---
title: "Scene classification (NWPU-RESISC45, UC Merced, AID)"
description: "Scene classification (NWPU-RESISC45, UC Merced, AID): compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/scene-classification-nwpu-resisc45-uc-merced-aid
markdown_url: https://scholar4ai.com/datasets/scene-classification-nwpu-resisc45-uc-merced-aid.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Scene classification (NWPU-RESISC45, UC Merced, AID): compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends. (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# Scene classification (NWPU-RESISC45, UC Merced, AID)

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/6.md)

Hyperspectral and aerial image collections used to compare classification and detection methods.

- data category: Remote sensing benchmarks
- 0 papers in the last 7 days
- Names searched for: NWPU-RESISC45, RESISC45, UC Merced Land Use, UCMerced, Aerial Image Dataset

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Remote sensing benchmarks datasets: https://scholar4ai.com/datasets/remote-sensing-benchmarks.md

## Tasks, access and usage conditions

Mention counts below measure name matches in tracked papers, not benchmark quality or downloads. A family count can cover multiple datasets; it is not a separate count for each comparison row. Choose a task and version first, preserve evaluation splits, and follow the linked release terms. The site's content license does not replace the datasets' own terms.

*Dataset comparison: tasks, access and usage conditions*

| Dataset / version | Task and evaluation | Access and preparation | Usage conditions |
|---|---|---|---|
| NWPU-RESISC45 | RGB remote-sensing scene classification: 31,500 images, 45 scene classes. Evaluation: Report mean and variability across splits. Randomly held-out images test a different question from geographic transfer. | [Official source / documentation](https://gcheng-nwpu.github.io/) Use the dataset link on the creator’s page; record the train/test proportion, seed and repetitions. | Cite the creator’s benchmark paper and verify the dataset-specific terms; nearby datasets on the same website may use different licenses. |
| UC Merced Land Use | 21-class overhead-image scene classification. Evaluation: Disclose train/test proportions, seeds and augmentation; small sample size makes split variation important. | [Official source / documentation](http://weegee.vision.ucmerced.edu/datasets/landuse.html) Use the university’s original land-use release and preserve class labels; confirm the current availability of its archive. | Check the source-imagery and release terms; this guide does not infer a license from a repackaged copy. |
| AID | Aerial scene classification with 10,000 images in 30 classes. Evaluation: Report accuracy with repeated splits; this is image-level scene classification, not DOTA box detection. | [Official source / documentation](https://captain-whu.github.io/AID/) Use the authors’ download links and record split proportion, seed and image preprocessing. | The release requests the benchmark citation. Verify source-image and dataset permissions before redistribution or commercial reuse. |

Guide updated 2026-11-05.

