SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery

Li, Ruidong; Sun, Ting; Tian, Fuqiang; Ni, Guang-Heng

doi:https://doi.org/10.5194/gmd-16-751-2023

Articles | Volume 16, issue 2

https://doi.org/10.5194/gmd-16-751-2023

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

https://doi.org/10.5194/gmd-16-751-2023

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

Articles | Volume 16, issue 2

Model description paper

|

31 Jan 2023

Model description paper |

| 31 Jan 2023

SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery

Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni

Supplement

https://doi.org/10.5194/gmd-16-751-2023-supplement

Data sets

Reference Datasets for: SHAFTS (v2022.3): a deep-learning-based Python package for Simultaneous extraction of building Height And FootprinT from Sentinel Imagery Ruidong Li and Ting Sun https://doi.org/10.5281/zenodo.6587510

Model code and software

Simultaneous building Height And FootprinT extraction from Sentinel Imagery Ruidong Li and Ting Sun https://github.com/LllC-mmd/3DBuildingInfoMap

Short summary

We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.