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Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets containing remotely sensed imagery.
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Lightgbm: A highly efficient gradient boosting decision tree
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery
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Deepsat v2: feature augmented convolutional neural nets for satellite image classification
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A metric learning reality check
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