying pixels. The Hough transform is optimized by using angle of edges as additional input to search only pixels which are in the direction of a particular line.
The results using the data processing workflow were evaluated for the test site and the efficacy of automatic lineament extraction techniques were successfully demonstrated. Further, the algorithm was also tested on multi-direction relief shades that highlight terrain features which would not in terrain form or Convergence Index maps alone. As an ongoing work, optimizing parameter selection using machine learning is being tested to minimize subjectivity in linear feature extraction. The presentation will highlight on advantages of the approach to extract and validate “geologic” lineaments and demonstrate results obtained using high-resolution DEM.
Keywords: Lineaments, DEM, Terrain Forms, Convergence Index, Edge Extraction, Hough Transform
A talk for digital twin special session
FOSS4G-Asia 2023 Business to Business Talk
Accurate prediction of land use and land cover (LULC) is essential for effective
conservation, sustainable development planning, and managing competing user
demands. This study employs an ANN-Cellular Automation model to simulate and
predict future LULC maps in Chitwan District, Nepal. Three spatial variables, namely
DEM, distance from road and river, and aspect play a significant role in predicting the
LULC map of the area. The model achieves a high accuracy level, with a Kappa value
of 0.95636 indicating a strong agreement between observed and predicted 2020 LULC
maps. By utilizing the 2010 and 2015 LULC maps in combination with the same spatial
variables, the study projects the LULC maps for 2020 and 2030. The results reveal that
due to anthropogenic pressures, there will be a conversion of forest land and other
categories into built-up and cropland areas. The study also identifies dynamic changes
in land use and land cover patterns within Chitwan District. The area of water bodies
initially decreased from 76.88 sq. km in 2010 to 72.14 sq. km in 2015 but underwent a
significant expansion to 99.81 sq. km in 2020 and further to 106.39 sq. km in 2030.
Forest area exhibits a minor reduction from 1506.14 sq. km in 2010 to 1528.78 sq. km
in 2020, followed by a small increase to 1539.53 sq. km in 2030. Built-up areas witness
a steady increase from 3.90 sq. km in 2010 to 14.69 sq. km in 2020 and further growth
to 18.86 sq. km in 2030. The agricultural area shows a decline from 597.05 sq. km in
2010 to 519.36 sq. km in 2020, continuing the slight decline to 511.62 sq. km in 2030.
The study also observes an increase in the area of other wooded land from 35.99 sq. km
in 2010 to 57.34 sq. km in 2020, but a subsequent decrease to 43.58 sq. km in 2030.
Additionally, the analysis of land surface temperature (LST) maps in December for
2010, 2015, and 2020 reveals distinct patterns, with settlement areas consistently
displaying higher temperatures than forest areas. These findings highlight the impact of
land cover on local microclimates, potentially leading to increased urban heat island
effects in settlements. The study underscores the significance of considering land use
and land cover changes when managing urban and forest environments in Chitwan
Keywords: Land use and Land cover; ANN- Multi-layer perception; Predicted LULC,
FOSS4G-Asia 2023 Business to Business Talk
A talk for digital twin special session
FOSS4G-Asia 2023 Business to Business Talk
Cocoa is a major global commodity, and its production involves millions of mostly smallholder farmers in developing countries. Efficient and accurate counting of cocoa fruits during harvest is essential for effective resource management, yield estimation, and overall productivity improvement. Traditional manual counting methods are time-consuming, labor-intensive, and susceptible to errors, necessitating the exploration of innovative technological solutions.
