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“Deep learning in remote sensing: A comprehensive review and list of resources,”
Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, and Friedrich Fraundorfer, · 2017
Earlier work this paper cites.
“An image is worth 16x16 words: Transformers for image recognition at scale,”
Original
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al., · 2020
Earlier work this paper cites.
“Earthnets: Empowering ai in earth observation,”
Original
Zhitong Xiong, Fahong Zhang, Yi Wang, Yilei Shi, and Xiao Xiang Zhu, · 2022
Earlier work this paper cites.
“Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery,”
Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, and Stefano Ermon, · 2022
Earlier work this paper cites.
“Ssl4eo-s12: A large-scale multi-modal, multi-temporal dataset for self-supervised learning in earth observation,”
Original
Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M Albrecht, and Xiao Xiang Zhu, · 2022
Earlier work this paper cites.
“Self-supervised learning for invariant representations from multi-spectral and sar images,”
Pallavi Jain, Bianca Schoen-Phelan, and Robert Ross, · 2022
Earlier work this paper cites.

Similar

“The benchmark: Transferable representation learning for monocular height estimation,”
Zhitong Xiong, Wei Huang, Jingtao Hu, and Xiao Xiang Zhu, · 2023
Cited alongside, same era.
“Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery,”
Original
Xin Guo, Jiangwei Lao, Bo Dang, Yingying Zhang, Lei Yu, Lixiang Ru, Liheng Zhong, Ziyuan Huang, Kang Wu, Dingxiang Hu, et al., · 2023
Cited alongside, same era.
“Satlaspretrain: A large-scale dataset for remote sensing image understanding,”
Favyen Bastani, Piper Wolters, Ritwik Gupta, Joe Ferdinando, and Aniruddha Kembhavi, · 2023
Cited alongside, same era.
“A billion-scale foundation model for remote sensing images,”
Original
Keumgang Cha, Junghoon Seo, and Taekyung Lee, · 2023
Cited alongside, same era.
“Earthpt: a foundation model for earth observation,”
Original

Then

“Enabling country-scale land cover mapping with meter-resolution satellite imagery,”
Xin-Yi Tong, Gui-Song Xia, and Xiao Xiang Zhu, · 2023
Later among the works it cites.
“Hyspecnet-11k: A large-scale hyperspectral dataset for benchmarking learning-based hyperspectral image compression methods,”
Original
Martin Hermann Paul Fuchs and Begüm Demir, · 2023
Later among the works it cites.
“Causal graph neural networks for wildfire danger prediction,”
Original
Shan Zhao, Ioannis Prapas, Ilektra Karasante, Zhitong Xiong, Ioannis Papoutsis, Gustau Camps-Valls, and Xiao Xiang Zhu, · 2024
Closest in time.
“On the foundations of earth and climate foundation models,”
Original
Xiao Xiang Zhu, Zhitong Xiong, Yi Wang, Adam J Stewart, Konrad Heidler, Yuanyuan Wang, Zhenghang Yuan, Thomas Dujardin, Qingsong Xu, and Yilei Shi, · 2024
Closest in time.
“Neural plasticity-inspired foundation model for observing the Earth crossing modalities,”

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2024

One for All: Toward Unified Foundation Models for Earth Vision

Xiong, Zhitong, Wang, Yi, Zhang, Fahong et al.

Understand

Foundation models characterized by extensive parameters and trained on large-scale datasets have demonstrated remarkable efficacy across various downstream tasks for remote sensing data.

Current remote sensing foundation models typically specialize in a single modality or a specific spatial resolution range, limiting their versatility for downstream datasets.
While there have been attempts to develop multi-modal remote sensing foundation models, they typically employ separate vision encoders for each modality or spatial resolution, necessitating a switch in backbones contingent upon the input data.
To address this issue, we introduce a simple yet effective method, termed OFA-Net (One-For-All Network): employing a single, shared Transformer backbone for multiple data modalities with different spatial resolutions.

Built on

“Deep learning in remote sensing: A comprehensive review and list of resources,”
Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, and Friedrich Fraundorfer, · 2017
Earlier work this paper cites.
“An image is worth 16x16 words: Transformers for image recognition at scale,”
Original
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al., · 2020
Earlier work this paper cites.
“Earthnets: Empowering ai in earth observation,”
Original
Zhitong Xiong, Fahong Zhang, Yi Wang, Yilei Shi, and Xiao Xiang Zhu, · 2022
Earlier work this paper cites.
“Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery,”
Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, and Stefano Ermon, · 2022
Earlier work this paper cites.
“Ssl4eo-s12: A large-scale multi-modal, multi-temporal dataset for self-supervised learning in earth observation,”
Original
Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M Albrecht, and Xiao Xiang Zhu, · 2022
Earlier work this paper cites.
“Self-supervised learning for invariant representations from multi-spectral and sar images,”
Pallavi Jain, Bianca Schoen-Phelan, and Robert Ross, · 2022
Earlier work this paper cites.

Similar

“The benchmark: Transferable representation learning for monocular height estimation,”
Zhitong Xiong, Wei Huang, Jingtao Hu, and Xiao Xiang Zhu, · 2023
Cited alongside, same era.
“Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery,”
Original
Xin Guo, Jiangwei Lao, Bo Dang, Yingying Zhang, Lei Yu, Lixiang Ru, Liheng Zhong, Ziyuan Huang, Kang Wu, Dingxiang Hu, et al., · 2023
Cited alongside, same era.
“Satlaspretrain: A large-scale dataset for remote sensing image understanding,”
Favyen Bastani, Piper Wolters, Ritwik Gupta, Joe Ferdinando, and Aniruddha Kembhavi, · 2023
Cited alongside, same era.
“A billion-scale foundation model for remote sensing images,”
Original
Keumgang Cha, Junghoon Seo, and Taekyung Lee, · 2023
Cited alongside, same era.
“Earthpt: a foundation model for earth observation,”
Original

Then

“Enabling country-scale land cover mapping with meter-resolution satellite imagery,”
Xin-Yi Tong, Gui-Song Xia, and Xiao Xiang Zhu, · 2023
Later among the works it cites.
“Hyspecnet-11k: A large-scale hyperspectral dataset for benchmarking learning-based hyperspectral image compression methods,”
Original
Martin Hermann Paul Fuchs and Begüm Demir, · 2023
Later among the works it cites.
“Causal graph neural networks for wildfire danger prediction,”
Original
Shan Zhao, Ioannis Prapas, Ilektra Karasante, Zhitong Xiong, Ioannis Papoutsis, Gustau Camps-Valls, and Xiao Xiang Zhu, · 2024
Closest in time.
“On the foundations of earth and climate foundation models,”
Original
Xiao Xiang Zhu, Zhitong Xiong, Yi Wang, Adam J Stewart, Konrad Heidler, Yuanyuan Wang, Zhenghang Yuan, Thomas Dujardin, Qingsong Xu, and Yilei Shi, · 2024
Closest in time.
“Neural plasticity-inspired foundation model for observing the Earth crossing modalities,”

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Michael J Smith, Luke Fleming, and James E Geach, · 2023

Cited alongside, same era.

“Geo-bench: Toward foundation models for earth monitoring,”

Original

Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan David Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Andrew Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, et al., · 2023

Cited alongside, same era.

Original

Zhitong Xiong, Yi Wang, Fahong Zhang, Adam J Stewart, Joëlle Hanna, Damian Borth, Ioannis Papoutsis, Bertrand Le Saux, Gustau Camps-Valls, and Xiao Xiang Zhu, · 2024

Closest in time.