MAS-LAND: A multi-agent system for landslide detection and rapid response

Landslides require rapid, data-driven post-event assessment to support emergency response, yet most existing approaches focus on stand-alone landslide detection or mapping accuracy, with limited integration into operational decision-support workflows. This study presents MAS-LAND (Multi-Agent System for Landslide Detection and Rapid Response), a multi-agent, LLM-enhanced framework structured around three main stages: (1) post-event landslide detection, (2) infrastructure exposure assessment, and (3) automated reporting for civil protection operations. The framework is applied to the May 2023 Emilia–Romagna disaster and leverages very high-resolution post-event orthophotos and transformer-based semantic segmentation to identify newly occurred landslides. Among the evaluated models, SegFormer MIT-b2 achieved the strongest performance, detecting 603 of 806 landslides in the test dataset (Recall = 74.81%, F1 = 71.14%) and delivering robust multi-class segmentation results (macro F1 = 71.09%) despite pronounced class imbalance. In the second stage, infrastructure exposure is assessed through automated building footprint extraction using a pretrained RT-DETR model and road-network data derived from OpenStreetMap, enabling deterministic prioritization of intervention needs via a transparent, rule-based decision matrix. In the third stage, a reporting agent powered by Llama-3.3-70B, operated in deterministic mode, synthesizes analytical outputs into standardized operational summaries tailored for civil protection use. A key contribution of this work is the delivery of a fully integrated, post-event pipeline capable of transforming heterogeneous geospatial inputs into actionable intelligence within a few minutes of image acquisition and processing, as this ability is largely absent from previous landslide studies. The framework also produces structured, machine-readable outputs at every stage, forming a scalable knowledge base for systematic storage and reuse of event information. Overall, the results demonstrate the potential of multi-agent, hybrid AI–geospatial architectures to enhance situational awareness, improve reproducibility, and support timely decision-making of Civil Protection authorities during landslide emergencies.