构建首个近海岸洪水淹没预测数据集与评测基准。
Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting
- 构建包含多源数据的近海岸洪水淹没数据集
- 提出两类评测任务，覆盖一般区域与沿海重点区域
- 开源数据代码模型，支持算法持续优化
洪水是常见且破坏力强的自然灾害，对社会经济造成巨大损失。尽管天气预报和星载洪水监测技术取得进展，但二者尚未有效结合，且缺乏用于直接预测洪水范围的数据集与评测基准。为此，本文构建了一个新型数据集，支持及时预测洪水淹没范围，并设计了两项基准评测任务：一是通用洪水淹没预测，二是聚焦沿海区域的预测。该数据集与评测平台为洪水预报研究提供了全面评估环境，推动未来解决方案发展。数据、代码与模型已通过CC0许可在https://github.com/Multihuntr/GFF公开共享。
原文摘要 · Abstract (English)
Floods are among the most common and devastating natural hazards, imposing immense costs on our society and economy due to their disastrous consequences. Recent progress in weather prediction and spaceborne flood mapping demonstrated the feasibility of anticipating extreme events and reliably detecting their catastrophic effects afterwards. However, these efforts are rarely linked to one another and there is a critical lack of datasets and benchmarks to enable the direct forecasting of flood extent. To resolve this issue, we curate a novel dataset enabling a timely prediction of flood extent. Furthermore, we provide a representative evaluation of state-of-the-art methods, structured into two benchmark tracks for forecasting flood inundation maps i) in general and ii) focused on coastal regions. Altogether, our dataset and benchmark provide a comprehensive platform for evaluating flood forecasts, enabling future solutions for this critical challenge. Data, code & models are shared at https://github.com/Multihuntr/GFF under a CC0 license.
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