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The Last Puzzle of Global Building Footprints—Mapping 280 Million Buildings in East Asia Based on VHR Images

Authors :
Qian Shi
Jiajun Zhu
Zhengyu Liu
Haonan Guo
Song Gao
Mengxi Liu
Zihong Liu
Xiaoping Liu
Source :
Journal of Remote Sensing, Vol 4 (2024)
Publication Year :
2024
Publisher :
American Association for the Advancement of Science (AAAS), 2024.

Abstract

Building, as an integral aspect of human life, is vital in the domains of urban management and urban analysis. To facilitate large-scale urban planning applications, the acquisition of complete and reliable building data becomes imperative. There are a few publicly available products that provide a lot of building data, such as Microsoft and Open Street Map. However, in East Asia, due to the more complex distribution of buildings and the scarcity of auxiliary data, there is a lack of building data in these regions, hindering the large-scale application in East Asia. Some studies attempt to simulate large-scale building distribution information using incomplete local buildings footprints data through regression. However, the reliance on inaccurate buildings data introduces cumulative errors, rendering this simulation data highly unreliable, leading to limitations in achieving precise research in East Asian region. Therefore, we proposed a comprehensive large-scale buildings mapping framework in view of the complexity of buildings in East Asia, and conducted buildings footprints extraction in 2,897 cities across 5 countries in East Asia and yielded a substantial dataset of 281,093,433 buildings. The evaluation shows the validity of our building product, with an average overall accuracy of 89.63% and an F1 score of 82.55%. In addition, a comparison with existing products further shows the high quality and completeness of our building data. Finally, we conduct spatial analysis of our building data, revealing its value in supporting urban-related research. The data for this article can be downloaded from https://doi.org/10.5281/zenodo.8174931.

Details

Language :
English
ISSN :
26941589
Volume :
4
Database :
Directory of Open Access Journals
Journal :
Journal of Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.3ca3ac59b54e44caa9d13f8ce1c3f6b3
Document Type :
article
Full Text :
https://doi.org/10.34133/remotesensing.0138