Dynamic Inversion Method of Calculating Large-Scale Urban Building Height Based on Cooperative Satellite Laser Altimetry and Multi-Source Optical Remote Sensing

Haobin Xia, Jianjun Wu (), Jiaqi Yao (), Nan Xu, Xiaoming Gao, Yubin Liang, Jianhua Yang, Jianhang Zhang, Liang Gao, Weiqi Jin and Bowen Ni
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Haobin Xia: Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China
Jianjun Wu: Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China
Jiaqi Yao: Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China
Nan Xu: Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
Xiaoming Gao: Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
Yubin Liang: Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China
Jianhua Yang: Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China
Jianhang Zhang: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Liang Gao: Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China
Weiqi Jin: Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China
Bowen Ni: Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China

Land, 2024, vol. 13, issue 8, 1-18

Abstract: Building height is a crucial indicator when studying urban environments and human activities, necessitating accurate, large-scale, and fine-resolution calculations. However, mainstream machine learning-based methods for inferring building heights face numerous challenges, including limited sample data and slow update frequencies. Alternatively, satellite laser altimetry technology offers a reliable means of calculating building heights with high precision. Here, we initially calculated building heights along satellite orbits based on building-rooftop contour vector datasets and ICESat-2 ATL03 photon data from 2019 to 2022. By integrating multi-source passive remote sensing observation data, we used the inferred building height results as reference data to train a random forest model, regressing building heights at a 10 m scale. Compared with ground-measured heights, building height samples constructed from ICESat-2 photon data outperformed methods that indirectly infer building heights using total building floor number. Moreover, the simulated building heights strongly correlated with actual observations at a single-city scale. Finally, using several years of inferred results, we analyzed building height changes in Tianjin from 2019 to 2022. Combined with the random forest model, the proposed model enables large-scale, high-precision inference of building heights with frequent updates, which has significant implications for global dynamic observation of urban three-dimensional features.

Keywords: satellite laser altimetry; ICESat-2; building height; random forest; Sentinel-1; Sentinel-2; VIIRS; dynamic monitoring (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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