GEOID-Flood：用于洪水分割的大规模多模态基准数据集
GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation
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中文总结 AI 辅助
该研究推出GEOID-Flood大规模多模态洪水分割基准数据集，评估发现基础模型优势适度，光学-SAR融合微调效果最佳，该数据集训练的模型迁移性更优。
中文摘要 AI 辅助
地理空间基础模型旨在学习可跨区域和传感器迁移的表征，但在特定任务上评估它们需要大规模、高质量的多模态基准，以衡量这类模型从数据中提取价值的能力。关于洪水制图，现有数据集很少大规模结合双时相合成孔径雷达（SAR）和配准的光学图像，使得基础模型在该下游任务中的价值很大程度上未得到验证。我们推出GEOID-Flood，这是一个大规模多模态洪水分割基准，源自哥白尼应急管理服务的激活数据，覆盖65个国家的219个事件，时间跨度达十年。该数据集提供超过14000个图块，包含配准的事件前后哨兵1号（Sentinel-1）的GRD和RTC格式数据、事件前哨兵2号（Sentinel-2）合成数据以及数字高程模型（DEM），还包含手动验证的标签，将背景与永久水体、洪水水体区分开。利用该基准，我们针对单图像、多时相和多模态协议，将基础模型与传统编码器进行评估。我们报告了三个主要发现：基础模型提供持续但适度的优势；微调后的光学-SAR融合能最佳地解析 transient 洪水；在GEOID-Flood上训练的模型比在现有数据集上训练的模型能更好地迁移到未见过的事件。数据集和代码可在该https URL获取。
英文摘要
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.
发表机构
- Fondazione LINKS（LINKS基金会）
- Politecnico di Torino（都灵理工大学）
机构由 AI 辅助整理，请以论文原文为准。
