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Novel training methods that exploit the spatio-temporal structure of remote sensing data.
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2026-03-02 21:31:40
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Geography-Aware Self-Supervised Learning Geography-Aware Self-Supervised Learning Kumar Ayush* , Burak Uzkent* , Chenlin Meng* , Kumar Tanmay , Marshall Burke , David B. Lobell , Stefano Ermon Stanford University ICCV 2021 Paper arXiv Code Dataset Images over time concept in the Functional Map of the World (fMoW) dataset. The metadata associated with each image is shown underneath. We can see changes in contrast, brightness, cloud cover etc. in the images. These changes render spatially aligned images over time useful for constructing additional positives. Abstract 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 geo-located datasets, e.g. remote sensing, where unlabeled data is often abundant but labeled data is scarce. We first show that due to their different c...
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