This research introduces a novel approach to automatic cocoa fruit counting using a mobile application integrated with computer vision techniques. The proposed system leverages four services: 1) acquisition of farmers monitoring data using the mobile devices, 2) develop explainable AI model for automatic cocoa fruit recognition, 3) presentation of Cocoa pod detection and counting services through a web & mobile based application and 4) collect the farmers feedback on cocoa pod count to improve the services. To do this, Mobile app for data collection was co-designed and tested with local farmers in Cameroon. This app allows farmers to take geo-tagged photographs and transfer them to the server when connectivity permits. In total, 2132 Cocoa tree photos were manually annotated and different artificial intelligence (AI) models (e.g., faster R-CNN, darknet, ResNet-50, mobilenetv3) were developed and tested for automatic counting of Cocoa pods. The Faster R-CNN model with a ResNet-50 performed better and provides accuracy above 70%. The model was deployed on a web service and provides real-time prediction through a web- & Mobile based application
The insights gleaned from this research will spawn a new generation of tools for Cocoa farmers to use. It is an efficient, non-destructive, and low-cost method which offers useful information for them to plan agricultural work and obtain economic benefits from the correct administration of resources. Nonetheless, further research is needed to address potential limitations data collection strategies (specifically variable environmental conditions, complex Backgrounds for image collection), image Annotation challenges and ensure seamless integration into the existing agricultural workflow.
FOSS4G-Asia 2023 Business to Business Talk
A talk for digital twin special session
FOSS4G-Asia 2023 Business to Business Talk
Vietnamese students may feel differently about Open Source environment, depending on each student and their level of technological understanding. Especially, Free Open Source Software for Geospatial (FOSS4G) allows students to access and explore new technology easily, with plenty of applications in daily life and the society. It provides diverse and rich resources for students, being able to read, research and participate in the development of FOSS4G and helps students expand their knowledge and skills.
Subjects currently taught for students of the Information Systems Program include Open GIS Software Developments, GIS Programming Techniques, Data structure and Algorithms, and Data Mining with Open Source Resources. All the courses use Open Source software and Open data for labs and practice. Google Earth Engine codes development using Java Scripts and Python is introduced as extra selective course. QGIS Plugins development with Python is included as Mini-projects as well.
FOSS4G has a growing and supportive community that contributes through problem solving, mentoring, and evaluation of solutions. This gives students the opportunity to learn from experienced people, creating a positive community learning environment. This also enables students to create personal projects and applications, while also practicing software development and creativity skills. However, not all students can feel positive about Open Source. Some students may have difficulty reading and understanding source codes, or a feeling that they do not have enough time and knowledge to contribute to Open Source projects.
Our Vietnamese students have many ways to approach open source such as: learning from university curriculum, from various trainings online or offline, joining online communities and forums in the internet such as GitHub, GitLab and Bitbucket, just to name a few. They may participate in competitions and events like hackathons or software development competitions to practice their skills and learn deeper in the field. Free courses on online platforms like Coursera, edX or Codecademy are quite useful for beginners, where they can follow blogs and documentation. But most importantly, students should set goals and have a strong consent to study, work actively and continuously in this area, and that is indeed the goal of our FOSS4G curriculum at university.
A talk for B2B Session
A talk for digital twin special session
FOSS4G-Asia 2023 Business to Business Talk
City planning is a hard task given the scale and complexity of urban systems. Urban planners need better tools to help them understand and contextualise large urban systems. To address this concern, we introduce Urbanity, an open global tool that provides planners a seamless interface to generate city-scale representation of urban networks and their contextual features. Urbanity is developed with Python and built entirely on open source software and open data. The tool has been applied and tested at a global scale and demonstrates the powers and promises of urban analytics across any geographical location and scale. The proposed workshop session will include: 1) a presentation on Urbanity, 2) demonstration of its key functionalities, and 3) a step-by-step code along session to use Urbanity in urban science workflows. Urbanity plays an important role in helping urban planners and policymakers to make sense of the big data that is emerging from cities.
2023 Conference of Korea Spatial Information Society
2023 Conference of Korea Spatial Information Society
As spatial data in indoor space is quite different from outdoor space, building and processing indoor spatial data significantly differ from outdoor spatial data. In order to meet the demand for indoor spatial information, OGC has published a standard for indoor spatial information, called OGC IndoorGML, which is a standard data model and encoding schema. Accordingly, we need software and tools supporting indoor spatial data in IndoorGML. In this talk, we will introduce several open source software supporting OGC IndoorGML, which cover the lifecycle of spatial data from building indoor spatial data to applications. Several use-cases will be presented to explain how to apply these tools for specific projects.
