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

Assesing the Value of High-Resolution Data and Parameters Transferability Across Temporal Scales in Hydrological Modeling: A Case Study in Northern China
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-2966,https://doi.org/10.5194/egusphere-2024-2966, 2024
Short summary
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024,https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Delayed Stormflow Generation in a Semi-humid Forested Watershed Controlled by Soil Water Storage and Groundwater
Zhen Cui and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-2177,https://doi.org/10.5194/egusphere-2024-2177, 2024
Short summary
Bimodal hydrographs in a semi-humid forested watershed: characteristics and occurrence conditions
Zhen Cui, Fuqiang Tian, Zilong Zhao, Zitong Xu, Yongjie Duan, Jie Wen, and Mohd Yawar Ali Khan
Hydrol. Earth Syst. Sci., 28, 3613–3632, https://doi.org/10.5194/hess-28-3613-2024,https://doi.org/10.5194/hess-28-3613-2024, 2024
Short summary
Assessing the value of high-resolution rainfall and streamflow data for hydrological modeling: An analysis based on 63 catchments in southeast China
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1438,https://doi.org/10.5194/egusphere-2024-1438, 2024
Short summary

Related subject area

Earth and space science informatics
Random forests with spatial proxies for environmental modelling: opportunities and pitfalls
Carles Milà, Marvin Ludwig, Edzer Pebesma, Cathryn Tonne, and Hanna Meyer
Geosci. Model Dev., 17, 6007–6033, https://doi.org/10.5194/gmd-17-6007-2024,https://doi.org/10.5194/gmd-17-6007-2024, 2024
Short summary
An improved global pressure and zenith wet delay model with optimized vertical correction considering the spatiotemporal variability in multiple height-scale factors
Chunhua Jiang, Xiang Gao, Huizhong Zhu, Shuaimin Wang, Sixuan Liu, Shaoni Chen, and Guangsheng Liu
Geosci. Model Dev., 17, 5939–5959, https://doi.org/10.5194/gmd-17-5939-2024,https://doi.org/10.5194/gmd-17-5939-2024, 2024
Short summary
kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation
Jan Linnenbrink, Carles Milà, Marvin Ludwig, and Hanna Meyer
Geosci. Model Dev., 17, 5897–5912, https://doi.org/10.5194/gmd-17-5897-2024,https://doi.org/10.5194/gmd-17-5897-2024, 2024
Short summary
GNNWR: An Open-Source Package of Spatiotemporal Intelligent Regression Methods for Modeling Spatial and Temporal Non-Stationarity
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-62,https://doi.org/10.5194/gmd-2024-62, 2024
Revised manuscript accepted for GMD
Short summary
Consistency-Checking 3D Geological Models
Marion N. Parquer, Eric A. de Kemp, Boyan Brodaric, and Michael J. Hillier
EGUsphere, https://doi.org/10.5194/egusphere-2024-1326,https://doi.org/10.5194/egusphere-2024-1326, 2024
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.