Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data

CVPR 2019 Caleb Robinson Le Hou Kolya Malkin Rachel Soobitsky Jacob Czawlytko Bistra Dilkina Nebojsa Jojic

In this paper we propose multi-resolution data fusion methods for deep learning-based high-resolution land cover mapping from aerial imagery. The land cover mapping problem, at country-level scales, is challenging for common deep learning methods due to the scarcity of high-resolution labels, as well as variation in geography and quality of input images... (read more)

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