SenForFlood

Getting accurate information about the extent and severity of floods is essential for planning proper humanitarian emergency assistance. SentForFlood is a global dataset for training deep learning models for mapping flood extent, including images before and during flood from Sentinel-1 and -2, terrain elevation and slope, Land Use and Land Cover (LULC), and flood masks. The samples included in each flood event were selected by analysts considering quality of flood mask and completeness of the available satellite imagery. The dataset incorporated data from over 350 distinct flood events, encompassing all continents except Antarctica. The dataset was tested by training a convolutional neural network for detecting floods without permanent water bodies and the results are discussed. We expect that the dataset will facilitate the development of robust, transferable models for automatic flood mapping, thereby contributing to the humanitarian emergency repose in crisis situations. More information about the dataset, including useful usage scripts and examples, can be found in the project's <a href="https://github.com/menimato/SenForFlood">Github page</a>.

获取洪水影响范围与严重程度的准确信息，对于制定合理的人道主义应急援助计划至关重要。SentForFlood是一款用于训练深度学习模型以开展洪水范围制图的全球数据集，其数据包含哨兵-1（Sentinel-1）与哨兵-2（Sentinel-2）拍摄的洪水前后影像、地形高程与坡度数据、土地利用与土地覆盖（Land Use and Land Cover，简称LULC）数据，以及洪水掩膜（flood masks）。针对每一次洪水事件，分析师会综合考量洪水掩膜质量与可用卫星影像的完整性，筛选符合标准的样本纳入该数据集。该数据集整合了350余起独立洪水事件的观测数据，覆盖除南极洲以外的所有大洲。研究团队通过训练卷积神经网络（Convolutional Neural Network）检测非永久性水体中的洪水，对该数据集进行了性能测试，并对测试结果展开了讨论。我们期望该数据集能够推动鲁棒性强、可迁移性优异的自动洪水制图模型的研发，从而在危机情境下助力人道主义应急响应。有关该数据集的更多信息，包括实用的使用脚本与示例，可访问本项目的<a href="https://github.com/menimato/SenForFlood">Github页面</a>获取。

搜集汇总

背景与挑战

背景概述

SenForFlood是一个全球性的洪水测绘数据集，包含多源卫星影像和地形数据，用于训练深度学习模型。数据集覆盖全球350多个洪水事件，旨在支持自动洪水测绘模型的开发，以提升人道主义应急响应能力。