3D buildings layer is essential to implement geospatial digital twin environment. Considering content construction cost, having 3D buildings of LOD 1 is regarded cost-effective for location-based visualization and simulation. 3D buildings of LOD 1 layer without texture can be easily created by extruding the building heights from building footprint's planar geometry. 3D building models are processed to be a streamable tileset over the web. To apply enormous number of building features at nation-wide scale, a global tileset by merging external tilesets was implemented.
The author has implemented creating 3D buildings of LOD 1 and tiling it as a streamable tileset of 3D Tiles. Per each building feature, 3D COLLADA model with local coordinate was created from buliding footprint's geometry of shapefiles by FME Desktop. In order to manage each building's metadata, KML reference model per building is applied while 3D model is linked as a COLLADA file. At a building footprint's centroid location, a KML is created containing longitude, latitude, altitude and linking a corresponding COLLADA file. The metadata in KML is generated from shapefile's attributes by a custom bash shell script. When KML/COLLADA models are ready, Cesium ion 3D Tiling Pipeline is used to create a per-building tileset of 3D Tiles. As the number of building features grow up to nation-wide scale, managing a single tileset is not only difficult to create but also causing network payloads. To resolve these issues, tilesets are granulated at manageable level and merged as a global tileset by merging external tilesets using open source 3D Tiles Tools. The characteristics of this talk is in its streamlined process workflow rather than each part of the job.
While the world of free open-source geospatial software is vast, mobile navigation remains relatively uncharted territory. Despite apps like OSMAnd and Organic Maps offering navigation, the landscape lacks comprehensive and accessible SDKs. Perhaps the most well-known prior work of this type is the Mapbox Navigation SDK. It was originally open-source, but over the years it progressively more difficult to plug in routes from other vendors, and eventually the critical components were made closed source.
The future of mobility is evolving. Micromobility is one important trend of course, but we are also seeing innovation in routing itself with the advent of wheelchair routing, preference for lit areas, and even custom routing for golf cart communities in the United States. However, the barrier to integrating these custom routes into your own apps remains fairly high. In this changing landscape, the need for a flexible and open-source navigation SDK is more pronounced than ever.
This talk will introduce Ferrostar, a new open-source, cross-platform, vendor-neutral SDK which aims for these lofty goals. It will give a high-level overview of the prior work, our decision to use Rust for the core, and some of the technical challenges we have encountered in trying to build idiomatic libraries for both iOS and Android.
This talk is aimed equally at mobile and systems developers.
The GRASS GIS community celebrated its 40th birthday this past summer! Its version 8.3 provides more than 360 changes compared to the 8.2 release, and it comes with many fixes and improvements in its modules and graphical user interface. The single-window layout is now mature and provided as the default UI. We have extensively cleaned up the C/C++ code by adopting the Clang format and fixing almost all compiler warnings. Not only that, we started modernizing the build system using CMake for cross-platform build automation, testing, and packaging. We are also converting the HTML manuals to easier-to-maintain Markdown/mkdocs to collect more user contributions. Last but not least, for internalization efforts, we have migrated our translation platform from Transifex to Weblate, which automatically creates pull requests with new translations. As always, we welcome new contributors for development, documentation, and translation!
OSMSG is an Open Source Python command line tool for OpenStreetMap statistics, can be installed through pip. It processes planet files directly, allowing for generation of stats for any time period without any database or additional resources. It can also process changefiles for customized analysis. OSMSG can extract country-level data and generate hashtag stats, supporting multiple output formats for data visualization. Ideal for researchers ,developers and mappers, it's a simple yet powerful tool for timely and relevant OSM data insights.
We're living in the world of APIs. CRUD operations are base of lot of operations. Many smart frameworks such as Django, Flask, Laravel provides out of the box solutions to filter the data, which covers almost all needs to separate data based on column values.
When it comes to Geospatial data, we expect to filter data based on their location property instead of metadata. This is where things get complicated, if you are using framework that doesn't have package, library built to handle such use cases, you are likely to be dependent on either database or any external package to handle it.
