GEOID-Flood：大规模多模态洪水分割基准数据集
原标题：GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation
AI 摘要
GEOID-Flood是一个大规模多模态洪水分割基准数据集，基于Copernicus应急管理服务数据，覆盖65个国家219起事件，包含超过14,000个瓦片，提供配准的灾前/灾后Sentinel-1（GRD和RTC格式）、灾前Sentinel-2合成影像和DEM，并带有手动验证的标签。研究评估了基础模型与传统编码器在单图像、多时序和多模态协议下的表现，发现基础模型提供了一致但适度的优势，光学-SAR融合微调最能解决瞬时洪水，且基于该数据集训练的模型对未见事件的迁移能力优于现有数据集。数据集和代码已在GitHub上以CC BY 4.0许可发布。
正文节选
GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation Abstract 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