A global product of 150-m urban building height based on spaceborne lidar
Urban building height, as a fundamental 3D urban structural feature, has far-reaching applications. However, creating readily available datasets of recent urban building heights with fine spatial resolutions and global coverage remains a challenging task. Here, we provide a 150-m global urban building heights dataset around 2020 by combining the spaceborne lidar (Global Ecosystem Dynamics Investigation, GEDI), multi-sourced data (Landsat-8, Sentinel-2, and Sentinel-1), and topographic data. The validation results revealed that the GEDI-estimated building height samples were effective compared to the reference data (Pearson's r = 0.81, RMSE = 3.58 m). The mapping product also demonstrated good performance, as indicated by its strong correlation with the reference data (Pearson's r = 0.71, RMSE = 4.73 m). Compared with the currently existing datasets, it holds the ability to provide a spatial resolution (150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This product will boost future urban studies across many fields, including environmental, ecological, and social sciences.
Authors
- Huan Xie (ORCID: https://orcid.org/0000-0003-3272-7848)
- Yong Pang (ORCID: https://orcid.org/0000-0002-9760-6580)
- Xuecao Li (ORCID: https://orcid.org/0000-0002-6942-0746)
- Guang Zheng (ORCID: https://orcid.org/0000-0003-4118-7804)
- Qinghua Guo (ORCID: https://orcid.org/0000-0002-1065-0838)
- Bo Zhao (ORCID: https://orcid.org/0000-0002-6406-3911)
- L. Monika Moskal (ORCID: https://orcid.org/0000-0003-1563-6506)
- Peng Gong (ORCID: https://orcid.org/0000-0003-1513-3765)
- Xinlian Liang (ORCID: https://orcid.org/0000-0002-1585-2340)
- Yuyu Zhou (ORCID: https://orcid.org/0000-0003-1765-6789)
- Cheng Wang (ORCID: https://orcid.org/0000-0002-3029-1166)
- Bailang Yu (ORCID: https://orcid.org/0000-0001-5628-0003)
- Chi Xu (ORCID: https://orcid.org/0000-0001-7389-5763)
- Huabing Huang (ORCID: https://orcid.org/0000-0001-6253-8437)
- Xiao Ma (ORCID: https://orcid.org/0009-0000-3912-5351)
Institutions
- Tongji University (CN)
- Chinese Academy of Sciences (CN)
- University of Washington (US)
- Peking University (CN)
- Wuhan University (CN)
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN)
- Institute of Forest Resource Information Techniques (CN)
- State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
- Chinese Academy of Forestry (CN)
- Aerospace Information Research Institute (CN)
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (CN)
- China Agricultural University (CN)
- East China Normal University (CN)
- Nanjing University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Scientific Data
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1038/s41597-024-04237-5
- Citations
- 13
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 5.97
Funders
- National Natural Science Foundation of China
- Nanjing University
- Fundamental Research Funds for the Central Universities