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Geosr: A Computer Vision Package for Deep Learning Based Single-Frame Remote Sensing Imagery Super-Resolution

Recently, owing to the outstanding capability of deep learning in solving ill-posed problems, the single-frame super-resolution (SR) researches tend to focus on deep learning methods largely. However, related researches are implemented and evaluated through various datasets and different deep learning frameworks, which hinders the comparison of performance among different methods and heavily hampers the progress of SR techniques. In this study, we present GeoSR, an open source computer vision package for deep learning based single-frame remote sensing imagery super-resolution to facilitate the development of the SR community. As a unified, simple, and flexible package, GeoSR contains pipeline-like integrated tools from data retrieval to final result evaluation, which enables users to develop self-defined models conveniently; several state-of-the-art models trained through the same high-quality dataset are provided as the baseline in the package as well. Moreover, the proposed package could potentially serve as a viable backend for other related packages such as image segmentation with high efficiency.