This workshop is designed to teach participants how to digitize public transportation routes using a collaborative approach and open-source tools. Attendees will learn how to organize a mapping event, capture field data using OSM Tracker, review and clean the data with QGIS and JOSM, publish it on OpenStreetMaps, and finally generate a GTFS file. This workshop is aimed at beginners, although basic knowledge of QGIS and an OpenStreetMaps account is recommended.

Geospatial Data Science with Julia presents a fresh approach to data science with geospatial data and the Julia programming language. It contains best practices for writing clean, readable and performant code in geoscientific applications involving sophisticated representations of the (sub)surface of the Earth such as unstructured meshes made of 2D and 3D geometries.

Join us for the Mergin Maps workshop for GIS admins to empower you to master collaborative mapping in the field. Besides setting up and synchronizing your Mergin Maps project with QGIS and your mobile device, we will also equip you with the skills to manage your collaborative mapping projects effectively, including sharing, transferring, setting roles and permissions, and handling synchronization conflicts. The workshop places also a special emphasis on good practices.

In this workshop you will learn about QGIS Server: working with it; plugins; using it in production; apps that use it etc.

Configure and deploy geospatial datasets, API, and mapping interfaces to the cloud for free (as in Beer).

This workshop will guide participants through the configuration of an XYZ host as an interface for spatial data hosted in a PostGIS database.

The database can be hosted as a free cloud service via neon.tech.

The XYZ application can be run as a local[host] express application or deployed as a cloud function via Vercel.

Every participant should be able to create a cloud service and be able to digitize and modify vector geometries and their properties stored in a PostGIS database.

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.

Learn the basics of pgRouting.
Representing a city as a graph for pedestrians and vehicles.

Going beyond all the common functions that most folks use PostGIS for. Delving into advanced use cases and using PostGIS in production workloads.

This workshop will address the data products and software tools of the Brazil Data Cube Platform (http://brazildatacube.org/). The Brazil Data Cube (BDC) project is producing more than 2 petabytes of Analysis-Read Data (ARD) and multidimensional data cubes of satellite images Landsat-8/-9, Sentinel-2, CBERS-4/-4A and Amazonia for the entire Brazilian territory. Besides that, the BDC project is developing software tools to deal with big data sets, to extract image time series from Earth observation (EO) data cubes and to produce land use and land cover information using image time series and machine learning.

This workshop will address concepts of EO data cubes and satellite image time series analysis as well as promote hands-on activities using Python language for: (1) Discovering, accessing and viewing EO data cubes; (2) Extraction of satellite image time series from EO data cubes; (3) Analysis of satellite image time series; and (4) Extraction of land use and land cover trajectories. All software tools of the BDC platform will be demonstrated: BDCExplorer, TerraCollect, Satellite Image Time Series (SITS) R package and the web services, SpatioTemporal Asset Catalog (STAC), Web Time Series Service (WTSS) and Web Land Trajectory Service (WLTS).

The main goal of the BDC is to support environmental monitoring, land use and land cover applications, agricultural management, and other applications that require consistent and temporally structured EO satellite images and geospatial information. Image time series extracted from EO data cubes improve our understanding of environmental patterns and processes. Instead of selecting individual images from specific dates and comparing them, researchers can track change continuously. Satellite image time series analysis captures subtle changes in ecosystems and improves the quality of land classification.

BDC provides ARD and multidimensional data cubes of images from sensors onboard Landsat-8, Landsat-9, Sentinel-2, CBERS-4, CBERS-4A and AMAZONIA-1 satellites. Products like MOD13Q1 and MYD13Q1, which are derived from TERRA/MODIS and AQUA/MODIS satellite/sensor are also incorporated into BDC as data cubes. Using the same technologies to produce EO data cubes, BDC also produces Visualization Mosaics. BDC manages more than 2 petabytes of data, which brings big data challenges. Thus, the BDC platform also provides software tools to efficiently deal with these big EO data sets.

