NEURAL NETWORKS FOR THE CLASSIFICATION OF BUILDING USE FROM STREET-VIEW IMAGERY

7 citations as recorded by crossref.

1. CRFFNet: A cross-view reprojection based feature fusion network for fine-grained building segmentation using satellite-view and street-view data J. Yu et al. 10.1016/j.inffus.2025.103795
2. Towards High-Resolution Population Mapping: Leveraging Open Data, Remote Sensing, and AI for Geospatial Analysis in Developing Country Cities—A Case Study of Bangkok K. Maneepong et al. 10.3390/rs17071204
3. An annotated image database of building facades categorized into land uses for object detection using deep learning F. Bortoloti et al. 10.1007/s00138-022-01335-5
4. Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review Y. Li et al. 10.3390/buildings12081167
5. Automated identification of building features with deep learning for risk analysis F. Gouveia et al. 10.1007/s42452-024-06070-2
6. Fusion of Google Street View, LiDAR, and Orthophoto Classifications Using Ranking Classes Based on F1 Score for Building Land-Use Type Detection N. Ghasemian Sorboni et al. 10.3390/rs16112011
7. Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting K. Maneepong et al. 10.3390/rs16213922