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From optical sensors to microwave radars, leveraging the complementary strengths of remote sensing (RS) sensors is crucial for achieving dense spatio-temporal monitoring of our planet.
Satellite image classification via two-layer sparse coding with biased image representation
Dengxin Dai and Wen Yang · 2010
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Bag-of-visual-words and spatial extensions for land-use classification
Yi Yang and Shawn Newsam · 2010
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Multimodal classification of remote sensing images: A review and future directions
Luis Gómez-Chova, Devis Tuia, Gabriele Moser, and Gustau Camps-Valls · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Big data for remote sensing: Challenges and opportunities
Mingmin Chi, Antonio Plaza, Jón Atli Benediktsson, Zhongyi Sun, Jinsheng Shen, and Yangyong Zhu · 2016
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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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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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Lightweight temporal self-attention for classifying satellite images time series
Vivien Sainte Fare Garnot and Loic Landrieu · 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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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, Jakob Uszkoreit, and Neil Houlsby · 2021
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Self-supervised pre-training enhances change detection in Sentinel-2 images
Marrit Leenstra, Diego Marcos, Francesca Bovolo, and Devis Tuia · 2021
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Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data
Oscar Mañas, Alexandre Lacoste, Xavier Giró-i Nieto, David Vazquez, and Pau Rodríguez · 2021
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BigEarthNet-MM: A large scale multi-modal multi-label benchmark archive for remote sensing image classification and retrieval
Gencer Sumbul, Arne de Wall, Tristan Kreuziger, Filipe Marcelino, Hugo Costa, Pedro Benevides, Mário Caetano, Begüm Demir, and Volker Markl · 2021
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Towards a collective agenda on AI for earth science data analysis
Devis Tuia, Ribana Roscher, Jan Dirk Wegner, Nathan Jacobs, Xiao Xiang Zhu, and Gustua Camps-Valls · 2021
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The multi-temporal urban development spacenet dataset
Adam Van Etten, Daniel Hogan, Jesus Martinez Manso, Jacob Shermeyer, Nicholas Weir, and Ryan Lewis · 2021
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Multimae: Multi-modal multi-task masked autoencoders
Roman Bachmann, David Mizrahi, Andrei Atanov, and Amir Zamir · 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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Change-aware sampling and contrastive learning for satellite images
Utkarsh Mall, Bharath Hariharan, and Kavita Bala · 2023
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Towards geospatial foundation models via continual pretraining
Matías Mendieta, Boran Han, Xingjian Shi, Yi Zhu, and Chen Chen · 2023
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4M: Massively multimodal masked modeling
David Mizrahi, Roman Bachmann, Oguzhan Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, and Amir Zamir · 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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Cross-scale MAE: A tale of multiscale exploitation in remote sensing
Maofeng Tang, Andrei Liviu Cozma, Konstantinos Georgiou, and Hairong Qi · 2023
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MCMAE: 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
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Exploring plain vision transformer backbones for object detection
Yanghao Li, Hanzi Mao, Ross Girshick, and Kaiming He · 2022
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Contrastive self-supervised data fusion for satellite imagery
Linus Scheibenreif, Michael Mommert, and Damian Borth · 2022
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A novel self-supervised cross-modal image retrieval method in remote sensing
Gencer Sumbul, Markus Müller, and Begüm Demir · 2022
Cited alongside, same era.
DynamicEarthNet: Daily multi-spectral satellite dataset for semantic change segmentation
Aysim Toker, Lukas Kondmann, Mark Weber, Marvin Eisenberger, Camero Andres, Jingliang Hu, Ariadna Hoderlein, Caglar Senaras, et al · 2022
Cited alongside, same era.
Self-supervised learning in remote sensing: A review
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Lichao Mou, and Xiao Xiang Zhu · 2022
Cited alongside, same era.
SkySense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery
Xin Guo, Jiangwei Lao, Bo Dang, Yingying Zhang, Lei Yu, Lixiang Ru, Liheng Zhong, Ziyuan Huang, et al · 2024
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Bridging remote sensors with multisensor geospatial foundation models
Boran Han, Shuai Zhang, Xingjian Shi, and Markus Reichstein · 2024
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SpectralGPT: Spectral remote sensing foundation model
Danfeng Hong, Bing Zhang, Xuyang Li, Yuxuan Li, Chenyu Li, Jing Yao, Naoto Yokoya, Hao Li, et al · 2024
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S2mae: A spatial-spectral pretraining foundation model for spectral remote sensing data
Xuyang Li, Danfeng Hong, and Jocelyn Chanussot · 2024
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PANGAEA: A global and inclusive benchmark for geospatial foundation models
Valerio Marsocci, Yuru Jia, Georges Le Bellier, David Kerekes, Liang Zeng, Sebastian Hafner, Sebastian Gerard, Eric Brune, et al · 2024
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Mmearth: Exploring multi-modal pretext tasks for geospatial representation learning
Vishal Nedungadi, Ankit Kariryaa, Stefan Oehmcke, Serge Belongie, Christian Igel, and Nico Lang · 2024
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Rethinking transformers pre-training for multi-spectral satellite imagery
Mubashir Noman, Muzammal Naseer, Hisham Cholakkal, Rao Muhammad Anwar, Salman Khan, and Fahad Shahbaz Khan · 2024
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SenPa-MAE: Sensor parameter aware masked autoencoder for multi-satellite self-supervised pretraining
Jonathan Prexl and Michael Schmitt · 2024
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SICKLE: A multi-sensor satellite imagery dataset annotated with multiple key cropping parameters
Depanshu Sani, Sandeep Mahato, Sourabh Saini, Harsh Kumar Agarwal, Charu Chandra Devshali, Saket Anand, Gaurav Arora, and Thiagarajan Jayaraman · 2024
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Neural plasticity-inspired multimodal foundation model for earth observation
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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TerraMind: Large-scale generative multimodality for earth observation
Johannes Jakubik, Felix Yang, Benedikt Blumenstiel, Erik Scheurer, Rocco Sedona, Stefano Maurogiovanni, Jente Bosmans, Nikolaos Dionelis, et al · 2025
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