Fortunately Geodjango[https://docs.djangoproject.com/en/4.0/ref/contrib/gis/] (Django's extension) allows us to create databases which understands geometry and can process it[https://docs.djangoproject.com/en/4.0/ref/contrib/gis/geoquerysets/#gis-queryset-api-reference]. It also provides support to write APIs using Rest Framework extension [https://pypi.org/project/djangorestframework-gis/] which takes this to next level allowing user to output the data in various formats, creating paginations inside geojson, create TMSTileFilters, etc.
In this talk we'll scratch the surface of this python package and see how to build basic CRUD APIs to push, pull GIS data along with filtering it to the PostgreSQL database
Introduction of Session (Urban Thinkers Campus: Open Data and Artificial Intelligence for Urban Proximity)
[Urban Thinkers Campus] Opening Remarks
[UTC] Responsible AI for Cities by Soumaya Ben Dhau(E-governance unit of United Nations University, Guimarães, Portugal)
In the context of climate change, the increasing demand for energy-efficient buildings and sustainable urban development has become a pressing issue due to the significant proportion of global energy consumption and carbon dioxide (CO2) emissions
attributable to the building sector. This requires a concerted effort to reduce its environmental impact, and Geographic Information
System (GIS) applications are vital tools for achieving this by optimizing heat supply, calculating costs, analyzing profitability, and
balancing CO2 emissions. This study aims to address the challenge of achieving energy efficiency and reducing CO2 emissions in
the building sector, specifically at the district level. To this end, the research objective is to develop a QGIS plugin that can simulate
urban energy demand at the district level by integrating 2D data with CityGML files and connecting QGIS to SimStadt software
via API to visualize the simulated urban energy results in 3D on the Web Globe. The proposed plugin leverages the open-source
QGIS tool QField to capture building conditions and connect 2D and 3D data on urban energy simulation. Supplementary to this,
this plugin provides up-to-date information on energy demand, consumption, CO2 emissions, building component conditions via
updating related tables in the database. Decision-makers can use this comprehensive and user-friendly tool to understand and act on
the results, ultimately leading to a CO2-neutral district by 2045. The development of the QGIS plugin represents a significant step
towards sustainable urban development and climate change mitigation by utilizing GIS applications for optimizing energy demand
and reducing CO2 emissions in the built environment.
In the interconnected world, modern technologies can potentially revolutionize how we tackle and address crucial issues. The Internet of Things (IoT) is one of the emerging innovations, a network of linked devices that exchange and share data. IoT has opened up innovative solutions in various fields thanks to its ability to gather real-time data and enable accessible communication.
The UP Resilience Institute, in partnership with the local government of Quezon City, Philippines, has embarked on an ambitious project to incorporate an IoT system for rain and flood monitoring, striving to improve emergency preparedness and response for both the public sector and government. IoT provides unprecedented precision and timeliness in tracking and analyzing essential data regarding precipitation and flooding scenarios, making it a powerful tool to address the damages caused by disasters brought by floods. These events are associated with significant loss of life, property destruction, and community financial disruption. Traditional means of monitoring weather patterns and anticipating floods have limitations; however, IoT can revolutionize this process.
[UTC] AI powered Geospatial Platform, Artificial Intelligence Data Center
eeSDM은 Google Earth Engine을 활용하여 종 분포 모델링(SDM: Species Distribution Modeling)을 수행하기 위해 설계된 Python 패키지입니다. 이 패키지는 환경생태 및 데이터과학 분야 연구자가 종 분포와 환경인자 간의 관계를 탐색하고 예측하는데 효율적인 도구를 제공합니다. 이번 발표는 한국의 멸종위기 야생생물이자 천연기념물, 기후변화 생물지표종로 지정된 팔색조(Pitta Nympha)를 대상으로 eeSDM의 주요 기능을 소개합니다.
This talk considers the contributions of YouthMappers chapters in Asia. In addition to a regional overview, we highlight actions of students in Bangladesh and the Philippines to fill critical data gaps that support community access to information during emergencies, natural disasters, and pandemics using open data such as OpenStreetMap and open source tools like QGIS. Lack of data leads to poor decision-making at any time, but in the context of shocks and hazards, it can have an especially profound impact on local communities. By creating open geospatial data through OSM and by advancing the geospatial capacity of university students and local community members, local governing bodies will be able to plan for the well-being of their constituents and community members will have access to the information necessary to keep their families safe. This contributes to better health and well-being (SDG3)and a more resilient society in the face of impacts of climate change (SDG 13).
