Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 2
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

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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.
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