mlsanityData to 7 Oct 2026

Geographical Knowledge-driven Representation Learning for Remote Sensing Images

arXiv 2107.05276 ↗First posted 12 Jul 2021cs.CVcited by 37

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Abstract (arXiv)

The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images. However, due to human and material resource constraints, the vast majority of remote sensing images remain unlabeled. As a result, it cannot be applied to currently available deep learning methods. To fully utilize the remaining unlabeled images, we propose a Geographical Knowledge-driven Representation learning method for remote sensing images (GeoKR), improving network performance and reduce the demand for annotated data. The global land cover products and geographical location associated with each remote sensing image are regarded as geographical knowledge to provide supervision for representation learning and network pre-training. An efficient pre-training framework is proposed to eliminate the supervision noises caused by imaging times and resolutions difference between remote sensing images and geographical knowledge. A large scale pre-training dataset Levir-KR is proposed to support network pre-training. It contains 1,431,950 remote sensing images from Gaofen series satellites with various resolutions. Experimental results demonstrate that our proposed method outperforms ImageNet pre-training and self-supervised representation learning methods and significantly reduces the burden of data annotation on downstream tasks such as scene classification, semantic segmentation, object detection, and cloud / snow detection. It demonstrates that our proposed method can be used as a novel paradigm for pre-training neural networks. Codes will be available on https://github.com/flyakon/Geographical-Knowledge-driven-Representaion-Learning.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 41 of its 77 references that are in the map. First posted on arXiv 12 Jul 2021.

Builds on

41 of 77 references.

References · 41

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 9; 2024: 7; 2025: 8; 2026: 5.

37 papers.

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  1. 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 402
  2. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensingcited by 357
  3. 2023RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Modelcited by 180
  4. 2022Advancing Plain Vision Transformer Towards Remote Sensing Foundation Modelcited by 149
  5. 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
  6. 2022An Empirical Study of Remote Sensing Pretrainingcited by 105
  7. 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
  8. 2023CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representationscited by 67
  9. 2024MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretrainingcited by 66
  10. 2023CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understandingcited by 48
  11. 2025TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysiscited by 35
  12. 2024Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretrainingcited by 27
25 more
  1. 2024PhilEO Bench: Evaluating Geo-Spatial Foundation Modelscited by 25
  2. 2022Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentationcited by 22
  3. 2022Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domaincited by 22
  4. 2023Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advancescited by 20
  5. 2022DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detectioncited by 19
  6. 2025A Survey on Remote Sensing Foundation Models: From Vision to Multimodalitycited by 18
  7. 2025RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Modelscited by 17
  8. 2022Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detectioncited by 16
  9. 2023GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinatescited by 12
  10. 2026Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodalitycited by 12
  11. 2024RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Modelcited by 11
  12. 2024A2-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencodercited by 10
  13. 2024Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagerycited by 10
  14. 2023CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understandingcited by 9
  15. 2025AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Agriculture Mappingcited by 8
  16. 2023C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensingcited by 6
  17. 2025REJEPA: A Novel Joint-Embedding Predictive Architecture for Efficient Remote Sensing Image Retrievalcited by 4
  18. 2026MMEarth-Bench: Global Model Adaptation via Multimodal Test-Time Trainingcited by 4
  19. 2025S5: Scalable Semi-Supervised Semantic Segmentation in Remote Sensingcited by 3
  20. 2024MarsSeg: Mars Surface Semantic Segmentation with Multi-level Extractor and Connectorcited by 2
  21. 2025DynamicVis: Dynamic Visual Perception for Efficient Remote Sensing Foundation Modelscited by 1
  22. 2025A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Imagescited by 1
  23. 2026Remote Sensing Image Classification Using Deep Ensemble Learningcited by 1
  24. 2026Subimage Overlap Prediction: Task-Aligned Self-Supervised Pretraining For Semantic Segmentation In Remote Sensing Imagerycited by 0
  25. 2026Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagerycited by 0