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

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

2024
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Overview
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 .
Publisher
American Association for the Advancement of Science (AAAS)