The software tools of the BDC platform that will be presented in this workshop are: (1) BDCExplorer: web portal to access, visualize and download EO data cubes and extract image times series; (2) TerraCollect: web application to collect and analyze land use and land cover samples, based on satellite image time series; (3) Satellite Image Time Series (SITS) R package for land use and land cover classification using satellite image times series and machine learning; and (4) Web services and R and Python clients. The web services include: (1) Spatio Temporal Asset Catalog (STAC); Web Time Series Service (WTSS) to extract image time series from EO data cubes; (3) Web Land Trajectory Service (WLTS) to extract land use and land cover trajectories from distinct data sets.

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.

QField is the field data collection app for QGIS, with over 325K active users and 1Mio downloads. QFieldCloud, released in early 2023, is the sync platform for QField and QGIS.
We'll show you how to make the most of these state-of-the-art tools.

Artificial intelligence is disrupting many fields of knowledge. The Geoscience domain is not exempt from such disruptions. For that reason, understanding machine learning workflows is crucial. This workshop aims to demystify artificial intelligence (AI) for geospatial applications, introducing key concepts, tools, and techniques. Participants will gain hands-on experience with open-source AI tools and learn the fundamentals of AI and its practical applications in geospatial science. We will work with Jupyter notebooks and Python. The workshop will focus on machine learning, specifically in unsupervised and supervised machine learning approaches. In this way, participants will be able to get a basic understanding of artificial intelligence and how it can be applied.

During the first part of the workshop, instructors will dedicate a few minutes to setting up the basis of AI by introducing key concepts required to build a GEO AI project. During the second part of the workshop, participants will develop geo-AI projects concerning supervised classification, regression, and clustering. They will also apply common metrics for model performance evaluation and visualize the results. There will be opportunities for questions and discussions during the last part of the session.

Gleo is a nascent javascript WebGL mapping library. It aims to find a niche alongside Leaflet, OpenLayers, MapLibre and Deck.gl. It was first introduced at FOSS4G 2022.

This workshop/tutorial will start with the simplest examples of how to embed a Gleo map in a webpage, then go through different available cartographic symbols and data loaders/symbolizers, and end with a deep dive into how the data is stored in WebGL data structures in the GPU.

What is GPSSample App?
GPSSample is an Android-based mobile application to facilitate rapid statistical sampling and navigation back to selected locations in the field when conducting household surveys and rapid assessments. The app is designed to streamline the enumeration and statistical sampling process and can be used in areas with limited internet and cell phone connectivity.

Why is it important?
The 2018 World Health Organization guidance for vaccination coverage cluster surveys recommends that routine immunization coverage surveys use probability sampling methods and generally use census data derived from enumeration areas as the sampling frame.
• Existing process: As many countries lack up-to-date population sampling frames, teams are often sent to map and list households (HHs) in enumeration areas using a set of devices. Upon returning to an area with wifi, data from each device must be uploaded to a server, collated, and reviewed to ensure that no sections of the enumeration areas (EAs) were missed during mapping. Once the sampling frame is verified, a sample of HHs can be drawn and another team is deployed to navigate to selected HHs to conduct the coverage survey.
• Improved process: By streamlining this enumeration, statistical sampling, and survey process into a single field visit, GPSSample can substantially reduce time and costs associated with conducting coverage surveys, which has broad benefits to ministries of health and public health partners. In GPSSample, teams can collect foundational public health systems data used in providing services to the most vulnerable populations such as locations of health facilities and vaccination points and missing villages in hard-to-reach places that are often overlooked in microplanning efforts for immunization services.

How does it work?

Enumeration teams can use GPSSample to rapidly enumerate HHs in an EA. Teams can send encrypted data to a supervisor via new local-only mobile hotspot QR code technology developed for GPSSample. The supervisor easily presses a button to generate a simple random sample from eligible HHs and sends the list of selected HHs back to the teams. Using GPSSample, teams can navigate back to the selected HHs to conduct surveys. GPSSample integrates seamlessly with other survey tools like OpenDataKit or XLSForm apps.

