Welcome to the FOSS4G-Asia 2024 Workshop Day! Below are the key guidelines to ensure a smooth and productive experience.
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Workshops will be held in two main locations:
To ensure a smooth and enjoyable workshop experience, we kindly ask all participants to prepare the following:
We look forward to seeing you at the workshop and appreciate your cooperation in making the event a success! 😊
Instructor: Janica Kylle De Guzman, Ceejay T. Abilay
Building: Faculty of Social Sciences Building
Room: Computer Room 503 (Floor 5)
Time: 14.00-18.00
As the world increasingly goes digital, real-world information becomes accessible online, enabling virtual visits to locations through street-level imagery. This imagery is invaluable for capturing daily life and sharing local perspectives, making it useful for finding attractions or services remotely. Liminal spaces, which might seem trivial, can offer crucial insights for those in need of specific information. Our interactive workshop will introduce participants to KartaView and Mapillary, covering how to access and contribute to these platforms while having fun through demonstrations using GoPro 360 cameras. Open to all, this four-hour session includes a playful scavenger hunt, turning learning into an adventure as participants hone their skills in capturing and sharing street-level imagery.
Instructor: Massimiliano Cannata
Building: Professor Dr. Saroj Buasri SWU Innovation Building
Room: 34-1102 (floor 11)
Time: 9.30-13.30
Description of the workshop:
istSOS (http://istsos.org) is a software that has been designed to support sensor data management, from collection to management and quality assessment to dissemination using OGC and ISO standard formats. Following the evolution of software libraries, hardware technologies and IoT wide adoption, istSOS has been reimplemented in its version 4: named “Things”. Taking its tradition of being a Python implementation OGC compliant it takes advantage of latest solutions to support the Sensor Things API (STA) specification.
At the end of the workshop participants will understand the principles of the istSOS4 and of the STA standard; will be able to setup an istSOS4 STA service and will learn how to interact with the service both as a consumer or producer, using supplementary interfaces or pure python code.
Workshop outline:
The workshop covers the following activities:
1. Introduction to the SensorThings API standard and istSOS4
2. Installing the software & service set-up
3. Registering new sensors & data
4. Manipulation of observations (loading, modification, elimination)
5. Validation of observations (quality) and data analysis
6. Web integration of istSOS4 and STA
Pre-requisite knowledge for the attendees/background of participants:
Attendees do not have mandatory skills. Nevetheless, Docker usage, Web HTTP requests understanding, and basic python knowledge is recommended to execute exercises.
Material required from the participants:
Participant may bring their laptop with installed docker and a Hoppscotch (or similar software) if they want to follow the step by step during the workshops. Alternatively, they can just follow the activity of the presenters.
Instructor:Haneul Yoo, Yeonhwa Jeong, Sanghee Shin, SUNGJIN KANG, Dawoon KIM, Seungmin Kwon
In this workshop titled “Building an Urban Digital Twin using Open Data, Open Source, Open Standards, a mago3D way!”, participants will embark on a hands-on journey to create a digital twin of a selected urban area in Thailand. Leveraging open data from Overture Maps and NASA’s 30m resolution Digital Elevation Model (DEM), participants will learn how to integrate and process these datasets using open-source tools like mago3DTiler and visualize the final output in a Cesium-based 3D environment.
The workshop will focus on using open standards, specifically the OGC’s 3D Tiles format, to ensure compatibility and interoperability across platforms. Participants will begin by downloading and processing building data from Overture Maps and terrain data from NASA. These datasets will then be converted into 3D Tiles using mago3DTiler, enabling detailed and accurate 3D representations of the urban environment. The final visualization step will be performed using Cesium, where participants can explore the digital twin in an interactive 3D space.
This workshop is designed for GIS professionals, urban planners, and developers interested in the creation of urban digital twins using open technologies. By the end of the session, participants will have a comprehensive understanding of how to create, process, and visualize 3D urban data using open resources and standards.
In the “Building an Urban Digital Twin using Open Data, Open Source, Open Standards, a mago3D way!” workshop, participants will explore how to create a detailed and interactive digital twin of an urban area in Thailand. The workshop will guide participants through the following steps:
Data Acquisition: Learn how to source open data, specifically downloading building data from Overture Maps and a 30m resolution Digital Elevation Model (DEM) from NASA.
