{"id":19624,"date":"2026-06-01T08:47:17","date_gmt":"2026-06-01T06:47:17","guid":{"rendered":"https:\/\/it-u.at\/?page_id=19624"},"modified":"2026-06-03T12:54:27","modified_gmt":"2026-06-03T10:54:27","slug":"earth-observation-and-geoai-for-climate-resilient-urban-systems","status":"publish","type":"page","link":"https:\/\/it-u.at\/en\/earth-observation-and-geoai-for-climate-resilient-urban-systems\/","title":{"rendered":"Earth Observation and GeoAI for Climate-Resilient Urban Systems"},"content":{"rendered":"
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About the school<\/strong><\/h2>\n\n\n\n

Earth Observation and GeoAI for Climate-Resilient Urban Systems is an advanced school designed to equip the next generation of researchers, practitioners, and decision-makers with state-of-the-art Earth Observation (EO), geospatial artificial intelligence (GeoAI), and data fusion methodologies to analyze, quantify, and mitigate climate-driven risks and effects in urban systems.<\/p>\n\n\n\n

Within this framework, climate-resilient Urban Systems are understood as complex socio-technical systems composed of buildings, critical infrastructure, transportation networks, public spaces, and human communities. The program emphasizes how Earth Observation (EO), multi-sensor data fusion (including optical, SAR, and microwave EO), geospatial artificial intelligence (GeoAI), and participatory sensing can be integrated to strengthen urban resilience under conditions of climate change and multi-hazard exposure.<\/p>\n\n\n\n

The school combines expert lectures, structured hands-on laboratories, and a Digital City Studio living-lab experience to advanced EO analytics, GeoAI model development, and operational decision-making contexts. Participants will work with satellite imagery (Sentinel, Landsat, SAR, microwave missions), airborne data, in-situ measurements, geo-social media data and human-generated datasets using open-source tools and AI-driven methodologies to develop scalable scalable, explainable, and transferable workflows for urban risk intelligence, infrastructure vulnerability assessment, and climate perception analysis.<\/p>\n\n\n\n

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:registration <\/strong> <\/a><\/div>\n\n\n\n
:school program<\/a><\/div>\n<\/div>\n\n<\/div>\n\n \n <\/div> \n \n<\/div>\n\n\n
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Host: Interdisciplinary Transformation University (IT:U), Linz, Austria <\/div><\/div>
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14\u201316 October 2026 <\/div><\/div>
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IT:U Research Campus, Freist\u00e4dter Str. 400, OG1 4040 Linz, Austria (Gold Meeting room)<\/div><\/a>
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10 Speakers<\/div><\/div>
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Participants: ~30 (young professionals + graduate students)<\/div><\/div>
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English <\/div><\/div>
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Format: In-person, 3-day intensive program<\/div><\/div>
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All participants will receive an official Certificate of Participation recognizing attendance and engagement, equivalent to 1 ECTS.<\/div><\/div>
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Organizing body: IEEE GRSS (with Austrian, Italian, and Polish chapters), IT:U and PLUS<\/div><\/div><\/div> <\/div>\n\n\n\n\n
co-organizers<\/h5>\n\n\n\n
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Learning Outcomes<\/strong> <\/h2>\n\n\n\n

By the end of the school, participants will be able to:<\/p>\n\n\n\n

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  1. Analyze and quantify climate-related hazards affecting built environments using multi-source Earth Observation datasets (including optical, SAR, and climate data).<\/li>\n\n\n\n
  2. Design and implement multimodal workflows integrating satellite, in-situ, and human-generated data.<\/li>\n\n\n\n
  3. Understand the value of GeoAI-based methodologies for analysing geo-social media data to deconstruct climate perception, narratives, and socio-environmental interactions in urban contexts.<\/li>\n\n\n\n
  4. Develop AI-based classification models for disaster damage and infrastructure vulnerability assessment.<\/li>\n\n\n\n
  5. Adapt remote sensing methodologies to low-resource and humanitarian contexts using open-access tools.<\/li>\n<\/ol>\n\n<\/div>\n\n \n <\/div> \n \n<\/div>\n\n\n