## Key findings

- From Nov 2023 to Oct 2026, 39 archived papers mentioned Scene classification (NWPU-RESISC45, UC Merced, AID) in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Mar 2025 (4).
- No paper of the tracked journals mentioned Scene classification (NWPU-RESISC45, UC Merced, AID) in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), Scene classification (NWPU-RESISC45, UC Merced, AID) was mentioned in 48 papers in the tracked journals, most of them in AI (47) and operations research (1).
- 11 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were Wuhan University (7), Shanghai Jiao Tong University (4) and Nanyang Technological University (3).
- The journals publishing most of them were IEEE Transactions on Pattern Analysis and Machine Intelligence (8), Neural Computing and Applications (8) and Pattern Recognition (5).
- The most-cited of them is “Vision-Language Models for Vision Tasks: A Survey” (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024), with 998 citations.
- Papers use Scene classification (NWPU-RESISC45, UC Merced, AID) most often with ImageNet (25), CIFAR-10 / CIFAR-100 (12) and MS COCO (8); it has 18 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 15 | 11 | 13 | 39 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 0 | 0 | 0 | 2 | 2 | 1 | 6 | 13 | 11 | 12 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| All categories | 0 | 0 | 0 | 2 | 2 | 1 | 6 | 14 | 11 | 12 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 8 |
| 2 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 8 |
| 3 | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 5 |
| 4 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 3 |
| 5 | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 3 |
| 6 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 3 |
| 7 | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 2 |
| 8 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2 |
| 9 | [Artificial Intelligence](https://scholar4ai.com/journals/artificial-intelligence.md) | 1 |
| 10 | [Pattern Recognition Letters](https://scholar4ai.com/journals/pattern-recognition-letters.md) | 1 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Wuhan University](https://scholar4ai.com/institutions/wuhan-university.md) | 7 |
| 2 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 4 |
| 3 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 3 |
| 4 | Aberystwyth University | 2 |
| 5 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 2 |
| 6 | [Huazhong University of Science and Technology](https://scholar4ai.com/institutions/huazhong-university-of-science-and-technology.md) | 2 |
| 7 | [Lancaster University](https://scholar4ai.com/institutions/lancaster-university.md) | 2 |
| 8 | RIKEN | 2 |
| 9 | [Southeast University](https://scholar4ai.com/institutions/southeast-university.md) | 2 |
| 10 | [The University of Tokyo](https://scholar4ai.com/institutions/the-university-of-tokyo.md) | 2 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 21 |
| 2 | United States | 7 |
| 3 | Singapore | 4 |
| 4 | United Kingdom | 3 |
| 5 | Brazil | 2 |
| 6 | Egypt | 2 |
| 7 | Spain | 2 |
| 8 | France | 2 |
| 9 | Italy | 2 |
| 10 | Japan | 2 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [computational efficiency](https://scholar4ai.com/keywords/computational-efficiency.md) | 5 |
| 2 | remote sensing image classification | 5 |
| 3 | remote sensing images | 5 |
| 4 | [convolutional neural networks](https://scholar4ai.com/keywords/convolutional-neural-networks.md) | 4 |
| 5 | [few-shot learning](https://scholar4ai.com/keywords/few-shot-learning.md) | 4 |
| 6 | [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) | 3 |
| 7 | [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 3 |
| 8 | model compression | 3 |
| 9 | parameter-efficient fine-tuning | 3 |
| 10 | [vision-language models](https://scholar4ai.com/keywords/vision-language-models.md) | 3 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 998 | 30.9 | [Vision-Language Models for Vision Tasks: A Survey](https://doi.org/10.1109/tpami.2024.3369699) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-02-26 | Jingyi Zhang, Jiaxing Huang, Sheng Jin et al. |
| 2 | 271 | 10.4 | [A comprehensive review of model compression techniques in machine learning](https://doi.org/10.1007/s10489-024-05747-w) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 2024-09-02 | Pierre V. Dantas, Waldir Sabino da Silva, Lucas Carvalho Cordeiro et al. |
| 3 | 224 | 5.4 | [Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis](https://doi.org/10.1186/s40537-023-00772-x) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 2023-06-02 | Adekanmi Adeyinka Adegun, Serestina Viriri, Jules‐Raymond Tapamo |
| 4 | 195 | 2.6 | [C-CNN: Contourlet Convolutional Neural Networks](https://doi.org/10.1109/tnnls.2020.3007412) | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 2020-07-21 | Mengkun Liu, Licheng Jiao, Xu Liu et al. |
| 5 | 182 | 2.7 | [Deep Feature Aggregation Framework Driven by Graph Convolutional Network for Scene Classification in Remote Sensing](https://doi.org/10.1109/tnnls.2021.3071369) | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 2021-04-15 | Kejie Xu, Hong Bing Huang, Peifang Deng et al. |

## Used together with

Papers use Scene classification (NWPU-RESISC45, UC Merced, AID) most often with ImageNet (25), CIFAR-10 / CIFAR-100 (12) and MS COCO (8); it has 18 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.

*What papers use Scene classification (NWPU-RESISC45, UC Merced, AID) together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 25 | 10.6 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 12 | 8.6 |
| [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | Dataset | 8 | 8.6 |
| [SVHN](https://scholar4ai.com/datasets/svhn.md) | Dataset | 8 | 38.1 |
| [Sentinel / Copernicus](https://scholar4ai.com/datasets/sentinel-copernicus.md) | Dataset | 7 | 25.3 |
| [Caltech-101 / 256](https://scholar4ai.com/datasets/caltech-101-256.md) | Dataset | 6 | 40.4 |
| [Kaggle](https://scholar4ai.com/datasets/kaggle.md) | Dataset | 6 | 10.4 |
| [DOTA / xView](https://scholar4ai.com/datasets/dota-xview.md) | Dataset | 5 | 81.7 |
| [Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers)](https://scholar4ai.com/datasets/fine-grained-benchmarks-cub-200-stanford-cars-oxford-flowers.md) | Dataset | 5 | 21.8 |
| [Landsat](https://scholar4ai.com/datasets/landsat.md) | Dataset | 5 | 26.5 |
| [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md) | Dataset | 5 | 4.5 |
| [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 4 | 8.5 |

The whole network: https://scholar4ai.com/code/network.md

## Cite this page

Scholar4AI. (2026). Scene classification (NWPU-RESISC45, UC Merced, AID). Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/scene-classification-nwpu-resisc45-uc-merced-aid

```bibtex
@misc{scholar4ai-datasets-scene-classification-nwpu-resisc45-uc-merced-aid,
  author = {{Scholar4AI}},
  title = {Scene classification (NWPU-RESISC45, UC Merced, AID)},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/scene-classification-nwpu-resisc45-uc-merced-aid}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).