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Geography-Aware Self-Supervised Learning

2020-11-19 17:29:13
Kumar Ayush, Burak Uzkent, Chenlin Meng, Marshall Burke, David Lobell, Stefano Ermon

Abstract

tract: Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks. In this paper, we explore their application to remote sensing, where unlabeled data is often abundant but labeled data is scarce. We first show that due to their different characteristics, a non-trivial gap persists between contrastive and supervised learning on standard benchmarks. To close the gap, we propose novel training methods that exploit the spatiotemporal structure of remote sensing data. We leverage spatially aligned images over time to construct temporal positive pairs in contrastive learning and geo-location to design pre-text tasks. Our experiments show that our proposed method closes the gap between contrastive and supervised learning on image classification, object detection and semantic segmentation for remote sensing and other geo-tagged image datasets

Abstract (translated)

URL

https://arxiv.org/abs/2011.09980

PDF

https://arxiv.org/pdf/2011.09980


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