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Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities.
Classification of imbalanced remote-sensing data by neural networks
Lorenzo Bruzzone and Sebastiano B Serpico · 1997
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Long short-term memory
Hochreiter Sepp and Schmidhuber Jürgen · 2012
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Convolutional LSTM network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-Chun Woo · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Semantic segmentation of crop type in Africa: A novel dataset and analysis of deep learning methods
Rose M Rustowicz, Robin Cheong, Lijing Wang, Stefano Ermon, Marshall Burke, and David Lobell · 2019
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So2Sat LCZ42: A benchmark dataset for global local climate zones classification
Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu, Yilei Shi, Jian Kang, Lichao Mou, Hossein Bagheri, Matthias Häberle, Yuansheng Hua, Rong Huang, et al · 2019
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Sen1Floods11: A georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1
Derrick Bonafilia, Beth Tellman, Tyler Anderson, and Erica Issenberg · 2020
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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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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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Lightweight temporal self-attention for classifying satellite images time series
Vivien Sainte Fare Garnot and Loic Landrieu · 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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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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ViViT: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid · 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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MSResNet: Multiscale residual network via self-supervised learning for water-body detection in remote sensing imagery
Bo Dang and Yansheng Li · 2021
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Panoptic segmentation of satellite image time series with convolutional temporal attention networks
Vivien Sainte Fare Garnot and Loic Landrieu · 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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A generalizable and accessible approach to machine learning with global satellite imagery
Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht, and Solomon Hsiang · 2021
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Omni-scale CNNs: A simple and effective kernel size configuration for time series classification
Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, and Jing Jiang · 2021
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Timesen2crop: A million labeled samples dataset of Sentinel 2 image time series for crop-type classification
Giulio Weikmann, Claudia Paris, and Lorenzo Bruzzone · 2021
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TreeSatAI Benchmark Archive: A multi-sensor, multi-label dataset for tree species classification in remote sensing
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit · 2022
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Data2vec: A general framework for self-supervised learning in speech, vision and language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli · 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 Lobell, and Stefano Ermon · 2022
Masked image modeling with denoising contrast
Kun Yi, Yixiao Ge, Xiaotong Li, Shusheng Yang, Dian Li, Jianping Wu, Ying Shan, and Xiaohu Qie · 2023
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https://lightning-flash.readthedocs.io/en/stable/api/generated/flash.core.optimizers.LinearWarmupCosineAnnealingLR.html
Lightning: LinearWarmupCosineAnnealingLR · 2024
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org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html#torch.optim.lr_scheduler.ReduceLROnPlateau
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AI2-S2-NAIP
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Omnisat: Self-supervised modality fusion for earth observation
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Learning representations of satellite images from metadata supervision
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Multi-modal temporal attention models for crop mapping from satellite time series
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Masked autoencoders are scalable vision learners
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Laion-5b: An open large-scale dataset for training next generation image-text models
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CROCO: Cross-modal contrastive learning for localization of Earth observation data
Wei-Hsin Tseng, Hoàng-Ân Lê, Alexandre Boulch, Sébastien Lefèvre, and Dirk Tiede · 2022
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SITS-Former: A pre-trained spatio-spectral-temporal representation model for sentinel-2 time series classification
Yuan Yuan, Lei Lin, Qingshan Liu, Renlong Hang, and Zeng-Guang Zhou · 2022
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Self-supervised learning from images with a joint-embedding predictive architecture
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas · 2023
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Foundational models defining a new era in vision: A survey and outlook
Muhammad Awais, Muzammal Naseer, Salman Khan, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, and Fahad Shahbaz Khan · 2023
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SpectralGPT: Spectral remote sensing foundation model
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Geospatial foundation models for image analysis: Evaluating and enhancing NASA-IBM Prithvi’s domain adaptability
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Remoteclip: A vision language foundation model for remote sensing
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AI foundation models in remote sensing: A survey
Siqi Lu, Junlin Guo, James R Zimmer-Dauphinee, Jordan M Nieusma, Xiao Wang, Parker VanValkenburgh, Steven A Wernke, and Yuankai Huo · 2024
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Cross-sensor self-supervised training and alignment for remote sensing
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PANGAEA: A global and inclusive benchmark for geospatial foundation models
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Rethinking transformers pre-training for multi-spectral satellite imagery
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Planted: A dataset for planted forest identification from multi-satellite time series
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Better, not just more: Data-centric machine learning for Earth observation
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SICKLE: A multi-sensor satellite imagery dataset annotated with multiple key cropping parameters
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SSSL4EO-l: Datasets and foundation models for Landsat imagery
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Prithvi-EO-2.0: A versatile multi-temporal foundation model for earth observation applications
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Cross-scale mae: A tale of multiscale exploitation in remote sensing
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Neural plasticity-inspired foundation model for observing the Earth crossing modalities
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TaxaBind: A unified embedding space for ecological applications
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