Data Processing: Use mago3DTiler, an open-source tool, to convert the acquired data into 3D Tiles, an OGC standard format. This process ensures the data is ready for 3D visualization while maintaining compatibility with other platforms.
Visualization: Implement the 3D Tiles in a Cesium environment to visualize the urban digital twin, allowing for an interactive exploration of the digital urban space.
This workshop is ideal for those interested in GIS, urban planning, or 3D visualization technologies. Participants will gain practical skills in working with open data and open-source tools, and learn how to apply open standards to their projects.
Instructor: Siriwat Suttipanyo , Siriya Saenkhom-or
Building: Faculty of Social Sciences Building
Room : Computer Room 504 (Floor 5)
Time: 9.30-13.30
Object snapping is a fundamental feature in Geographic Information Systems (GIS) that enhances the accuracy and efficiency of spatial data editing and analysis. This technique allows users to seamlessly align and connect geographic features, ensuring spatial relationships are maintained and data integrity is preserved. By snapping objects to predefined points, lines, or polygons, GIS professionals can create more precise maps and models, which is crucial for applications in urban planning, environmental management, and infrastructure development.
The process of object snapping involves algorithms that detect proximity between features and automatically adjust their positions based on user-defined criteria. This capability not only streamlines the editing process but also reduces the likelihood of errors arising from manual adjustments.
As web mapping technologies evolve, the need for intuitive and efficient tools becomes increasingly important. Implementing object snapping in web map applications not only streamlines the editing process but also ensures that spatial relationships are maintained, thereby enhancing the overall quality of geospatial data. This session will explore various methodologies for developing robust snapping algorithms with HTML and JavaScript, highlighting how these solutions can improve user experience and practical to implement.
For those looking to create a web map application capable of managing data for real-world tasks, such as adjusting the position of a streetlight to a specified area or managing objects to snap to geographic locations, this workshop will address those needs using practical HTML and JavaScript solutions.
This workshop will guide you through the process of creating a web map application that enables users to drag points or objects on the map and snap them to specific geometries such as points, lines, or polygons. For instance, you may want to drag a point and have it snap onto a line, or if there is a polygon area where you want to place an object, you can write additional conditions to restrict the dragging of the object to only the specific areas where it can be placed.
Prerequisite knowledge:
Participants are required to have basic knowledge of HTML and JavaScript.
Material required from the participants:
Laptop with internet connection.
Instructor: Lawrence Xiao
Building: Professor Dr. Saroj Buasri SWU Innovation Building
Room: 34-1104 (Floor 11)
Time: 14.00-18.00
We want to advance geospatial data science through first providing an optimal dev ops layer for anyone to build geospatial models/code in a GDAL native environment while being supported by co-pilot or GPT like Large Language Model that is trained/fine tuned on GDAL and geospatial python.
With our proprietary technology being built through entirely serverless architecture, we can significantly reduce costs and increase accessibility to powerful GIS dev ops infrastructure.
Spatial data science has come a long way since the great innovation of jupyter, followed by many packages such as xarray and rasterio built in python to allow integration and manipulation of many geospatial data formats such as geojson, geoparquet, geotiff and in more recent years zarr-type datacubes. And innovation never ceases to stop here. Due to the requirements of larger data workload and large computing power, many users today are moving to run geospatial python on the cloud. However, that is not a smooth transition. We see many things broken where data scientists struggled to put JupyterLab onto Amazon EC2 with GPU, and had to work with command line interface in order to install GDAL properly with python binding such that their geospatial python package would work.
On top of the environment setup every time a bigger VM is created, there are also many that struggle with learning to code in python, because they were transitioning from softwares such as QGIS or languages in school such as R. Python is not a very intense coding language, but still there are many nuances as to what packages we should use and how to get certain data sources such as OSM. What ChatGPT can do is increasingly powerful in other type of codings. So we started our research on LLM trained to server the purpose of generating geospatial python code that can run immediately in python notebook to complete geospatial tasks, and would love to showcase our research results with the geoscience community in a workshop manner in which they can try it themselves!
Here are the main features you can expect from the workshop:
Jupyter Notebook like experience optimised for GDAL in terms of cost and efficiencies. Yes, no more manually configuring your GDAL and PDAL libraries.
Having a GDAL native co-pilot: a Large Language Model trained and fine tuned on GDAL and geospatial native python to help anyone learn and code geospatial specific functions and models incredibly fast.