웹 기반 3차원 공간정보 서비스에서 '3D Tiles'는 OGC 표준 서비스 사양으로서 전 세계적으로 점유율을 늘려가고 있지만, 정작 '3D Tiles' 데이터를 만드는 수단은 상업적 제품인 FME나 Cesium Ion 말고는 찾아보기 힘들다. 본 발표에서는 상업적 수준의 3차원 공간정보 웹 서비스에서 어려움 없이 바로 사용이 가능한 '3D Tiles' maker 오픈소스 제품을 공개하고, 그 특징과 기존의 상업적인 제품 대비 특별한 점을 소개하는 기회를 갖고자 한다.
In web-based 3D geo-spatial services, "3D Tiles" is a standard service specification that is gaining ground globally, but the means to create "3D Tiles" data are hard to find other than commercial products such as FME and Cesium Ion. Therefore, I would like to take this opportunity to introduce an open source project "3D Tiles" maker, which can be used directly in commercial-level 3D geo-spatial web services without any difficulty, and introduce its features and special points compared to existing commercial products.
This research delves into the evolving interactions between sidewalk delivery robots and pedestrians within urban environments, focusing on the dual objectives of pedestrian safety and effective robot navigation in shared spaces. A comprehensive 3D digital twin model, replicating real-world urban settings, was designed to simulate the multifaceted engagements between humans, robots, and the urban fabric. Incorporated within this simulation was the Pedestrian Aware Model (PAM), a multi-agent system, employed to mimic both robot and human movements.
Employing an agent-based modeling approach, a series of scenarios involving pedestrians, wheelchair users, and robots coexisting in sidewalk spaces were dissected. The salient revelation from this study is the non-contributory role of robots to sidewalk congestion. Through programmed safety buffer zones, robots not only ensure pedestrian safety but also facilitate streamlined navigation, hinting at a feasible harmonious integration.
Despite this, the study unearthed certain challenges. Robots were predominantly implicated in collisions, whereas pedestrians often infringed upon set distance thresholds, emphasizing the imperative for enhanced strategic measures to alleviate these risks. However, the overarching inference remains optimistic: with judicious design and continuous research, robots have the potential to integrate seamlessly with pedestrians, enriching the urban milieu. The simulation model posited in this study stands as a pivotal resource for urban planners and policymakers, guiding them in formulating strategies and policies for smooth robot-human cohabitation.
Quantitative analyses further cement the significance of this research, underscoring nuances like the importance of ample safety buffers around robots to minimize collisions and enhance sidewalk traffic fluidity. In essence, this research pioneers in illuminating the pathways for optimized robot-human coexistence in bustling urban settings.
Flood Detection from Sentinel-1 imagery has traditionally relied on thresholding approaches. Conventional methods often face challenges in regions where the surrounding areas also exhibit low backscatter, resulting in inaccurate results. Furthermore, processing and conducting large-scale analysis pose significant challenges.
In this presentation, we aim to overcome these limitations by harnessing the power of Deep Neural Networks (DNN) and supplementary data, such as elevation and land-cover information, to enhance flood detection accuracy. This approach centers around training a neural network using open source cloud2street flood dataset. To achieve large scale analysis we leverage the capabilities of the Cloud Native Geospatial Tools, which helps us avoid downloading data when performing inference.
[UTC] Neighborhood planning support using big data and AI by Junyoung Choi (The Seoul Institute, Seoul, Korea)
In recent years, the frequent occurrence of natural disasters has become a serious social issue, making it extremely important for local residents to have accurate and timely access to disaster information that may affect their residential areas. This paper focuses on this challenge and reports an attempt to create a low-cost, locally operated web-based hazard map system for sharing regional information.
In contrast to conventional expensive systems, this system proposes a combination of open-source geographic information system tools and web technology to provide a low-cost and easy-to-use mechanism. It introduces a feature that allows local residents themselves to update and edit disaster preparedness materials and facility information easily, ensuring the accuracy of the information.
