ArticleNature communications2026
Deep learning completes US flood hazard maps revealing millions exposed to previously unrecognized risk.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Deep learning completes US flood hazard maps revealing millions exposed to previously unrecognized risk.Nature communications · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
Flood maps are essential for disaster preparedness, insurance and climate adaptation, yet US official maps cover only one-third of river channels. Here we present a deep-learning framework that learns from existing records to produce a spatially complete 30-m flood hazard map for the contiguous US, filling unmapped regions and updating legacy extents. We estimate that the 2023 national database omits 11 million people and 4.1 million buildings in flood zones, adding 69% and 81% beyond the official baseline respectively. Crucially, our socioeconomic analysis of 917 cities reveals that entirely unmapped areas disproportionately harbor highly vulnerable demographics, particularly the elderly and children, exposing them to unrecognized risks and compounding existing inequalities. By solving the trilemma between accuracy, scale, and cost in national scale flood mapping, our open-source method broadens public access to risk information, paving the way for targeted and equitable climate adaptation.
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Registered trials
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