基于深度学习的空间显式准确性评估,美国的精细分辨率建成土地数据
Johannes H Uhl1,2, Stefan Leyk1,3
1University of Colorado Boulder, Institute of Behavioral Science, 483 UCB, Boulder, CO 80309, USA.
概括
这项研究验证了全球人类定居层 (GHS-BUILT-S2) 产品,显示0.5概率值准确地识别了建筑区. 这种使用Sentinel-2数据的机器学习方法显著提高了人类定居点绘图的准确性.
科学领域:
- 地理信息学和遥感技术
- 机器学习应用 机器学习应用
- 城市研究 城市研究
背景情况:
- 来自遥感的地理空间数据集往往产生难以用于实际应用的概率输出.
- 全球人类定居层 (GHS-BUILT-S2) 提供了10米分辨率的建地概率,但通常需要二进制地图或子像素分数.
研究的目的:
- 评估GHS-BUILT-S2构建概率和参考分数之间的关系.
- 为了确定一个最佳的二元化值,以创建可解释的二元化建成的土地表面.
- 为了对衍生出来的二进制表面进行严格的准确性评估.
主要方法:
- 将GHS-BUILT-S2概率与美国的参考表面分数进行比较.
- 应用协议最大化方法来确定二元化值.
- 使用焦点精度回忆签名图表进行空间显式,规模敏感的准确性评估.
主要成果:
- 对于GHS-BUILT-S2概率的0.5值最大限度地与参考数据达成一致.
- 高精度 (平均F-1评分为~0.8) 获得了衍生二进制建成区域.
- 在农村-城市梯度上观察到一致的准确性.
结论:
- 该研究证实了将GHS-BUILT-S2概率转换为可靠的二进制建成土地数据的0.5值的实用性.
- 使用Sentinel-2数据和深度学习的人类定居模式显示,与之前基于Landsat的版本相比,准确性有了显著的改进.
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