By using this system, access to disaster information becomes more convenient compared to traditional paper or PDF-based hazard maps, and it is expected to enhance overall disaster preparedness in the region.
The Digital Urban Environment project “toENG” aims to analyze the influence of shadow, wind, and noise on the urban environment . The project incorporates functionalities for user management, allowing users to create workspaces and projects to conduct analyses.
Within the project, users can select specific services for analyzing shadow, wind, and noise within their chosen area. Currently, the project is capable of analyzing shadow based on the 3D architecture of buildings.
To analyze shadow in the urban environment, users follow these steps: they select the shadow analysis option and specify the analysis area on a 2D map. They input analysis parameters such as the time of day for the analysis and the duration of the study. The analysis results are then presented through czml files, displaying the levels of shadow influence.
After completing the analysis, users can upload the 3D architecture of buildings in either IFC or 3D-tiles format. The analysis results and user-uploaded data are linked to “Re:Earth”, allowing visualization on a 3D map.
“toENG” project provides valuable insights into the interaction between environmental factors and urban spaces, aiding in informed decision-making and sustainable urban planning.
환경영향평가의 스코핑(Scoping)제도는 사업자가 환경경향평가서를 작성할 때 ‘선택과 집중’ 차원에서 꼭 평가해야 할 항목과 범위를 미리 정하는 절차이다(환경영향평가 스코핑 가이드라인(안) - 한경부). 이러한 환경영향평가의 스코핑 기능 및 역할의 강화를 위해 웹서비스 시스템을 개발하였다. 이 시스템은 평가항목별 환경요소 자동분석, 일반 및 중점 대상 항목 자동 선정, 평가 범위 설정 등을 지원하기 위한 환경전문가 의견 표출 등을 지원하고 있다. 첫번째. 사업지구와의 이격거리 / 포함여부 판단, 중첩 분석(중첩 면적 및 비율), 지형분석(표고분석, 경사분석, 능선분할분석)의 공간분석을 통한 평가항목별 환경요소 자동분석이 가능하다. 두 번째, 공간분석 결과를 활용하여 일반 및 중점 대상 항목의 자동 선정이 가능하다. 세번째, 환경전문가들이 선정한 평가항목별 알고리즘을 이용하여 평가 범위 설정, 심의의견 추천이 가능하다. 공간정보 분석 결과 환경영향평가 현황 분석에 활용이 될 것으로 기대된다.
[UTC] Unpacking the 15-minute City Model
한국에서 오픈스트리트맵을 널리 보급하기 위해 다양한 방면에서 노력하는 오픈스트리트맵 한국 커뮤니티를 소개합니다. 또한 각종 해외 사례를 토대로 오픈스트리트맵 한국 커뮤니티와 OSGeo 한국어 지부가 앞으로 소통, 교류하면서 얻을 수 있는 여러 이점을 제시합니다.
Identifying the current ecosystem extent and the underlying economic activities and opportunities therein requires spatial analysis utilizing the most recent data. This is made possible with the use of Earth Observation (EO) data retrieved from multi-spectral satellite imagery openly available to the public domain through the Copernicus and Landsat program of the European Space Agency (ESA) and the National Aeronautics and Space Administration (NASA) respectively. EO data has several advantages. It is supported by national governments, and inter-governmental agencies maintaining access to the data publicly available. It is free and open for use by researchers, governments, and by the commercial sector. The project areas are three municipalities in the island of Mindoro, Region IV-B MIMAROPA, Philippines. Specifically in the province of Occidental Mindoro. The three municipalities are the provincial capital, the Municipality of Mamburao, and the other two are the Municipalities of Rizal and Sablayan. The World Wide Fund for Nature – Philippines (WWF) commissioned the Community Environmental Economic and Social Accounting Matrix (CEESAM) study for these three municipalities in support of its expanded sustainable livelihood project for Tuna production and fisherfolk. This study’s component for spatial analysis is in support of the CEESAM for these municipalities. In summary, the study produced a land cover classification map achieving an average overall accuracy of 80% computed for all three municipalities. This is a good result since the accepted practice of achieving an overall accuracy within that 80% range is commendable. Processing the land cover classification was complemented with in situ observations using aerial imagery from light commercial drones and from ground based mobile imagery through QFIELD. Referencing the different imagery from earth observation data to aerial drone imagery, and ground based field observations led to the identification of spatial challenges and opportunities per municipality.
