Rapid Landslide Mapping During the 2023 Emilia-Romagna Disaster: Assessing Automated Approaches with Limited Training Data

Abstract. The catastrophic rainfall events of May 2023 in the Emilia-Romagna region, Italy triggered more than 80.000 landslides (https://doi.org/10.5281/zenodo.13742643, Pizziolo et al., 2024) and placed extraordinary demands on emergency response systems. One of the most critical tasks during the emergency was mapping the landslides caused by the event, a process carried out manually that required substantial time and effort (Berti et al., 2024).This study explores the potential of automated landslide mapping to support rapid disaster response, evaluating two deep learning models, U-Net and SegFormer, under realistic emergency constraints, including minimal training data. The models were trained exclusively in one affected municipality area (Casola Valsenio, 84 km²) and tested on three additional municipalities (Predappio, 91 km²; Modigliana, 101 km²; Brisighella, 194 km²) with varying geological settings. To reflect a range of operational scenarios, we tested seven combinations of input layers, progressively increasing in complexity from post-event Sentinel-2 imagery alone to full integration of high-resolution aerial imagery, NDVI change maps, and slope data.

Results show that both models achieved comparable segmentation performance, with SegFormer displaying greater robustness to variations in input layers and geological conditions, while U-Net was more sensitive but occasionally more accurate with rich inputs. Both models successfully identified landslides, but encountered difficulties in shadowed zones, cultivated fields, and geologically distinct terrains. A key limitation emerged in Brisighella FAA, a sector dominated by Blue Clay formations, where poor generalization was traced to the lack of lithological diversity in the training data, highlighting the need for geologically balanced training datasets.

Despite these challenges, the study confirms the operational value of automated mapping as a first-pass tool. Both models delivered accurate baseline maps that could support manual validation and prioritization in time-critical scenarios. The findings suggest that AI-based mapping could become a key component of emergency management protocols. While manual revision remains essential, these tools offer a scalable, time-efficient solution to the increasing demand for rapid and spatially detailed hazard information.