China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5&thinsp;m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery

Liu, Zeping; Tang, Hong; Feng, Lin; Lyu, Siqing

doi:https://doi.org/10.5194/essd-15-3547-2023

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.

https://doi.org/10.5194/essd-15-3547-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 8

Data description paper

|

09 Aug 2023

Data description paper |

| 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, Hong Tang, Lin Feng, and Siqing Lyu

Supplement

https://doi.org/10.5194/essd-15-3547-2023-supplement

Data sets

CBRA: 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, Hong Tang, Lin Feng, and Siqing Lyu https://doi.org/10.5281/zenodo.7500612

Model code and software

Spatio-Temporal aware Super-Resolution SEGmentation framework Zeping Liu https://github.com/zpl99/STSR-Seg

Download

Article (29462 KB)
Full-text XML

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.