Research Center for Humanities and Social Sciences, Academia Sinica

Developing deep-learning models guided by semantics to colorize historical grayscale aerial imagery for land-cover classification

High-resolution historical aerial imagery is valuable for analyzing long-term land-cover and environmental change, but most deep-learning methods rely on multispectral or color data and cannot fully use grayscale archives. We developed a semantically guided framework that uses Op...