GEOID-Flood：一個用於洪水分割的大規模多模態基準數據集

摘要

地理空間基礎模型的目標是學習能跨區域與感測器遷移的表徵，然而，要在特定任務上評估它們，需要大規模、高品質、多模態的基準，以衡量此類模型如何從數據中提取價值。就洪水測繪而言，現有數據集很少在大規模尺度上結合雙時相SAR與共配準的光學影像，使得基礎模型在此下游任務中的價值大多未被測試。我們提出GEOID-Flood，一個大規模多模態洪水分割基準，源自哥白尼緊急管理服務（Copernicus Emergency Management Service）的啟動任務，涵蓋十年間65個國家的219起事件。該數據集提供超過14,000個圖塊，包含共配準的事件前後Sentinel-1影像（以GRD與RTC格式）、事件前Sentinel-2合成影像，以及DEM，並具備手動驗證的標籤，用以區分背景、永久水體與洪水。利用此基準，我們在單一影像、多時相與多模態協議下，比較基礎模型與傳統編碼器的表現。我們報告三項主要發現：基礎模型提供一致但溫和的優勢；光學與SAR融合搭配微調最能解決暫時性洪水；以及訓練於GEOID-Flood的模型，其遷移至未見事件的能力優於訓練於現有數據集的模型。數據集與代碼可在 https://github.com/links-ads/geoid-flood 取得。

English

Geospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal benchmarks that measure how well such models extract value from data. Concerning flood mapping, existing datasets rarely combine bi-temporal SAR and co-registered optical imagery at scale, leaving the value of foundation models for this downstream task largely untested. We introduce GEOID-Flood, a large-scale multi-modal flood segmentation benchmark, derived from Copernicus Emergency Management Service activations, spanning 219 events across 65 countries over ten years. The dataset provides more than 14,000 tiles with co-registered pre- and post-event Sentinel-1, in GRD and RTC format, pre-event Sentinel-2 composite, and DEM, including manually validated labels that separate background from permanent water and flooded water. Using this benchmark, we evaluate foundation models against conventional encoders across single-image, multi-temporal, and multi-modal protocols. We report three main findings: foundation models offer a consistent but modest advantage; optical-SAR fusion with finetuning best resolves transient flooding; and models trained on GEOID-Flood transfer to unseen events better than those trained on existing datasets. Dataset and code available at https://github.com/links-ads/geoid-flood.