GPSSample app has tiered roles facilitating study design and implementation and securely transfers data from GPSSample to ODK and Kobo Collect for participants to conduct the longer household survey.
Tiered roles:
• The Admin sets up the configuration for an area, study questions, and defines sampling design
• The Supervisor defines teams, manages field work, selects sample, reviews data from listing and from survey
• The Enumerator maps and lists households, collects points of interest, syncs data with supervisor
• The Data Collector navigates back to selected sample, and completes the longer survey

Selected Use Cases
• HH surveys: A district-level HH malaria survey is planned to estimate malaria prevalence. 14 HHs will be selected in each cluster. High precision is required as the country is approaching elimination. GPSSample is used to map HHs in clusters and capture structure eligibility for the HH survey and an upcoming indoor residual spraying (IRS) campaign in the app. Three teams working in a cluster sync data with their field supervisor who draws the sample in GPSSample the same day listing finishes. In GPSSample, teams use points of interest and HH listing details just collected to navigate to HHs selected for the HH survey. A month later, IRS teams use IRS eligibility data to ensure all eligible HHs have been visited.
• Zero dose children: Children missing the first dose of diphtheria, tetanus, and pertussis (DTP) containing vaccine (“zero dose”) account for an estimated 50% of vaccine-preventable deaths. Zero dose children are challenging to identify since they often reside in urban areas, remote communities, or conflict-affected areas, where population estimates are unreliable. By offering the ability to rapidly conduct enumeration and mapping of targeted areas, GPSSample provides a valuable solution for identifying zero dose children. Through HH listing, GPS coordinates and contextual information can be collected, ensuring precise HH location and incorporating details into digital catchment maps. In conflict zones with restricted team mobility, satellite imagery and mapathons are used to identify inhabited structures. Once structures are identified, teams can safely conduct HH listing to gather information on zero dose individuals. GPSSample streamlines the zero-dose listing process.

Workshop Objectives
Participants will gain an applied knowledge of the GPSSample application and experience setting up the application for a household cluster field study.

By the end of the workshop, participants will gain applied experience to:
• Explain the tiered role of GPSSample application
• Set up a new configuration and study within GPSSample with a mapping and listing questionnaire, sampling configuration, and link to an ODK household survey
• Create clusters directly in GPSSample by drawing, specifying a size, or walking the perimeter or bringing in known boundaries
• Use Android tablets or phones (OS 8+) to navigate to a cluster, use GPSSample to map and list households and points of interest, and to sync data to and from devices
• Experience the ease of sampling in an offline field environment and navigating back to selected households
• Decrypt example field data using the GPSSample Decoder App

While GPSSample is designed for health surveys, it can be used in other scientific fields too.

Introduction:
This workshop presents the importance of FOSS tools such as WebODM and QGIS for drone image processing. We will cover the pre-processing of control point photographs in QGIS using the LFTools plugin, followed by the post-processing of photogrammetric products (orthophotos, elevation models, and point clouds) generated by WebODM. Additionally, some applications with the CloudCompare software will be discussed.

QGIS and the LFTools Plugin:
QGIS is an open-source Geographic Information System (GIS) that enables the visualization, editing, and analysis of geospatial data. Known for its flexibility and robustness, QGIS supports a wide variety of raster and vector data formats and integrates with other GIS software. One of its main advantages is the ability to expand its functionalities through plugins.
QGIS plugins, developed by the global community, allow users to add new tools and features such as advanced geoprocessing, field data collection, spatial analysis, image processing, and 3D visualization. Notable examples include the LFTools plugin for drone photo processing and the GRASS plugin for advanced geospatial analysis. This extensibility makes QGIS a versatile and powerful tool for professionals in various fields who need geospatial solutions.

