Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3547-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-3547-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
09 Aug 2023
Data description paper |
| 09 Aug 2023
| 09 Aug 2023
China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery
Zeping Liu
Key Laboratory of Environmental Change and Natural Disaster of
Ministry of Education, Beijing Normal University, Beijing 100875,
China
Beijing Key Laboratory for Remote Sensing of Environment and Digital
Cities, Faculty of Geographical Science, Beijing Normal University, Beijing
100875, China
Key Laboratory of Environmental Change and Natural Disaster of
Ministry of Education, Beijing Normal University, Beijing 100875,
China
Beijing Key Laboratory for Remote Sensing of Environment and Digital
Cities, Faculty of Geographical Science, Beijing Normal University, Beijing
100875, China
State Key Laboratory of Remote Sensing Science, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Lin Feng
Beijing Key Laboratory for Remote Sensing of Environment and Digital
Cities, Faculty of Geographical Science, Beijing Normal University, Beijing
100875, China
Siqing Lyu
Beijing Key Laboratory for Remote Sensing of Environment and Digital
Cities, Faculty of Geographical Science, Beijing Normal University, Beijing
100875, China
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Total article views: 4,055 (including HTML, PDF, and XML)
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- Fusing multimodal data of nature-economy-society for large-scale urban building height estimation S. Du et al. 10.1016/j.jag.2024.103809
- China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery Z. Liu et al. 10.5194/essd-15-3547-2023
Latest update: 13 Dec 2024
Short summary
Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In this paper, we propose a deep-learning method for high-resolution BRA mapping (2.5 m) from Sentinel-2 imagery (10 m). The resulting China building rooftop area dataset (CBRA) is the first multi-annual (2016–2021) and high-resolution (2.5 m) BRA dataset in China. Cross-comparisons show that the CBRA achieves the best performance in capturing the spatiotemporal information.
Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and...
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