Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-751-2023
https://doi.org/10.5194/gmd-16-751-2023
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

Related authors

Enhancing Urban Pluvial Flood Modelling through Graph Reconstruction of Incomplete Sewer Networks
Ruidong Li, Jiapei Liu, Ting Sun, Shao Jian, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3780,https://doi.org/10.5194/egusphere-2024-3780, 2025
Short summary

Related subject area

Earth and space science informatics
DustNet (v1): skilful neural network predictions of dust aerosols over the Saharan desert
Trish E. Nowak, Andy T. Augousti, Benno I. Simmons, and Stefan Siegert
Geosci. Model Dev., 18, 3509–3532, https://doi.org/10.5194/gmd-18-3509-2025,https://doi.org/10.5194/gmd-18-3509-2025, 2025
Short summary
RiverBedDynamics v1.0: a Landlab component for computing two-dimensional sediment transport and river bed evolution
Angel D. Monsalve, Samuel R. Anderson, Nicole M. Gasparini, and Elowyn M. Yager
Geosci. Model Dev., 18, 3427–3451, https://doi.org/10.5194/gmd-18-3427-2025,https://doi.org/10.5194/gmd-18-3427-2025, 2025
Short summary
A GPU parallelization of the neXtSIM-DG dynamical core (v0.3.1)
Robert Jendersie, Christian Lessig, and Thomas Richter
Geosci. Model Dev., 18, 3017–3040, https://doi.org/10.5194/gmd-18-3017-2025,https://doi.org/10.5194/gmd-18-3017-2025, 2025
Short summary
The Earth System Grid Federation (ESGF) Virtual Aggregation (CMIP6 v20240125)
Ezequiel Cimadevilla, Bryan N. Lawrence, and Antonio S. Cofiño
Geosci. Model Dev., 18, 2461–2478, https://doi.org/10.5194/gmd-18-2461-2025,https://doi.org/10.5194/gmd-18-2461-2025, 2025
Short summary
Can AI be enabled to perform dynamical downscaling? A latent diffusion model to mimic kilometer-scale COSMO5.0_CLM9 simulations
Elena Tomasi, Gabriele Franch, and Marco Cristoforetti
Geosci. Model Dev., 18, 2051–2078, https://doi.org/10.5194/gmd-18-2051-2025,https://doi.org/10.5194/gmd-18-2051-2025, 2025
Short summary

Cited articles

Bengio, Y., Courville, A., and Vincent, P.: Representation Learning: A Review and New Perspectives, IEEE T. Pattern Anal., 35, 1798–1828, https://doi.org/10.1109/tpami.2013.50, 2013. a, b
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, 1996. a
Brunner, D., Lemoine, G., Bruzzone, L., and Greidanus, H.: Building Height Retrieval From VHR SAR Imagery Based on an Iterative Simulation and Matching Technique, IEEE T. Geosci. Remote, 48, 1487–1504, https://doi.org/10.1109/tgrs.2009.2031910, 2010. a
Bruwier, M., Maravat, C., Mustafa, A., Teller, J., Pirotton, M., Erpicum, S., Archambeau, P., and Dewals, B.: Influence of urban forms on surface flow in urban pluvial flooding, J. Hydrol., 582, 124493, https://doi.org/10.1016/j.jhydrol.2019.124493, 2020. a
Burke, M., Driscoll, A., Lobell, D. B., and Ermon, S.: Using satellite imagery to understand and promote sustainable development, Science, 371, eabe8626, https://doi.org/10.1126/science.abe8628, 2021. a, b
Download
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.
Share