OpenDroneMap / WebODM:
OpenDroneMap (ODM) is an open-source software for processing drone images. It transforms aerial photos into orthophotos, digital terrain models (DTM), digital surface models (DSM), and 3D point clouds. ODM is widely used in mapping and cartography applications, offering an accessible and customizable alternative to proprietary software. The open-source community supporting ODM ensures continuous updates and the inclusion of new features.
WebODM is the web interface for OpenDroneMap, designed to facilitate access and use of ODM through a browser. It provides a user-friendly interface for uploading, processing, and viewing drone images. With WebODM, users can manage mapping projects, visualize results, and perform analyses directly on the platform, making the drone image processing workflow more accessible and efficient. The web interface allows for collaboration and data sharing, increasing productivity and accessibility of drone image processing tools.

CloudCompare:
CloudCompare is an open-source software specialized in the visualization and processing of 3D point clouds. It allows users to visualize, edit, analyze, and compare point clouds and 3D mesh models, performing tasks such as filtering, segmentation, data registration, and volume and distance calculation. Initially developed for comparing high-density point clouds, CloudCompare is widely used in various fields, including geography, engineering, and archaeology. Its active community ensures continuous improvements and support, keeping the software updated and relevant to user needs.

Workshop Content:
1. Brief Introduction to Drone Image Processing:
 - Importance and applications in cartography.
2. Loading and Selecting Photos in QGIS by Location:
 - Techniques for image selection and organization.
3. Filtering by Date and Time with the "gpsdate" Function:
 - Using metadata to filter images.
4. Separating Processing Blocks:
 - Strategies for dividing large datasets.
5. Photo Histogram Adjustment:
 - Improving image quality for processing.
6. Processing in WebODM/OpenDroneMap:
 - Steps to process drone images using WebODM.
7. Why Use Alpha Band?
 - Advantages and disadvantages of using the alpha band in images.
8. Photometric and JPEG Compression:
 - Compression techniques for efficient storage and processing.
9. Efficiency of TIFF Pyramids with Compression:
 - Benefits and techniques of using TIFF format with pyramids.
10. ECW vs. TIFF Comparison:
 - Comparison between different image formats.
11. Generating Point Clouds with DSM and Orthophoto:
 - Production and use of point clouds and orthophotos.
12. Adding Value to Drone Mapping:
 - Strategies to increase the utility and accuracy of drone mapping.

Requirements:
As the workshop is only 2 hours long, participants should bring their notebooks with QGIS, LFTools, WebODM, and CloudCompare installed.

Who Should Attend This Workshop:
This workshop is intended for professionals and students in the fields of geotechnology, cartography, geography, engineering, surveying, architecture, urban and environmental planning, among others. It is especially useful for those who use drones for mapping and need to ensure the precision and quality of the collected data. Additionally, it is ideal for anyone interested in learning how to use open-source tools for drone image processing, such as QGIS, LFTools, WebODM, and CloudCompare, and who seeks to enhance their skills in geospatial data manipulation and analysis.

Overture Maps Foundation provides millions of points of interest (POIs) as open map data. This gives OpenStreetMap users a chance to improve the map using open source geospatial tools in Python, along with various open APIs, to compare and validate the data. This workshop will let users learn how to do it in a place on the map of their own choosing.

Summary

The Drone Tasking Manager (DroneTM) is a suite of tools for communities to use low-cost, safe, accessible drones to collect high-quality aerial imagery and data on the scale of thousands of square kilometers.

The Problem

Drone imagery and data is useful for mapping, disaster resilience, and a variety of humanitarian, development, and economic activities, but drone mapping tools have not been well-adapted to community use in development and humanitarian contexts—until now.

While imagery acquisition by local communities using small, lightweight drones is possible, the tools for doing so are mostly proprietary and not well-adapted to local needs. Commercial flight planning applications such as Pix4D Capture, DroneDeploy, Litchi, Dronelink, and similar programs do not work with recently-manufactured low-cost drones, and are not effective for multiple users to coordinate their efforts, making them ineffective for colllecting imagery and data over large areas.

