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Earth observation (EO) in open-world settings presents a unique challenge: different applications rely on diverse sensor modalities, each with varying ground sampling distances, spectral ranges, and numbers of spectral bands.
Short-term synaptic plasticity
Robert S Zucker and Wade G Regehr · 2002
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Hypernetworks
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Attention is all you need
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Deep learning using rectified linear units (ReLU)
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Tile2Vec: Unsupervised representation learning for spatially distributed data
Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, and Stefano Ermon · 2019
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Deep learning and process understanding for data-driven Earth system science
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Geography-aware self-supervised learning
Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, and Stefano Ermon · 2021
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Deep learning for the Earth sciences: A comprehensive approach to remote sensing, climate science and geosciences
Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, and Markus Reichstein · 2021
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Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data
Oscar Manas, Alexandre Lacoste, Xavier Giró-i Nieto, David Vazquez, and Pau Rodriguez · 2021
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How to train your ViT? data, augmentation, and regularization in vision transformers
Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer · 2021
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Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, and Stefano Ermon · 2022
Towards geospatial foundation models via continual pretraining
Matías Mendieta, Boran Han, Xingjian Shi, Yi Zhu, and Chen Chen · 2023
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CMID: A unified self-supervised learning framework for remote sensing image understanding
Dilxat Muhtar, Xueliang Zhang, Pengfeng Xiao, Zhenshi Li, and Feng Gu · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Scale-MAE: A scale-aware masked autoencoder for multiscale geospatial representation learning
Colorado J Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell · 2023
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Tov: The original vision model for optical remote sensing image understanding via self-supervised learning
Chao Tao, Ji Qi, Guo Zhang, Qing Zhu, Weipeng Lu, and Haifeng Li · 2023
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Convmae: Masked convolution meets masked autoencoders
Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, and Yu Qiao · 2022
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Masked autoencoders are scalable vision learners
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Anthony Fuller, Koreen Millard, and James R Green · 2023
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RingMo-sense: Remote sensing foundation model for spatiotemporal prediction via spatiotemporal evolution disentangling
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SpectralGPT: Spectral remote sensing foundation model
D Hong, B Zhang, X Li, Y Li, C Li, J Yao, N Yokoya, H Li, P Ghamisi, X Jia, et al · 2024
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Pangaea: A global and inclusive benchmark for geospatial foundation models
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Skysense v2: A unified foundation model for multi-modal remote sensing
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