12 citations as recorded by crossref.

1. Morphology-guided cross-task coupling for joint building height and footprint estimation J. Han et al. https://doi.org/10.1016/j.srs.2026.100474
2. Multi-angle analysis of shading-based modelling: extracting the urban building height based on ZY-3 three-line-array camera S. Lu et al. https://doi.org/10.1080/01431161.2025.2501107
3. Spatio-temporal variation of building morphology in Indian Cities from 2018 to 2023 N. Mishra et al. https://doi.org/10.1007/s12518-026-00765-4
4. CNNs for remote extraction of urban features: A survey-driven benchmarking B. Neupane et al. https://doi.org/10.1016/j.eswa.2024.124751
5. A systematic review of building height estimation using remote sensing imagery P. Hu et al. https://doi.org/10.1080/10095020.2026.2728378
6. Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau B. Li et al. https://doi.org/10.1016/j.jhydrol.2023.129401
7. GLAMOUR: GLobAl building MOrphology dataset for URban hydroclimate modelling R. Li et al. https://doi.org/10.1038/s41597-024-03446-2
8. A Review of Urban Building Extraction From Synthetic Aperture Radar Imagery Based on Deep Learning F. Wu et al. https://doi.org/10.1109/ACCESS.2025.3593955
9. MF-BHNet: A Hybrid Multimodal Fusion Network for Building Height Estimation Using Sentinel-1 and Sentinel-2 Imagery S. Wang et al. https://doi.org/10.1109/TGRS.2024.3477588
10. Revealing the Impact of Urban Morphology Evolution on the Urban Heat Island Effect in the Main Urban Area of Guangzhou: Insights from Local Climate Zones X. Yang et al. https://doi.org/10.3390/rs17172959
11. FU3D: the first global projections of future urban three-dimensional (3D) expansion for the 21st century under shared socioeconomic pathways Q. Zhao et al. https://doi.org/10.1038/s41597-025-05711-4
12. Diurnal Asymmetry in the Relationships Between Urban Morphology and Canopy Urban Heat Islands: An Interpretable Machine Learning Analysis T. Shi & G. Lu https://doi.org/10.3390/rs18172980