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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-85', Anonymous Referee #1, 11 Sep 2022
  • RC2: 'Comment on gmd-2022-85', Anonymous Referee #2, 25 Oct 2022
  • AC3: 'Revised Manuscript', Ruidong Li, 01 Nov 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ruidong Li on behalf of the Authors (02 Nov 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (28 Dec 2022) by Richard Mills
AR by Ruidong Li on behalf of the Authors (05 Jan 2023)  Author's response    Manuscript
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.