用单模态遥感数据，零依赖物理模型实现高精度洪水风险制图。
ZeroFlood: Flood Hazard Mapping from Single-Modality SAR Using Geo-Foundation Models
- 基于地理基础模型，仅用SAR影像预测洪水风险。
- 最佳模型F1达88.36%，比监督学习提升超3个百分点。
- 适合缺乏气象水文数据的地区灾害预警应用。
洪水风险制图对防灾至关重要，但在数据匮乏地区仍具挑战性，传统水动力模型需大量地理物理输入。本文提出 extit{ZeroFlood}框架，利用地理基础模型（GeoFMs）仅通过单模态地球观测（EO）数据——尤其是SAR影像——预测洪水风险图。我们构建了覆盖欧洲大陆的标注数据集，将EO数据与洪水风险模拟结果配对。在此基础上，评估多种近期GeoFMs在洪水风险分割任务上的表现。实验表明，表现最优的TerraMind模型获得88.36%的F1分数，较监督学习基线提升超过3个百分点。进一步引入思维-模态机制（TiM）可进一步提升性能。结果证明，地理基础模型在有限观测输入下具备数据驱动洪水风险制图的巨大潜力。数据集与代码已公开于https://github.com/khyeongkyun/zeroflood。
原文摘要 · Abstract (English)
Flood hazard mapping is essential for disaster prevention but remains challenging in data-scarce regions, where traditional hydrodynamic models require extensive geophysical inputs. This paper introduces \textit{ZeroFlood}, a framework that leverages Geo-Foundation Models (GeoFMs) to predict flood hazard maps using single-modality Earth Observation (EO) data, specifically SAR imagery. We construct a dataset that pairs EO data with flood hazard simulations across the European continent. Using this dataset, we evaluate several recent GeoFMs for the flood hazard segmentation task. Experimental results show that the best-performing model, TerraMind, achieves an F1-score of 88.36\%, outperforming supervised learning baselines by more than 3 percentage points. We shows the performance can be further improved by applying the Thinking-in-Modality (TiM) mechanism. These results demonstrate the potential of Geo-Foundation Models for data-driven flood hazard mapping using limited observational inputs. The dataset and experiment code are publicly available at https://github.com/khyeongkyun/zeroflood.
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