[ONLINE] Earth observation use cases with AI

Tuesday 22 Sept 2026, 11:00 → 12:00 Europe/Prague

ZOOM (ONLINE)

Annotation

Recent advances in agentic AI are transforming how Earth observation (EO) data can be explored and analyzed. This workshop introduces the concept of agentic EO systems, where AI agents combine planning, tool use, and reasoning to autonomously perform multi-step geospatial analysis from natural-language requests. Drawing on recent developments, the session explores open-source architectures, data sources, and AI frameworks that could support similar capabilities in research and public-sector contexts. Through conceptual examples and interactive discussion, participants will examine opportunities, limitations, and governance considerations for trustworthy, explainable agentic EO workflows.

Benefits for the attendees, what will they learn

Participants will gain a practical understanding of how emerging agentic AI approaches can be applied to Earth observation (EO) workflows. By attending the workshop, they will:

• Understand the key characteristics of agentic AI systems, including planning, tool use, memory, and multi-step reasoning in an EO context.

• Learn how natural-language interfaces can support complex geospatial analyses without requiring extensive remote-sensing expertise.

• Become familiar with open-source and open-data components that can be combined to build agentic EO workflows, including Sentinel and Landsat data, EO APIs, open-weight models, and agent frameworks.

• Understand the architecture of AI agents interacting with EO data, from data discovery and processing to analysis and reporting.

• Explore practical EO use cases such as environmental monitoring, disaster assessment, urban expansion analysis, and infrastructure detection.

• Develop a critical perspective on the opportunities, limitations, and risks of agentic EO systems, including issues of reliability, explainability, transparency, evaluation, and governance.

• Exchange ideas with peers and identify potential applications of agentic EO approaches within their own research, projects, or organizational contexts.

Attendees will leave with a clear conceptual framework for understanding and evaluating agentic Earth observation systems, as well as practical insight into how such capabilities could be prototyped using open technologies.

Level

Intermediate. Participants should have a basic familiarity with Earth observation, geospatial data, or machine learning concepts. No advanced programming skills or prior experience with agentic AI systems are required.

Language

English

Prerequisites

Basic familiarity with Earth observation, geospatial data, or machine learning concepts.

Tutor

Karol Bot Gonçalves is a researcher at IT4Innovations National Supercomputing Center with expertise spanning artificial intelligence, data analytics, and sustainable digital technologies. Her research focuses on applying advanced AI and computational methods to real-world challenges, including environmental monitoring, Earth observation, energy systems, and decision support. Karol has an interdisciplinary background combining engineering, machine learning, and high-performance computing, and is actively involved in international research and innovation projects.

 

 

LUMI AI Factory is funded jointly by the EuroHPC Joint Undertaking, through the European Union's Connecting Europe Facility and the Horizon 2020 research and innovation programme, as well as Finland, the Czech Republic, Poland, Estonia, Norway, and Denmark.

This course was supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254).

All presentations and educational materials of this course are provided under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. 

Presentation: Earth observation use cases with AI

1 Q&A and closing