This project develops a deep learning pipeline for mapping urban tree canopy cover change from the 1960s to present using historical panchromatic aerial imagery of Malmö, Gothenburg and Stockholm. We fine-tune BWTreeNet, a U-Net based segmentation architecture, on Swedish aerial imagery paired with manually annotated canopy labels across multiple decades. GPU resources are required for iterative model training experiments and city-scale inference across multiple time periods. The project is funded by the Swedish University of Agricultural Sciences (SLU) until end of 2027.