A global product of fine-scale urban building height based on spaceborne lidar

Xiao Ma, Guang Zheng, Chi Xu, L. Monika Moskal, Peng Gong +9 more
10/22/2023
cs.LGeess.IV

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Abstract

Characterizing urban environments with broad coverages and high precision is more important than ever for achieving the UN's Sustainable Development Goals (SDGs) as half of the world's populations are living in cities. Urban building height as a fundamental 3D urban structural feature has far-reaching applications. However, so far, producing readily available datasets of recent urban building heights with fine spatial resolutions and global coverages remains a challenging task. Here, we provide an up-to-date global product of urban building heights based on a fine grid size of 150 m around 2020 by combining the spaceborne lidar instrument of GEDI and multi-sourced data including remotely sensed images (i.e., Landsat-8, Sentinel-2, and Sentinel-1) and topographic data. Our results revealed that the estimated method of building height samples based on the GEDI data was effective with 0.78 of Pearson's r and 3.67 m of RMSE in comparison to the reference data. The mapping product also demonstrated good performance as indicated by its strong correlation with the reference data (i.e., Pearson's r = 0.71, RMSE = 4.60 m). Compared with the currently existing products, our global urban building height map holds the ability to provide a higher spatial resolution (i.e., 150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This work will boost future urban studies across many fields including climate, environmental, ecological, and social sciences.

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Cite this paper

@article{ma2023global,
  title  = {A global product of fine-scale urban building height based on spaceborne lidar},
  author = {Xiao Ma and Guang Zheng and Chi Xu and L. Monika Moskal and Peng Gong and Qinghua Guo and Huabing Huang and Xuecao Li and Yong Pang and Cheng Wang and Huan Xie and Bailang Yu and Bo Zhao and Yuyu Zhou},
  year   = {2023},
  eprint = {2310.14355},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.14355v1}
}

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