Articles | Volume 26, issue 8
https://doi.org/10.5194/nhess-26-4101-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-4101-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Aug 2026
Research article |
| 27 Aug 2026
| 27 Aug 2026
Rapid landslide mapping during the 2023 Emilia-Romagna disaster: assessing automated approaches with limited training data
Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, via Zamboni 67, Bologna, Italy
Giuseppe Ciccarese
Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, via Zamboni 67, Bologna, Italy
Davide Evangelista
Department of Informatics: Science and Engineering (DISI), University of Bologna, Bologna, Italy
Elena Loli Piccolomini
Department of Informatics: Science and Engineering (DISI), University of Bologna, Bologna, Italy
Alessandro Corsini
Department of Chemical and Geological Sciences, University of Modena, Modena, Italy
Matteo Berti
Department of Biological, Geological, and Environmental Sciences (BiGeA), University of Bologna, via Zamboni 67, Bologna, Italy
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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.
The extreme rainfall in Emilia-Romagna in May 2023 caused over 80 000 landslides. Mapping them...
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