Still retaining a mix of familiarity of QGIS/ArcGIS Enterprise like interface that just works way better built for small GIS teams. For instance, the ability to collaborate on data labelling, seamless sharing of data/code/models and using the same LLM to run geospatial functions for those who are less technical!
Instructor: CHANDAN M C
Building: Professor Dr. Saroj Buasri SWU Innovation Building
Room: 34-1101 floor 11
Time: 14.00-18.00
This hands-on workshop delves into the creation of a web-based Spatial Decision Support System (SDSS) from the ground up, utilizing Geoserver as a key tool. SDSS development involves the integration of conventional and spatially referenced data, decision logic, and a web-based interface for spatial data analysis. The SDSS architecture comprises components such as Web Processing Service (WPS), Web Feature Service (WFS), Web Mapping Service (WMS), Geoserver/Map-server, and Geo-processing.
Participants will learn how to retrieve map features from a database, encode raw data into defined layers, and assess these layers within the core DSS. Sensitivity analysis aids in selecting the optimal alternative through a decision-making process. The resulting outputs are visualized through styled layers and a user-friendly graphical interface.
The workshop also explores the role of web servers in serving web content, processing HTTP requests, and delivering web pages, including HTML documents, images, style sheets, and scripts. Geoserver, an open-source Java-based software, is employed to view, share, and store spatial data on the web. It supports various spatial data formats and provides interoperability to publish data from diverse sources using open standards.
By the end of this workshop, participants will possess the skills to construct a robust web-based SDSS, empowering them to make informed spatial decisions using Geoserver and other essential web development tools.
Use Case and Applications, Education and Training, Data Management and Visualization
Instructor: Gérald Fenoy
Building: Professor Dr. Saroj Buasri SWU Innovation Building
Room: 34-1102 floor 11
Time: 14.00-18.00
The ZOO-Project will first be presented, along with details about the OGC API – Processes part 1: core. The participants will then learn how to set up the ZOO Kernel and to get an OGC API – Processes server running in a few simple steps. Some basic services will be presented to the attendees to give them the capability to reuse them later in their own application. Then, they will learn how to develop simple service using the Python language, through simple programming exercises. A ready to use client will be used to interact with the available OGC API – Processes services and the one to be developed. Participants will finally learn how to chain the existing services using the server-side Javascript ZOO-API.
Instructor: Wijae Cho, Taehoon Kim,Tsubasa Shimizu, TRAN THUAN BANG, Hirofumi Hayashi, Kyoungsook KIM
Building: Professor Dr. Saroj Buasri SWU Innovation Building
Room: 34-1101 Floor 11
Time: 9.30-13.30
OGC Moving Features standards are developed to provide application services for sharing and handling moving feature data in a standardized way. In particular, OGC MF-JSON (OGC 19-045r3) supports various types of moving feature representations in JSON format. OGC API–Moving Features–Part 1:Core (OGC API–MF Core) provides a standard and interoperable way to manage moving features data, which has valuable applications in transportation management, disaster response, environmental monitoring, and beyond. OGC API–MF Core also provides 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 Core and open source-based implementations, which are an extension of OGC API–Features. Specifically, the following items will be addressed in this workshop:
The below open sources will be used in this workshop:
– MF-API Server based on pygeoapi: https://github.com/aistairc/mf-api
– MobilityDB (and PyMEOS): https://github.com/MobilityDB
– STINUUM, visualization tools for MF-JSON: https://github.com/aistairc/mf-cesium
Each program will be installed using a Docker file.
Lastly, you can check many helpful information about OGC API–MF here: https://github.com/opengeospatial/ogcapi-movingfeatures
Instructor: Feye Andal, Fritz Dariel Andal
Building: Faculty of Social Sciences Building
Room : Computer Room 503 (Floor 5)
Time: 9.30-13.30
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.
The topics covered at the workshop are the following:
1. Introduction to Python
– Basics of Python syntax and data structures
– Overview of key geospatial libraries
2. Using Pandas for Data Wrangling
– Reading and manipulating tabular data
– Basic operations using Pandas
3. Using GeoPandas for Vector Data
– Analyzing vector data with GeoPandas
4. Using Rasterio and GeoCube for Raster Data
– Processing raster data with Rasterio
– Basic interpolation using GeoCube