    School Program<\/strong><\/strong><\/h2>\n\n\n
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    Day 1 : 14th October 2026 <\/th>\n<\/tr>\n<\/thead>\n
    Time <\/strong><\/td> Topic <\/strong> <\/td>\n<\/tr>\n
    8:30 \u2013 9:00 am <\/td>Registration <\/td><\/td>\n<\/tr>\n
    9:00 - 9:15 am <\/td>Opening remark<\/td><\/td>\n<\/tr>\n
    9:15 \u2013 9:45 am <\/td>Welcome remark<\/td><\/td>\n<\/tr>\n
    9:45 \u2013 10:30 am <\/td>School Photo Session + Coffee Break<\/td><\/td>\n<\/tr>\n
    10:30 - 12:00 pm <\/td>Keynote <\/td><\/td>\n<\/tr>\n
    12:00 - 1:00 pm <\/td>Lunch <\/td><\/td>\n<\/tr>\n
    1:00 \u2013 3:00 pm <\/td>Urban Heat Island Analysis using Multi-Sensor Earth Observation (Optical and Thermal) <\/td><\/td>\n<\/tr>\n
    3:00 - 3:30 pm <\/td>Coffee Break <\/td><\/td>\n<\/tr>\n
    3:00 - 3:30 pm <\/td>Coffee Break <\/td><\/td>\n<\/tr>\n
    3:30 - 5:00 pm <\/td>Measuring Drought in a Warming World with Climate Data <\/td><\/td>\n<\/tr>\n
    6:00 pm - 10:00 pm <\/td>Gala dinner <\/td><\/td>\n<\/tr>\n
    Day 2: 15th October 2026<\/strong><\/td>\n<\/tr>\n
    9:00 - 10:30 am <\/td>Deep Learning-based Disaster-Induced Rapid Impact Mapping in Complex Urban settings <\/td><\/td>\n<\/tr>\n
    10:30 - 11:00 am <\/td>Coffee Break <\/td><\/td>\n<\/tr>\n
    11:00 - 12:00 pm <\/td>AI-driven Urban Flood mapping <\/td><\/td>\n<\/tr>\n
    12:00 - 1:00 pm <\/td>Lunch <\/td><\/td>\n<\/tr>\n
    1:00 - 3:00 pm <\/td>Multi-Hazard Mapping using SAR for disaster risk response <\/td><\/td>\n<\/tr>\n
    3:00 - 3:30 pm <\/td>Coffee Break <\/td><\/td>\n<\/tr>\n
    3:30 - 5:00 pm <\/td>Multi-Hazard Mapping (Hands-on \/ Workflow Integration) <\/td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n<\/div>\n\n \n <\/div> \n \n<\/div>\n\n\n
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    Day 3 : 16th October 2026 <\/th>\n<\/tr>\n<\/thead>\n
    9:00 - 10:30 am <\/td>Microwave Earth Observation for Land Surface Modelling and Urban Environmental Monitoring <\/td><\/td>\n<\/tr>\n
    10:30 - 11:00 am <\/td>Coffee Break <\/td><\/td>\n<\/tr>\n
    11:00 - 12:30 pm <\/td>Mapping floods in urban areas using SAR and optical satellites - How to\u202f
    \ncope with data gaps?\u202f <\/td>    		<\/td>\n<\/tr>\n
    12:30 - 1:30 pm <\/td>Lunch <\/td><\/td>\n<\/tr>\n
    1:30 - 3:00 pm <\/td>Geospatial EO Data cubes for climate-driven impact studies <\/td><\/td>\n<\/tr>\n
    3:00 - 3:30 pm <\/td>Coffee Break <\/td><\/td>\n<\/tr>\n
    3:30 - 5:00 pm <\/td>Participatory Sensing and Citizen-Generated Data for Climate Resilience <\/td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n