mlsanityData to 7 Oct 2026

Geography-Aware Self-Supervised Learning

arXiv 2011.09980 ↗First posted 19 Nov 2020cs.CVcited by 190

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Abstract (arXiv)

Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks. In this paper, we explore their application to geo-located datasets, e.g. remote sensing, where unlabeled data is often abundant but labeled data is scarce. We first show that due to their different characteristics, a non-trivial gap persists between contrastive and supervised learning on standard benchmarks. To close the gap, we propose novel training methods that exploit the spatio-temporal structure of remote sensing data. We leverage spatially aligned images over time to construct temporal positive pairs in contrastive learning and geo-location to design pre-text tasks. Our experiments show that our proposed method closes the gap between contrastive and supervised learning on image classification, object detection and semantic segmentation for remote sensing. Moreover, we demonstrate that the proposed method can also be applied to geo-tagged ImageNet images, improving downstream performance on various tasks. Project Webpage can be found at this link geography-aware-ssl.github.io.

Key facts. Cited by 190 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Nov 2020.

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Citing papers per year: 2021: 16; 2022: 29; 2023: 43; 2024: 40; 2025: 39; 2026: 23.

190 papers.

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  1. 2022Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distributioncited by 722
  2. 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 402
  3. 2022Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learningcited by 302
  4. 2021Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Datacited by 214
  5. 2023SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagerycited by 202
  6. 2023CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoderscited by 167
  7. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensingcited by 150
  8. 2022Advancing Plain Vision Transformer Towards Remote Sensing Foundation Modelcited by 149
  9. 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
  10. 2023DiffusionSat: A Generative Foundation Model for Satellite Imagerycited by 130
  11. 2023SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensingcited by 123
  12. 2023Towards Geospatial Foundation Models via Continual Pretrainingcited by 109
38 more
  1. 2022An Empirical Study of Remote Sensing Pretrainingcited by 105
  2. 2023Lightweight, Pre-trained Transformers for Remote Sensing Timeseriescited by 102
  3. 2024Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observationcited by 97
  4. 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
  5. 2021When Does Contrastive Visual Representation Learning Work?cited by 92
  6. 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligencecited by 88
  7. 2022SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observationcited by 82
  8. 2021Self-Supervised Pretraining Improves Self-Supervised Pretrainingcited by 81
  9. 2024Mission Critical -- Satellite Data is a Distinct Modality in Machine Learningcited by 76
  10. 2024MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learningcited by 76
  11. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
  12. 2024AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalitiescited by 73
  13. 2024HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Modelcited by 72
  14. 2024TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Datacited by 69
  15. 2023CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representationscited by 67
  16. 2024MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretrainingcited by 66
  17. 2024OmniSat: Self-Supervised Modality Fusion for Earth Observationcited by 63
  18. 2023A Billion-scale Foundation Model for Remote Sensing Imagescited by 62
  19. 2024PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Modelscited by 62
  20. 2021Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Codingcited by 58
  21. 2025Towards a Unified Copernicus Foundation Model for Earth Visioncited by 52
  22. 2021PASS: An ImageNet replacement for self-supervised pretraining without humanscited by 51
  23. 2024Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Communitycited by 50
  24. 2021Self-Supervised Material and Texture Representation Learning for Remote Sensing Taskscited by 49
  25. 2023SSL4EO-L: Datasets and Foundation Models for Landsat Imagerycited by 49
  26. 2023CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understandingcited by 48
  27. 2024Bridging Remote Sensors with Multisensor Geospatial Foundation Modelscited by 46
  28. 2024SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentationcited by 41
  29. 2022Self-supervised Vision Transformers for Joint SAR-optical Representation Learningcited by 40
  30. 2023Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictionscited by 38
  31. 2021Geographical Knowledge-driven Representation Learning for Remote Sensing Imagescited by 37
  32. 2024Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Modelcited by 37
  33. 2025TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysiscited by 35
  34. 2022SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Modelscited by 34
  35. 2024On the Foundations of Earth and Climate Foundation Modelscited by 31
  36. 2024TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learningcited by 29
  37. 2023USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagerycited by 28
  38. 2023Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matterscited by 27

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