The Solution

DroneTM, built by HOT and Naxa with support from the World Bank's Caribbean Disaster Risk work, GFDRR, OpenDroneMap.org, and many others, is a Digital Public Good for everyone, but particularly intended to support local communities who don't have budgets for expensive equipment and software. It's Free/Open Source Software made by, with, and for communities.

The Workshop—Practical Commmunity Drone Operation and Data Acquisition

In Belém, we would like to offer a workshop in which participants will:
- Use the Drone Tasking Manager desktop application to create projects, defining and Area of Interest to cover with multiple drones by multiple teams concurrently.
- Use the DroneTM mobile Web app to create, access, and download individual flight plans for low-cost drones
- Execute short (~15minute) automated drone flights using the DroneTM flight plans (we encourage people to bring their own drones, but we will supply several drones—properly registered and and legal to operate—so that everyone can experience it).
- Process the resulting data into orthophoto images, Digital Elevation Models, and 3D meshes (using OpenDroneMap as part of the DroneTM back end)
- Import the orthophoto images into QGIS for visualization. We won't have time—or scope—to get into use and analysis of the imagery, but people will be able to get it loaded up into a QGIS canvas and ready for use.

Participant Profile

We hope to welcome participants who are:
- From, working with, or supporting communities with actual needs and/or projects that can benefit from aerial imagery and data. We especially hope to welcome those who have not been able to access drone technology due to barriers related to cost and complexity.
- Interested in testing, supporting, or using drone technology and meeting like-minded folks
- Interested in advocating to regulatory agencies, donors, and other stakeholders to promote inclusive, community-based drone empowerment
- Due to existing inequities in the drone and tech sector, women will be prioritized for hands-on practical flight operations experience

Participant Requirements

• Owning or bringing a drone is not required. However, any participants with drones that are legally permitted to fly in Brazil are encouraged to bring them, particularly the DJI Mini 4 Pro (other drones are supported, but only the DJI Mini 4 Pro is guaranteed to work at this stage; there will be updates to the list of compatible devices before the workshop).
• Drone piloting experience is not required. We will cover the basics of manual flight, but the DroneTM flights will be automated and supervised by experienced operators.
• Laptops are useful for the project creation module. Those who do not have laptops will be able to follow along on a projector.
• Reasonably decent-spec Android devices are helpful to explore the mobile app.

The goal of this workshop is to explain on a hands-on demo what GraphQL is all about using a spatial database. While showing GraphQL features (including spatial features) we’ll demonstrate how they can be perfectly implemented on top of an existing PostgreSQL/PostGIS spatial database. We’ll discuss pros and cons of this technology over REST, highlighting a few important points in which they differ.

During the workshop we will:

• Learn how to use, create and manage a fully spatial GraphQL API using PostGraphile and a common, existing PostgreSQL/PostGIS spatial database;
• Publish spatial and non-spatial data;
• Create processing/analysis endpoints that rely on PostgreSQL/PostGIS functions;
• Secure the data with Row-Level Security (RLS) set at the database level and transpose those credentials to the API in a straightforward way. Users can then make use of JWT tokens to authenticate.
• Extend the GraphQL API using plugins that can be used to create complex endpoints that need to be created at the server level with NodeJs and Javascript or Typescript.

Ever wonder what GDAL is doing under the hood when you read a GeoTIFF file? Doubly so when the file is a Cloud-optimized GeoTIFF (COG) on a remote server somewhere? Have you been wondering what this new GeoZarr thing is all about and how it actually works? Then there's the whole Kerchunk/VirtualiZarr indexing to get cloud-native access for non-cloud-native data formats, what's that about?

Cloud-native geospatial is all the rage these days, and for good reason. As file sizes grow, layer counts increase, and analytical methods become more complex, the traditional download-to-the-desktop approach is quickly becoming untenable for many applications. It's no surprise then that users are turning to cloud-based tools such as Dask to scale out their analyses, or that traditional tooling is adopting new ways of finding and accessing data from cloud-based sources. But as we transition away from opening whole files to now grabbing ranges of bytes off remote servers it seems all the more important to understand exactly how cloud native data formats actually store data and what tools are doing to access it.

