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洪水2深度:城市地区洪水深度估计的多源数据集
Jeffrey Blay1, Yared Gebregziabher2, Manoj K Jha3
1Department of Built Environment, North Carolina A&T State University, 1601 E Market St., Greensboro, NC 27401, USA.
Data in brief
|January 6, 2026
概括
一个新的数据集,Inundation2Depth,为城市地区提供了关键的洪水深度数据,使得更好的风险评估和深度学习模型能够用于洪水管理. 这个资源支持 GeoAI 关于洪水严重性的研究.
科学领域:
- 地理空间的人工智能 (GeoAI)
- 遥感 遥感 遥感 遥感
- 水文学的水文学
背景情况:
- 城市洪水带来重大风险,但运营地图通常缺乏关键的洪水深度数据.
- 开发精确的洪水深度模型受到大型,地理引用和标记良好的数据集稀缺的阻碍.
- 深度学习提供先进的解决方案,但需要大量高质量的培训数据.
研究的目的:
- 引入Inundation2Depth数据集,这是一个用于估计洪水深度的新型资源.
- 为了促进城市洪水严重性评估的GeoAI模型的开发和评估.
- 降低数据障碍,促进与洪水有关的GeoAI研究的可比性.
主要方法:
- 由空中图像和基于LiDAR (光检测和测距) 的数字地形模型 (DTM) 获得的配对洪水范围和深度标签.
- 从北卡罗来纳州和南卡罗来纳州的12个受洪水影响地区 (2016-2018) 收集的数据涵盖了各种环境条件.
- 多传感器遥感数据 (光学和LiDAR) 为空间一致性进行预处理,并作为光提供,用于机器学习集成.
主要成果:
- 洪水2深度数据集包括5925个重叠的,遍及24,649.88英.
- 数据包括原始和规范化版本,适合直接集成到机器/深度学习管道中.
- 用HEC-RAS雨在电网工具使用水力动力学建模进行的数据集验证.
结论:
- 洪水2深度数据集解决了对地理参考洪水深度数据的关键需求.
- 它的标准化格式和空间多样性增强了其用于洪水检测,细分和损害评估模型的价值.
- 预计这笔资金将促进GeoAI对城市洪水严重性的研究,并改进洪水风险管理策略.
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