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Self-supervised learning (SSL) has revolutionized representation learning in Remote Sensing (RS), advancing Geospatial Foundation Models (GFMs) to leverage vast unlabeled satellite imagery for diverse downstream tasks.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Unified perceptual parsing for scene understanding
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun · 2018
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Segdiff: Image segmentation with diffusion probabilistic models
Tomer Amit, Tal Shaharbany, Eliya Nachmani, and Lior Wolf · 2021
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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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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Ddpm-cd: Denoising diffusion probabilistic models as feature extractors for change detection
Wele Gedara Chaminda Bandara, Nithin Gopalakrishnan Nair, and Vishal M Patel · 2022
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Label-efficient semantic segmentation with diffusion models
Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2022
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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 B Lobell, and Stefano Ermon · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Towards a foundation model for geospatial artificial intelligence (vision paper)
Gengchen Mai, Chris Cundy, Kristy Choi, Yingjie Hu, Ni Lao, and Stefano Ermon · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Self-supervised learning in remote sensing: A review
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Lichao Mou, and Xiao Xiang Zhu · 2022
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Simmim: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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Satlaspretrain: A large-scale dataset for remote sensing image understanding
Favyen Bastani, Piper Wolters, Ritwik Gupta, Joe Ferdinando, and Aniruddha Kembhavi · 2023
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Croma: Remote sensing representations with contrastive radar-optical masked autoencoders, 2023
Anthony Fuller, Koreen Millard, and James R. Green · 2023
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Tdiffde: A truncated diffusion model for remote sensing hyperspectral image denoising
Jiang He, Yajie Li, Qiangqiang Yuan, et al · 2023
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Aliasghar Khani, Saeid Asgari Taghanaki, Aditya Sanghi, Ali Mahdavi Amiri, and Ghassan Hamarneh · 2023
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Diffusion hyperfeatures: Searching through time and space for semantic correspondence
Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell · 2023
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Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning, 2023
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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Ssl4eo-s12: A large-scale multimodal, multitemporal dataset for self-supervised learning in earth observation [software and data sets]
Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M. Albrecht, and Xiao Xiang Zhu · 2023
Cited alongside, same era.
Datasetdm: Synthesizing data with perception annotations using diffusion models
Remote sensing vision-language foundation models without annotations via ground remote alignment
Utkarsh Mall, Cheng Perng Phoo, Meilin Kelsey Liu, Carl Vondrick, Bharath Hariharan, and Kavita Bala · 2024
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Cross-sensor self-supervised training and alignment for remote sensing, 2024
Valerio Marsocci and Nicolas Audebert · 2024
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Pangaea: A global and inclusive benchmark for geospatial foundation models, 2024
Valerio Marsocci, Yuru Jia, Georges Le Bellier, David Kerekes, Liang Zeng, Sebastian Hafner, Sebastian Gerard, Eric Brune, Ritu Yadav, Ali Shibli, Heng Fang, Yifang Ban, Maarten Vergauwen, Nicolas Audebert, and Andrea Nascetti · 2024
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Do text-free diffusion models learn discriminative visual representations?
Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou, Jun Ohya, and Abhinav Shrivastava · 2024
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Hir-diff: Unsupervised hyperspectral image restoration via improved diffusion models
Li Pang, Xiangyu Rui, Long Cui, Hongzhong Wang, Deyu Meng, and Xiangyong Cao · 2024
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Weijia Wu, Yuzhong Zhao, Hao Chen, Yuchao Gu, Rui Zhao, Yefei He, Hong Zhou, Mike Zheng Shou, and Chunhua Shen · 2023
Cited alongside, same era.
Denoising diffusion autoencoders are unified self-supervised learners
Weilai Xiang, Hongyu Yang, Di Huang, and Yunhong Wang · 2023
Cited alongside, same era.
Open-vocabulary panoptic segmentation with text-to-image diffusion models
Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello · 2023
Cited alongside, same era.
