融合Landsat和全球高程数据绘制城市三维结构图
Mapping Three-dimensional Urban Structure by Fusing Landsat and Global Elevation Data
摘要：
为了应对全球城市化的挑战，地球观测信息是非常需要的。全球三维（3D）城市结构数据的缺乏一直是重要的城市应用，如人口绘图，灾害脆弱性评估和气候变化适应的主要限制因素。由于数据的有限性，遥感数据主要用于描述城市尺度的三维城市结构。在这项研究中，我们提出了一种方法来映射三维城市结构，使用免费提供的陆地卫星和全球高程数据。在基于对象的机器学习方法的基础上，利用陆地卫星和高程数据的协同作用，以30米的分辨率估计建筑物的高度和体积。该方法已在英格兰全国进行了测试，得出建筑物高度的均方根误差（RMSE）为1.61 m，建筑物体积的RMSE为1，142 m \textsuperscript{3}。在这项研究中的结果代表了第一次尝试使用开放数据的城市结构表征在国家范围内。我们的研究结果表明，这些数据的大规模城市研究的效用和潜在的全球三维城市结构数据产品，使用基于融合的方法。
英文摘要：
To meet the challenges of global urbanization, earth observation information is greatly needed. The lack of global three-dimensional (3D) urban structure data has been a major limiting factor in important urban applications such as population mapping, disaster vulnerability assessment, and climate change adaptation. Due to limited data availability, remote sensing data haven been mainly used to characterize 3D urban structure at the city scale. In this study, we propose a method to map 3D urban structure using freely available Landsat and global elevation data. Building on an object-based machine learning approach, the synergy of Landsat and elevation data were used to estimate building height and volume at 30 m resolution. This method has been tested for the entire country of England and yielded a root-mean-square error (RMSE) of 1.61 m for building height and an RMSE of 1,142 m\textsuperscript{3} for building volume. The results in this study represent the first attempt to use open data for urban structure characterization at the country scale. Our results demonstrated the utility of these data for large-scale urban studies and the potential of generating global 3D urban structure data products using a fusion-based approach.
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