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SUMMARY:[ONLINE] Earth observation use cases with AI
DTSTART:20260922T090000Z
DTEND:20260922T100000Z
DTSTAMP:20260803T205100Z
UID:indico-event-405@events.it4i.cz
CONTACT:training@it4i.cz
DESCRIPTION:\nAnnotation\nRecent advances in agentic AI are transforming h
 ow Earth observation (EO) data can be explored and analyzed. This workshop
  introduces the concept of agentic EO systems\, where AI agents combine pl
 anning\, tool use\, and reasoning to autonomously perform multi-step geosp
 atial analysis from natural-language requests. Drawing on recent developme
 nts\, the session explores open-source architectures\, data sources\, and 
 AI frameworks that could support similar capabilities in research and publ
 ic-sector contexts. Through conceptual examples and interactive discussion
 \, participants will examine opportunities\, limitations\, and governance 
 considerations for trustworthy\, explainable agentic EO workflows.\nBenefi
 ts for the attendees\, what will they learn\nParticipants will gain a prac
 tical understanding of how emerging agentic AI approaches can be applied t
 o Earth observation (EO) workflows. By attending the workshop\, they will:
 \n\n\nUnderstand the key characteristics of agentic AI systems\, including
  planning\, tool use\, memory\, and multi-step reasoning in an EO context.
 \n\n\nLearn how natural-language interfaces can support complex geospatial
  analyses without requiring extensive remote-sensing expertise.\n\n\nBecom
 e familiar with open-source and open-data components that can be combined 
 to build agentic EO workflows\, including Sentinel and Landsat data\, EO A
 PIs\, open-weight models\, and agent frameworks.\n\n\nUnderstand the archi
 tecture of AI agents interacting with EO data\, from data discovery and pr
 ocessing to analysis and reporting.\n\n\nExplore practical EO use cases su
 ch as environmental monitoring\, disaster assessment\, urban expansion ana
 lysis\, and infrastructure detection.\n\n\nDevelop a critical perspective 
 on the opportunities\, limitations\, and risks of agentic EO systems\, inc
 luding issues of reliability\, explainability\, transparency\, evaluation\
 , and governance.\n\n\nExchange ideas with peers and identify potential ap
 plications of agentic EO approaches within their own research\, projects\,
  or organizational contexts.\n\n\nAttendees will leave with a clear concep
 tual framework for understanding and evaluating agentic Earth observation 
 systems\, as well as practical insight into how such capabilities could be
  prototyped using open technologies.\nLevel\nIntermediate. Participants sh
 ould have a basic familiarity with Earth observation\, geospatial data\, o
 r machine learning concepts. No advanced programming skills or prior exper
 ience with agentic AI systems are required.\nLanguage\nEnglish\nPrerequisi
 tes\nBasic familiarity with Earth observation\, geospatial data\, or machi
 ne learning concepts.\nTutor\nKarol Bot Gonçalves is a researcher at IT4I
 nnovations National Supercomputing Center with expertise spanning artifici
 al intelligence\, data analytics\, and sustainable digital technologies. H
 er research focuses on applying advanced AI and computational methods to r
 eal-world challenges\, including environmental monitoring\, Earth observat
 ion\, energy systems\, and decision support. Karol has an interdisciplinar
 y background combining engineering\, machine learning\, and high-performan
 ce computing\, and is actively involved in international research and inno
 vation projects.\n \n\n \nLUMI AI Factory is funded jointly by the EuroH
 PC Joint Undertaking\, through the European Union's Connecting Europe Faci
 lity and the Horizon 2020 research and innovation programme\, as well as F
 inland\, the Czech Republic\, Poland\, Estonia\, Norway\, and Denmark.\nTh
 is course was supported by the Ministry of Education\, Youth and Sports of
  the Czech Republic through the e-INFRA CZ (ID:90254).\n\nAll presentation
 s and educational materials of this course are provided under the Creative
  Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
  \n\nhttps://events.it4i.cz/event/405/
LOCATION:ZOOM (ONLINE)
URL:https://events.it4i.cz/event/405/
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