This workshop aims to dig into how cloud-native geospatial data formats are enabling new operational paradigms, with a particular focus on raster data formats. We'll start on the surface by surveying the current cloud-native geospatial landscape to gain an understanding of why cloud native is important and how it is being used, including:

• the core tenets of cloud-native geospatial formats
• cloud-native data formats for both raster and non-raster geospatial data
• SpatioTemporal Asset Catalogs (STAC) and how STAC is used for raster data discovery and access
• high-level tooling like odc-stac that can leverage STAC and Dask to scale processing of cloud-native data

Then we'll get hands-on and go deep to build up an in-depth understanding of how cloud native raster formats work. We'll examine the COG format and read a COG from a cloud source by hand using just Python, selectively extracting data from the image without any geospatial dependencies. We'll repeat the same exercise for geospatial data in Zarr format to see how that compares to our experience with COGs. Lastly we'll turn our attention to Kerchunk/VirtualiZarr to see how these technologies might allow us to optimize data access for non-cloud-native formats.

pygeoapi is an OGC Reference Implementation supporting numerous OGC API specifications. This workshop will cover publishing geospatial data to the Web using pygeoapi in support of the suite of OGC API standards.

We will present and exemplify a subset of GRASS GIS toolsets for satellite imagery data processing and analysis in combination with other core modules and addons in a workflow going from data search and download to supervised classification of different scenes and visualization of results.

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.

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.

• 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.

This workshop will fully utilize QGIS + QGIS Server to create web map-based applications. We will use QGIS, QGIS Server, and PostgreSQL.

Projects will be stored on PostgreSQL, on the server side. A database trigger will extract all the necessary information from the project (using PyQGIS) to produce a wonderful web map application. Project layers will be served by QGIS Server. Participants will be able to create QGIS projects and all the same information and customizations will be reflected in the web app.

Cloud Native Geospatial for Earth Observation Workshop

The advent of cloud computing has revolutionised the capabilities of researchers and professionals globally, helping them to access and analyse Earth observation (EO) data more easily than ever. Despite the well-understood tools and technologies, such as cloud-optimised GeoTIFFs and the spatio-temporal asset catalog (STAC) specification, many EO professionals have not yet had the opportunity to practically apply these innovations. This workshop aims to bridge that gap by showcasing how cloud-native geospatial technologies simplify the process of working with EO data, using Python as the primary programming language.

Participants will delve into a real-world case study focused on documenting land productivity metrics, a crucial component for monitoring the UN Sustainable Development Goal (SDG) indicators for 15.3.1. The workshop will utilise NASA’s Harmonized Landsat and Sentinel data, accessed through Earthdata, to explore the land productivity metric in depth.

Our workshop hosts, Caitlin Adams and Alex Leith, bring extensive experience from their work on large-scale cloud-native programs such as Digital Earth Africa, Digital Earth Australia, and the recently launched Digital Earth Pacific. These projects leverage petabytes of data to create valuable information products that inform decision-making processes across countries and continents.

Throughout the workshop, participants will gain hands-on experience and insights into how cloud-native geospatial technologies have significantly enhanced the ability to manage and analyze large volumes of EO data. By the end of the session, attendees will have acquired practical examples and knowledge to further develop their skills in this innovative field.

This tutorial is supported by the CEOS Systems Engineering Office and aims to equip participants with the tools and techniques necessary to harness the full potential of cloud-native geospatial technologies in their work.

Key Learning Objectives:

• Understanding Cloud-Native Geospatial Technologies: Learn the fundamentals of cloud-native geospatial technologies and their significance in simplifying EO data workflows.

• Practical Application with Real-World Data: Engage in hands-on exercises using NASA’s Harmonized Landsat and Sentinel data to calculate land productivity metrics relevant to the UN SDG indicators for 15.3.1.

