AI & Environment Resource HubAngelica Beare Guiang (EXT) · 2024-09-01
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Angelica Beare Guiang (EXT). 2024-09-01. How Can Geospatial Data and AI Be Leveraged to Support Climate Change Adaptation in Cities and the Built Environment?. https://pages.awscloud.com/rs/112-TZM-766/images/24-EMEA-en-US-idc-other-ardm-geospatial-data-climate-change-adapt-reprint.pdf?version=1&trk=5b1c660c-08e7-41d8-b623-ad787564ab46&sc_channel=el&refid=c170b19d-8ef0-478c-9907-65d9673c48c5 (AI & Environment Resource Hub; record report-033; collection snapshot 2026-09-15).
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Source sheet: Policy Document · Row 37 · Original ID: report-033.
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Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Climate and greenhouse gases; Weather, hazards, and adaptation
This study showcases a machine learning hackathon in France where a CNN-based model accurately predicted flood risk evolution using geospatial and climate data, even without streamflow input, emphasizing trustworthy AI in data-scarce regions.
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Weather, hazards, and adaptation; Built environment and infrastructure
Paul Eremenko from P-1 AI discusses a lot of topics with hot takes including data centers killing hydrogen and the Paris Climate Agreement being dead with the conversation shifting to climate change mitigation and resilience.
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Weather, hazards, and adaptation; Built environment and infrastructure
AI is being deployed across both generative and predictive applications, but while generative AI demands enormous energy and data center infrastructure, predictive AI offers more efficient, targeted tools for climate solutions, such as emissions tracking, renewable grid forecasting, and extreme weather nowcasting, highlighting a crucial trade-off between innovation and sustainability.
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Weather, hazards, and adaptation; Built environment and infrastructure
Machine learning enhances real-time and future climate monitoring in historic buildings, aiding preservation efforts amid climate change.