Diffucd: Unsupervised hyperspectral image change detection with semantic correlation diffusion model
Xiangrong Zhang, Shunli Tian, Guanchun Wang, Huiyu Zhou, and Licheng Jiao · 2023
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Omnisat: Self-supervised modality fusion for earth observation, 2024
Guillaume Astruc, Nicolas Gonthier, Clement Mallet, and Loic Landrieu · 2024
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Diffcut: Catalyzing zero-shot semantic segmentation with diffusion features and recursive normalized cut
Paul Couairon, Mustafa Shukor, Jean-Emmanuel HAUGEARD, Matthieu Cord, and Nicolas THOME · 2024
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Building bridges across spatial and temporal resolutions: Reference-based super-resolution via change priors and conditional diffusion model
Runmin Dong, Shuai Yuan, Bin Luo, Mengxuan Chen, Jinxiao Zhang, Lixian Zhang, Weijia Li, Juepeng Zheng, and Haohuan Fu · 2024
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Diffusion models and representation learning: A survey
Michael Fuest, Pingchuan Ma, Ming Gui, Johannes Schusterbauer, Vincent Tao Hu, and Bjorn Ommer · 2024
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Lds2ae: Local diffusion shared-specific autoencoder for multimodal remote sensing image classification with arbitrary missing modalities
Jiahui Qu, Yuanbo Yang, Wenqian Dong, and Yufei Yang · 2024
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Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification
Neetu Sigger, Quoc-Tuan Vien, Sinh Van Nguyen, Gianluca Tozzi, and Tuan Thanh Nguyen · 2024
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Ssl4eo-l: Datasets and foundation models for landsat imagery
Adam Stewart, Nils Lehmann, Isaac Corley, Yi Wang, Yi-Chia Chang, Nassim Ait Ait Ali Braham, Shradha Sehgal, Caleb Robinson, and Arindam Banerjee · 2024
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Satsynth: Augmenting image-mask pairs through diffusion models for aerial semantic segmentation
Aysim Toker, Marvin Eisenberger, Daniel Cremers, and Laura Leal-Taixé · 2024
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Very high resolution canopy height maps from rgb imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar
Jamie Tolan, Hung-I Yang, Benjamin Nosarzewski, Guillaume Couairon, Huy V Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, et al · 2024
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Artificial intelligence to advance earth observation: A review of models, recent trends, and pathways forward
Devis Tuia, Konrad Schindler, Begüm Demir, Xiao Xiang Zhu, Mrinalini Kochupillai, Sašo Džeroski, Jan N. van Rijn, Holger H. Hoos, Fabio Del Frate, Mihai Datcu, Volker Markl, Bertrand Le Saux, Rochelle Schneider, and Gustau Camps-Valls · 2024
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Gcd-ddpm: A generative change detection model based on difference-feature guided ddpm
Yihan Wen, Xianping Ma, Xiaokang Zhang, and Man-On Pun · 2024
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Neural plasticity-inspired foundation model for observing the Earth crossing modalities
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
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Representation alignment for generation: Training diffusion transformers is easier than you think, 2024
Sihyun Yu, Sangkyung Kwak, Huiwon Jang, Jongheon Jeong, Jonathan Huang, Jinwoo Shin, and Saining Xie · 2024
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Changen2: Multi-temporal remote sensing generative change foundation model
Zhuo Zheng, Stefano Ermon, Dongjun Kim, Liangpei Zhang, and Yanfei Zhong · 2024
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Exploring multi-timestep multi-stage diffusion features for hyperspectral image classification
Jingyi Zhou, Jiamu Sheng, Peng Ye, Jiayuan Fan, Tong He, Bin Wang, and Tao Chen · 2024
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Diffcr: A fast conditional diffusion framework for cloud removal from optical satellite images
Xuechao Zou, Kai Li, Junliang Xing, Yu Zhang, Shiying Wang, Lei Jin, and Pin Tao · 2024
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Cop-gen-beta: Unified generative modelling of copernicus imagery thumbnails
Miguel Espinosa, Valerio Marsocci, Yuru Jia, Elliot Crowley, and Mikolaj Czerkawski · 2025
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Chenyang Liu, Keyan Chen, Rui Zhao, Zhengxia Zou, and Zhenwei Shi · 2025
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Semantic guided large scale factor remote sensing image super-resolution with generative diffusion prior
Ce Wang and Wanjie Sun · 2025
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Metaearth: A generative foundation model for global-scale remote sensing image generation
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Three things we need to know about transferring stable diffusion to visual dense prediction tasks
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