Application of Deep Learning on Global Spaceborne Radar and Multispectral Imagery for the Estimation of Urban Surface Height Distribution

Digital Surface Models (DSMs) have a wide range of applications, including the spatial and temporal analysis of human habitation. Traditionally, DSMs are generated by rasterizing Light Detection and Ranging (LiDAR) point clouds. While LiDAR provides high-resolution details, the acquisition of required data is logistically challenging and costly, leading to limited spatial coverage and temporal frequency. Satellite imagery, such as Synthetic Aperture Radar (SAR), contains information on surface height variations in the scene within the reflected signal. Transforming satellite imagery data into a global DSM is challenging but would be of great value if those challenges were overcome. This study explores the application of a U-Net architecture to generate DSMs by coupling Sentinel-1 SAR and Sentinel-2 optical imagery. The model is trained on surface height data from multiple U.S. cities to produce a normalized DSM (NDSM) and assess its ability to generalize inferences for cities outside the training dataset. The analysis of the results shows that the model performs moderately well when inferring test cities but its performance remains well below that of the training cities. Further examination, through the comparison of height distributions and cross-sectional analysis, reveals that estimation bias is influenced by the input image resolution and the presence of geometric distortion within the SAR image. These findings highlight the need for refinement in preprocessing techniques as well as advanced training approaches and model architecture that can better handle the complexities of urban landscapes encoded in satellite imagery.