• Exploring Advanced Tools: Gain familiarity with key Python packages for EO data analysis, including xarray, dask, pystac-client, odc-stac, and odc-geo.

• Developing Reproducible Workflows: Understand how to build reproducible workflows that can be executed anywhere, independent of specific computing environments.

• Leveraging Global Data Repositories: Learn how to access and utilize global free and open EO datasets, and how these resources can be integrated into cloud-native workflows.

Target Audience:

This workshop is designed for Earth observation professionals, researchers, data scientists, and students who are interested in enhancing their skills in EO data analysis using cloud-native technologies. Prior experience with Python is beneficial but not required, as the workshop will build up the workflow step-by-step.

Workshop Structure:

1. Introduction to Cloud-Native Geospatial: Overview of the cloud-native geospatial paradigm and its importance in EO data analysis.

2. Real-World Case Study: Detailed examination of the land productivity metric using NASA’s Harmonized Landsat and Sentinel data.

3. Hands-On Exercises: Step-by-step practical sessions to develop and apply Python-based workflows for EO data analysis.

4. Discussion and Q&A: Interactive session for participants to ask questions, share insights, and discuss the applications of cloud-native geospatial technologies in their work.

Instructors:

• Caitlin Adams: Senior Data Scientist at FrontierSI, specializing in machine learning applications for EO datasets. Internationally recognized for her contributions to EO and passionate about educating others in the field.

• Alex Leith: Technical Director at Auspatious, with a proven track record in delivering operational data infrastructure and making data accessible for informed decision-making. Alex is a leader in the EO community, dedicated to promoting sustainability through better data access.

This workshop is supported by the CEOS Systems Engineering Office, ensuring high-quality content and expert guidance throughout the session.

Join us to explore the transformative potential of cloud-native geospatial technologies and elevate your EO data analysis capabilities to new heights.

Discover the power of Re:Earth Visualizer and Plugin system, an open-source WebGIS developed by Eukarya Inc., that enables users to easily visualize geospatial data on the web—no coding required and extend functionality by plugin system. This interactive workshop caters to both non-technical users and developers, progressively increasing in technical depth. You'll be guided through:

1. Setup local environment for Re:Earth visualizer and plugin
2. How to use/interact with Re:Earth Visualizer
3. Key Point About Re:Earth Visualizer
4. Introduce Re:Earth plugin and system architecture
5. Create a simple plugin

The first half of the workshop is designed for all skill levels, ensuring everyone can create basic interactive maps. The latter half delves into more advanced topics, allowing developers to learn about plugin development for Re:Earth Visualizer. By the end, you'll have the skills to publish your own interactive online maps and a deeper understanding of how Re:Earth can streamline your geospatial workflows. Whether you're a GIS professional, developer, or enthusiast, join us to unlock the potential of this innovative WebGIS platform.

Unlock the full potential of your fieldwork with QField's groundbreaking project and app-wide plugins. Join our hands-on workshop at FOSS4G to explore the powerful customization and functionality enhancements made possible by QField 3.3. Learn how to create and implement plugins tailored to your specific needs, streamline data collection, and improve field operations. Whether you're a beginner or an advanced user, this session will equip you with the skills to take your field mapping projects to the next level. Don't miss this opportunity to elevate your GIS capabilities and enhance your fieldwork efficiency with QField.

REQUIREMENTS:
+ A computer with a web browser and your favorite text editor.
+ The QField desktop application downloaded (execute the downloaded file to test that it runs):
.... Windows: qfield-v3.4.5-windows-x64.exe
.... GNU/Linux: qfield-v3.4.5-linux-x64.AppImage
.... Mac OS: qfield-v3.4.5-x64-osx.dmg
+ QField app installed in your smartphone/tablet. (Optional, but nice to have for testing).

Join us to learn everything you need to know to build an OpenLayers application. Whether you're new to the library, haven't used it in a while, or are a regular user wanting to dig into some new features, this workshop is for you! The workshop is divided into a series of modules with hands-on exercises walking through OpenLayers concepts and functionality.