PANGAEA provides a standardized multi-task, multi-sensor, and multi-temporal benchmark for geospatial foundation models, and shows that they do not consistently outperform supervised baselines.
Self-supervised Learning in Remote Sensing: A Review
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abstract
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While there has been a big success in computer vision, most of the potential of SSL in the domain of earth observation remains locked. In this paper, we provide an introduction to, and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for earth observation (SSL4EO) to pave the way for fruitful interaction of both domains.
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PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
PANGAEA provides a standardized multi-task, multi-sensor, and multi-temporal benchmark for geospatial foundation models, and shows that they do not consistently outperform supervised baselines.