Enhancing Urban Canopy Prediction Through Selective Image Matching for Zero-Shot and Few-Sample Unsupervised Domain Adaptation

Simple data-based image matching methods can effectively adapt a multi-task deep learning algorithm for urban canopy cover and height prediction to a new geographic setting, especially with a small amount of fine-tuning.