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.
翻译：洪水易发性制图（FSM）对灾害预防至关重要，但在数据稀缺地区仍面临挑战，因为水动力模型需要密集的地球物理输入。本研究提出了零洪水（ZeroFlood），一种用于数据高效FSM的地理空间基础模型框架。该方法通过模态思维（TiM）推理对地理空间基础模型（GFMs）进行微调，使其能够基于哨兵一号或哨兵二号等基本地球观测数据进行洪水预测。利用数据丰富地区的配对地球观测数据与模拟洪水图，零洪水通过跨模态表征学习弥合数据可用性差距。基于TerraMind和Prithvi GFMs的实验表明，TiM增强了模型鲁棒性，其中TerraMind-Large配置取得了67.21的F1分数。研究结果证明了基于基础模型的FSM作为可扩展且数据高效的洪水风险管理解决方案的可行性。
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