在光学卫星图像中的全球洪水范围细分
Enrique Portalés-Julià1, Gonzalo Mateo-García2,3, Cormac Purcell3,4
1Image Processing Laboratory, University of Valencia, Valencia, Spain. enrique.portales@uv.es.
Scientific reports
|November 21, 2023
概括
一个新的机器学习框架从卫星图像提供了准确的,云意识的洪水映射. 这可以通过更快,更全面地检测洪水范围来改善应急响应和损害评估.
科学领域:
- 地球和环境科学 地球和环境科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 洪水是破坏性的自然灾害,预计其强度和频率将增加.
- 目前的卫星洪水测绘方法在云层覆盖,准确性和地理概括性方面扎.
- 准确,及时的洪水地图对于灾害响应和损害评估至关重要.
研究的目的:
- 开发一种机器学习框架,用于使用光学卫星图像进行操作洪水绘图.
- 解决当前方法的局限性,包括云覆盖和通用化.
- 为改善应急响应提供准确和快速的水分化口罩.
主要方法:
- 在一个扩展的WorldFloods数据集上训练了一个云意识的细分模型.
- 该框架使用来自Sentinel-2和Landsat 8/9.9的光学卫星数据.
- 用手动标记的洪水地图的全新全球数据集来评估性能.
主要成果:
- 该模型产生精确的水分面具,即使在半透明的云下.
- 与传统的光谱指数值方法相比,这种方法显示出更高的性能.
- 在巴基斯坦和澳大利亚最近发生的大洪水中,成功生成了洪水地图.
结论:
- 拟议的机器学习框架增强了操作洪水映射能力.
- 它为洪水检测提供了更高的准确性,速度和地理适用性.
- 高分辨率的洪水范围数据有助于评估对人口和基础设施的影响.
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