Abstract. The present state of the art technologies for flood mapping are typically tested on small geographical regions due to limitation of resources, which hinders the implementation of real-time flood management activities. We proposed a unified framework (GEE4FLOOD) for rapid flood mapping in Google Earth Engine (GEE) cloud platform. With the unexpected spells of extreme rainfall in August 2018, many parts of Kerala state in India experienced a major disastrous flood. Therefore, we tested the GEE4FLOOD processing chain on August 2018 Kerala flood event. GEE4FLOOD utilizes multitemporal Sentinel-1 synthetic aperture radar images available in GEE catalog and an automatic Otsu’s thresholding algorithm for flood mapping. It also utilizes other remote sensing datasets available in GEE catalog for permanent water body mask creation and result validation. The ground truth data collected during the Kerala flood indicates promising accuracy with 82% overall accuracy and 78.5% accuracy for flood class alone. In addition, the entire process from data fetching to flood map generation at a varying geographical extent (district to state level) took ∼2 to 4 min.

中文翻译：

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GEE4FLOOD：使用 Google Earth Engine 云平台使用时间 Sentinel-1 SAR 图像快速绘制洪水区域

摘要。由于资源有限，当前最先进的洪水测绘技术通常在较小的地理区域进行测试，这阻碍了实时洪水管理活动的实施。我们提出了一个统一的框架（GEE4FLOOD）用于谷歌地球引擎（GEE）云平台中的快速洪水测绘。随着 2018 年 8 月出乎意料的极端降雨，印度喀拉拉邦的许多地区发生了严重的灾难性洪水。因此，我们在 2018 年 8 月喀拉拉邦洪水事件中测试了 GEE4FLOOD 处理链。GEE4FLOOD 利用 GEE 目录中提供的多时相 Sentinel-1 合成孔径雷达图像和自动 Otsu 阈值算法进行洪水映射。它还利用 GEE 目录中可用的其他遥感数据集进行永久性水体掩膜创建和结果验证。在喀拉拉邦洪水期间收集的地面实况数据表明准确度有希望，整体准确度为 82%，仅洪水等级准确度为 78.5%。此外，从数据获取到不同地理范围（区到州级）的洪水地图生成的整个过程大约需要 2 到 4 分钟。