# ZeroFlood: Flood Hazard Mapping from Single-Modality SAR Using Geo-Foundation Models

arXiv: https://arxiv.org/abs/2510.23364 · HTML: https://mlsanity.com/paper/2510.23364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [TerraMind: Large-Scale Generative Multimodality for Earth Observation](https://mlsanity.com/paper/2504.11171/) (2025)
- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [Assessment of a new GeoAI foundation model for flood inundation mapping](https://mlsanity.com/paper/2309.14500/) (2023)
- [A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data](https://mlsanity.com/paper/2006.09201/) (2020)
- [Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI](https://mlsanity.com/paper/2406.18295/) (2024)
- [Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping](https://mlsanity.com/paper/2309.13360/) (2023)

## Built on by (most cited first, 1 of 1)

- [Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference](https://mlsanity.com/paper/2608.15282/) (2026)

## Abstract (arXiv)

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 textitZeroFlood, 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.