[2510.23364] ZeroFlood: Flood Hazard Mapping from Single-Modality SAR Using Geo-Foundation Models

Computer Science > Machine Learning arXiv:2510.23364 (cs) [Submitted on 27 Oct 2025 (v1), last revised 30 Mar 2026 (this version, v2)] Title:ZeroFlood: Flood Hazard Mapping from Single-Modality SAR Using Geo-Foundation Models Authors:Hyeongkyun Kim, Orestis Oikonomou View a PDF of the paper titled ZeroFlood: Flood Hazard Mapping from Single-Modality SAR Using Geo-Foundation Models, by Hyeongkyun Kim and Orestis Oikonomou View PDF HTML (experimental) Abstract: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 c...