mlsanityData to 7 Oct 2026

Remote Sensing Image Scene Classification: Benchmark and State of the Art

arXiv 1703.00121 ↗First posted 1 Mar 2017cs.CVcited by 791

Not on the map. Why?

Cited by 107 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.

Key facts. Cited by 791 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 47; 2022: 76; 2023: 122; 2024: 167; 2025: 228; 2026: 151.

791 papers.

Sort:

By area:

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,534
  3. 2022Visual Prompt Tuningcited by 1,674
  4. 2022Reproducible scaling laws for contrastive language-image learningcited by 1,163
  5. 2022Editing Models with Task Arithmeticcited by 1,076
  6. 2023Vision-Language Models for Vision Tasks: A Surveycited by 834
  7. 2023TIES-Merging: Resolving Interference When Merging Modelscited by 795
  8. 2023EVA-CLIP: Improved Training Techniques for CLIP at Scalecited by 786
  9. 2021Scaling Vision with Sparse Mixture of Expertscited by 740
  10. 2023Scaling Vision Transformers to 22 Billion Parameterscited by 690
  11. 2023DataComp: In search of the next generation of multimodal datasetscited by 635
  12. 2021How to train your ViT? Data, Augmentation, and Regularization in Vision Transformerscited by 562
38 more
  1. 2021SLIP: Self-supervision meets Language-Image Pre-trainingcited by 434
  2. 2023EVA-02: A Visual Representation for Neon Genesiscited by 377
  3. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensingcited by 357
  4. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensingcited by 320
  5. 2025Perception Encoder: The best visual embeddings are not at the output of the networkcited by 315
  6. 2022Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classificationcited by 310
  7. 2022Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learningcited by 302
  8. 2023VeRA: Vector-based Random Matrix Adaptationcited by 294
  9. 2022Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuningcited by 292
  10. 2022Exploring Visual Prompts for Adapting Large-Scale Modelscited by 271
  11. 2022What does a platypus look like? Generating customized prompts for zero-shot image classificationcited by 263
  12. 2023AdaMerging: Adaptive Model Merging for Multi-Task Learningcited by 248
  13. 2023Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Modelscited by 218
  14. 2023Improving CLIP Training with Language Rewritescited by 216
  15. 2023SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagerycited by 202
  16. 2022Patching open-vocabulary models by interpolating weightscited by 187
  17. 2023Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Needcited by 183
  18. 2021Combined Scaling for Zero-shot Transfer Learningcited by 182
  19. 2024EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domaincited by 181
  20. 2022SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understandingcited by 164
  21. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensingcited by 150
  22. 2022Advancing Plain Vision Transformer Towards Remote Sensing Foundation Modelcited by 149
  23. 2024Task Singular Vectors: Reducing Task Interference in Model Mergingcited by 143
  24. 2022ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Modelscited by 141
  25. 2021The Benchmark Lotterycited by 133
  26. 2022Neural Prompt Searchcited by 133
  27. 2022FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformercited by 133
  28. 2024LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Modelcited by 133
  29. 2023Hyperbolic Image-Text Representationscited by 132
  30. 2023Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Maskscited by 130
  31. 2024Localizing Task Information for Improved Model Merging and Compressioncited by 130
  32. 2023SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensingcited by 123
  33. 2022Convolutional Bypasses Are Better Vision Transformer Adapterscited by 120
  34. 2024Model merging with SVD to tie the Knotscited by 118
  35. 2022Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questionscited by 116
  36. 2024Twin-Merging: Dynamic Integration of Modular Expertise in Model Mergingcited by 114
  37. 2023Towards Geospatial Foundation Models via Continual Pretrainingcited by 109
  38. 2022Chinese CLIP: Contrastive Vision-Language Pretraining in Chinesecited by 108

50 most cited of 791 All 791 (JSON)