SHAFTS (v2022.3): a deep-learning-based Python package for Simultaneous extraction of building Height And FootprinT from Sentinel Imagery

Abstract. Building height and footprint are two fundamental urban morphological features required by urban climate modeling. Although some statistical methods have been proposed to estimate average building height and footprint from publicly available satellite imagery, they often involve tedious feature engineering, which is hard to achieve efficient knowledge discovery in a changing urban environment with ever-increasing earth observations. In this work, we develop a deep-learning-based (DL) Python package–SHATFS (Simultaneous building Height And FootprinT extraction from Sentinel Imagery) to extract such information. Multi-task DL (MTDL) models are proposed to automatically learn feature representation shared by building height and footprint prediction. Besides, we integrate Digital Elevation Model (DEM) information into developed models to inform models of terrain-induced effects on the backscattering displayed by Sentinel-1 imagery. We set conventional machine-learning-based (ML) models and single-task DL (STDL) models as benchmarks and select 46 cities worldwide to evaluate developed models’ patch-level prediction skills and city-level spatial transferability at four resolutions (100 m, 250 m, 500 m and 1000 m). Patch-level results of 43 cities show that DL models successfully produce more discriminative feature representation and improve the coefficient of determination (R²) of building height and footprint prediction over ML models by 0.27–0.63, 0.11–0.49, respectively. Moreover, stratified error assessment reveals that DL models effectively mitigate severe systematic underestimation of ML models in the high-value domain: for the 100 m case, DL models reduce Root Mean Square Error of building height higher than 40 m and building footprint larger than 0.25 by 31 m and 0.1, respectively, which demonstrates the superiority of DL models on refined 3D building information extraction in highly urbanized area. For the evaluation of spatial transferability, when compared with an existing state-of-the-art product, DL models can achieve similar improvement on the overall performance and high-value prediction. Furthermore, within the DL family, comparison in building height prediction between STDL and MTDL models reveals that MTDL models achieve higher accuracy in all cases and smaller bias uncertainty for the prediction in the high-value domain at the refined scale, which proves the effectiveness of multi-task learning on building height estimation.