Deep learning enables urban change profiling through alignment of historical maps
This framework uses deep learning to automatically analyze urban changes from historical maps. It first aligns different map editions by correcting spatial differences, then detects specific urban features like buildings using advanced segmentation, and finally quantifies changes over time. This allows for systematic, quantitative tracking of how cities evolve historically without manual comparison.