The study presented a workflow with a use-case for utilizing earth observation data using the latest and available update satellite imagery, complemented by in situ observations from aerial drone imagery and mobile field mapping app QFIELD. Also, the municipalities included in the study have common issues with regards to intermittent power interruption. Disruption in this vital utility slows down the production of any processing or cold chain facilities. Agricultural production primarily focus on annual crops for staples such as rice and corn. Mono cropping dominates the agricultural landscape with pockets of perennial crops which have room to integrate with traditional agriculture. Some areas have unfinished or unpaved farm to market roads. This results to inefficient logistics for the farmers in taking their produce to market. This could also add additional cost for them. Further, there is still a need for improving on post-harvest and post-catch support for farmers and fisherfolk with regards to facilities and market linkage facilitation.
[UTC] Discussion (Chair: Bongmoon Choi)
Junyoung Choi, The Seoul Institute
Seunghoon Han, CHAIRE ETI
Student Reporter, Korea Planning Association
Korea Research Institute for Human Settlement (TBA)
Open Street Map (OSM) is a collaborative and open-source platform that aims to create a detailed and freely accessible digital data of the world. The OSM data has been developed from approaches like Maxar and Bing Imagery helps to address the local level issues during the climate change and disaster. However, in densely populated urban area, the mentioned imagery resolution does not seem to fulfill the necessary resolution required to produce the digital data. In such a case the high resolution imagery is required. Hence, this study has adopted advanced approach of integrating high-resolution drone imagery, facilitated by OSM platform. The study is conducted in the Banepa Municipality of Nepal. With an aim of collecting the inventory of roof top which are feasible for farming, the concept of Volunteered Geographic Information System (VGIS) has been applied. A multi criteria analysis including Roof Shape, Tin Roof, Buildings Materials, Water tanks. Solar Panels, collected by VGIS technique, was used to extract suitable rooftop areas. Total number of houses in study area is 1055. The result shows 10.23 % (505.2 m2) are already adopting rooftop farming. Housing structure shows that 1.7 % are temporary, 54 % are concrete houses which include 16.68 % (Single flat roofs) and 37.25 % (double roofs), 16.1 % (with solar panels), and 47.86. % (with water tank). The preliminary analysis suggest that 54.02 % houses (approximately 48500 m2 ) are potential roof top farming areas identified by excluding Tin roof, water tank and solar panel areas. Having these data collected, the main aims of this study is to describe the methodological process and the strength and weakness identified during the process of developing the data inventory by OSM by integrating OAM. Finally, this study recommend the necessary intervention needed.
2D 군 지형분석서비스는 국방 디지털 트윈 플랫폼 Api에서 구현했던 프로그램을 저 사양 클라이언트 사용자를 위하여 2D기반에서 구현하고 기존 지형분석기능을 고도화하기 위해 개발한 서비스이다. 주요 분석 기능으로는 전술 공간 분석 기능, 전투지원 분석, 추가 공간분석으로, OGC 표준 Web processing service를 활용하여 분석 처리된 데이터를 가시화하였다. 이를 통해 군 작전환경에 적합한 의사결정을 지원할 것이다. 분석에 사용한 data는 대체 DB로 실상황과 유사한 데이터를 Raster 및 Vector 데이터로 수집 및 가공하여 OGC웹 서비스(WMS, WFS, WPS, WCS 등 서비스)로 제공한다.
The 2D military terrain analysis is a service developed to implement the program implemented in the defense digital twin platform API on a 2D basis for low-specification client users and to enhance the existing terrain analysis function. The main analysis functions include tactical space analysis, combat support analysis, and additional space analysis, and the analyzed and processed data was visualized using the OGC standard web processing service. This will support decision-making appropriate for the military operational environment. The data used in the analysis is an alternative DB, and data similar to the actual situation is collected and processed into raster and vector data and provided through OGC web services (WMS, WFS, WPS, WCS, etc..).
FOSS4G-Asia 2023 Seoul Closing