Articles | Volume 26, issue 8
https://doi.org/10.5194/nhess-26-4101-2026
https://doi.org/10.5194/nhess-26-4101-2026
Research article
 | 
27 Aug 2026
Research article |  | 27 Aug 2026

Rapid landslide mapping during the 2023 Emilia-Romagna disaster: assessing automated approaches with limited training data

Nicola Dal Seno, Giuseppe Ciccarese, Davide Evangelista, Elena Loli Piccolomini, Alessandro Corsini, and Matteo Berti

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Short summary
The extreme rainfall in Emilia-Romagna in May 2023 caused over 80 000 landslides. Mapping them manually was slow and demanding, so we tested artificial intelligence to speed up this process. We applied two models in different areas using satellite and aerial images. Both produced useful maps that can guide emergency teams, although performance was lower in complex terrains. Our results show that AI can support faster disaster response in future events.
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