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

• URL: http://arxiv.org/abs/2510.23364v1
• Date: Mon, 27 Oct 2025 14:14:09 GMT
• Title: ZeroFlood: A Geospatial Foundation Model for Data-Efficient Flood Susceptibility Mapping
• Authors: Hyeongkyun Kim, Orestis Oikonomou,
• Abstract summary: Flood susceptibility mapping (FSM) is vital for disaster prevention but remains challenging in data-scarce regions.<n>This work introduces ZeroFlood, a framework for data-efficient FSM.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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