在农村地区使用GF-7数据提取高分辨率建筑高度信息的方法
Mingbo Liu1, Ping Wang1, Kailong Hu1
1National Disaster Reduction Center of China, Ministry of Emergency Management of the People's Republic of China, Beijing 100124, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
这项研究引入了一种使用Gaofen-7卫星数据在中国农村地区提取建筑高度的新方法. 该方法结合了摄影计和深度学习,实现了灾害管理和城市化研究的高精度.
科学领域:
- 地理空间科学 地理空间科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 准确的建筑高度数据对于灾害管理,损害评估,人口建模和城市化研究至关重要.
- 目前用于测量建筑高度的方法有限,特别是在中国的农村地区.
- 高芬-7卫星 (GF-7) 提供高分辨率图像,适合详细地图绘制.
研究的目的:
- 利用GF-7卫星图像开发和验证一种用于农村地区提取建筑高度的新方法.
- 根据LiDAR参考数据评估拟议方法的准确性.
- 促进卫星数据的应用,以便在服务不足的地区进行大规模的建筑高度调查.
主要方法:
- 采用了摄影测量和深度学习的综合方法.
- 一个深度学习模型,Delama (基于LaMa架构),已被开发用于数字表面模型 (DSM) 编辑.
- 建筑高度是使用在建筑足迹中规范数字表面模型 (nDSM) 的百分点值来确定的.
主要成果:
- 德拉马模型有效地编辑了DSM,保存了地形细节,并预测了建筑面具中的地形.
- 提取的建筑高度与ICESat-2 LiDAR数据具有很高的一致性.
- 验证指标包括R2为0.83,平均绝对误差 (MAE) 为1.81m,根平均平方误差 (RMSE) 为2.13m.
结论:
- 拟议的摄像度和深度学习方法准确地从农村地区的GF-7数据中提取建筑高度.
- 这种方法显著提高了卫星图像的实用性,用于大规模的建筑高度测绘,特别是在农村环境中.
- 这些发现支持通过更全面的地理空间数据改进灾害管理和城市规划.
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