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Massive amounts of unlabelled data are captured by Earth Observation (EO) satellites, with the Sentinel-2 constellation generating 1.6 TB of data daily.
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“Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data,”
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“Geography-aware self-supervised learning,” arxiv:2011.09980, 2021
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Michail Tarasiou and Stefanos Zafeiriou, · 2022
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“Self-supervised learning for scene classification in remote sensing: Current state of the art and perspectives,”
Paul Berg et al., · 2022
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“CORSA deep Earth Observation semantic compression applied to flood detection,”
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“Masked autoencoders are scalable vision learners,” In Proc. CVPR, 2022
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“Globe230k: A benchmark dense-pixel annotation dataset for global land cover mapping,”
Qian Shi et al., · 2023
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“USat: A unified self-supervised encoder for multi-sensor satellite,”
J. Irvin et al., · 2023
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“ClimaX: A foundation model for weather and climate,” arxiv:2301.10343, 2023
Tung Nguyen et al., · 2023
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“Lightweight, pre-trained Transformers for remote sensing timeseries,” arxiv:2304.14065, 2023
G. Tseng et al., · 2023
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“Change-aware sampling and contrastive learning for satellite images,”
Utkarsh Mall et al., · 2023
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“Geographical knowledge-driven representation learning for remote sensing images,”
Wenyuan Li et al., · 2022
Cited alongside, same era.
“An agenda for multimodal Foundation Models for Earth Observation,” In Proc. IGARSS, 2023
Philipe Dias, Abhishek Potnis, Sreelekha Guggilam, et al., · 2023
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“Foundation Models for Generalist Geospatial Artificial Intelligence,” arxiv:2310.18660, 2023
Johannes Jakubik, Sujit Roy, C. E. Phillips, Paolo Fraccaro, et al., · 2023
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Yezhen Cong, Samar Khanna, et al., · 2023
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“Artificial Intelligence to advance Earth observation: A perspective,” arxiv:2305.08413, 2023
D. Tuia et al., · 2023
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“Satlas: A large-scale, multi-task dataset for RS image understanding,” In ICCV, 2023
Favyen Bastani et al., · 2023
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“Towards geospatial foundation models via continual pretraining,” In Proc. ICCV, 2023
Matias Mendieta et al., · 2023
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“Geo-Bench: Toward Foundation Models for Earth monitoring,” arxiv:2306.03831, 2023
A. Lacoste et al., · 2023
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“The trade-off between universality and label efficiency of representations from contrastive learning,”
Zhenmei Shi, Jiefeng Chen, et al., · 2023
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“SatCLIP: Global, general-purpose location embeddings,”
K. Klemmer et al., · 2023
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Isaac Corley et al., · 2023
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“SpectralGPT: Spectral foundation model,”
Danfeng Hong et al., · 2023
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“A cookbook of self-supervised learning,”
Randall Balestriero et al., · 2023
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