ZeroFlood: A Geospatial Foundation Model for Data-Efficient Flood Susceptibility Mapping

📝 Original Info

• Title: ZeroFlood: A Geospatial Foundation Model for Data-Efficient Flood Susceptibility Mapping
• ArXiv ID: 2510.23364
• Date: 2025-10-27
• Authors: ** 제공되지 않음 (논문에 저자 정보가 포함되지 않았습니다.) **

📝 Abstract

Flood susceptibility mapping (FSM) is vital for disaster prevention but remains challenging in data-scarce regions where hydrodynamic models require dense geophysical inputs. This work introduces ZeroFlood, a geospatial foundation model framework for data-efficient FSM. The approach fine-tunes Geospatial Foundation Models (GFMs) with Thinking-in-Modality (TiM) reasoning, enabling flood prediction from basic Earth observation data such as Sentinel-1 or Sentinel-2 imagery. Using paired EO and simulated flood maps from data-rich regions, ZeroFlood bridges data availability gaps through cross-modal representation learning. Experiments with TerraMind and Prithvi GFMs show that TiM enhances model robustness, with the TerraMind-Large configuration achieving an F1 score of 67.21. The results demonstrate the feasibility of foundation-model-based FSM as a scalable and data-efficient solution for flood risk management.

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