Abstract
Automated classification of images across image archives requires reducing the semantic gap between high-level features perceived by humans and low-level features encoded in images. Due to rapidly growing image archives in the Earth science domain, it is critical to automatically classify images for efficient sorting and discovery. In particular, classifying images based on the presence of Earth science phenomena allows users to perform climatology studies and investigate case studies. We present applications of deep learning-based classification of Earth science images.
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