---
title: "DOTA / xView: tasks, downloads and usage conditions"
description: "DOTA / xView: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/dota-xview
markdown_url: https://scholar4ai.com/datasets/dota-xview.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "DOTA / xView: 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)
---

# DOTA / xView

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/2.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: DOTA dataset, DOTA-v1, xView, DOTA

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 |
|---|---|---|---|
| DOTA 1.0 / 1.5 / 2.0 | Object detection in aerial images, including oriented bounding boxes. Evaluation: State oriented/horizontal-box task, IoU threshold, class set and patch merging; keep patches from the same scene in one split. | [Official source / documentation](https://captain-whu.github.io/DOTA/) Choose one release and its annotations/devkit; retain original large-image IDs when making patches. | Follow DOTA’s release agreement and source-image conditions; evaluation-code licensing does not itself license every image. |
| xView 2018 | Overhead-image object detection with fine-grained object categories. Evaluation: Use the official class mapping and evaluation protocol; compare like-for-like box definitions and image resolutions. | [Official source / documentation](https://xviewdataset.org/) Use the official challenge registration/download route and its annotations; confirm present account access. | Accept the official dataset/challenge terms, including source-imagery restrictions; do not assume that DOTA terms apply. |

Guide updated 2026-11-05.

## Key findings

- From Nov 2023 to Oct 2026, 42 archived papers mentioned DOTA / xView in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Jan 2025 (4).
- No paper of the tracked journals mentioned DOTA / xView in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), DOTA / xView was mentioned in 61 papers in the tracked journals, most of them in AI (61).
- 16 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were University of Chinese Academy of Sciences (5), Wuhan University (5) and Chinese Academy of Sciences (3).
- The journals publishing most of them were IEEE Transactions on Pattern Analysis and Machine Intelligence (9), Pattern Recognition (7) and Expert Systems with Applications (5).
- The most-cited of them is “Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges” (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021), with 681 citations.
- Papers use DOTA / xView most often with ImageNet (14), MS COCO (12) and Kaggle (5); it has 11 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 |
|---|---:|---:|---:|---:|
|  | 11 | 17 | 14 | 42 |

## 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 | 1 | 3 | 8 | 8 | 13 | 16 | 12 |
| All categories | 0 | 0 | 0 | 1 | 3 | 8 | 8 | 13 | 16 | 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) | 9 |
| 2 | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 7 |
| 3 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 5 |
| 4 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 4 |
| 5 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 4 |
| 6 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 3 |
| 7 | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 3 |
| 8 | [Image and Vision Computing](https://scholar4ai.com/journals/image-and-vision-computing.md) | 2 |
| 9 | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 2 |
| 10 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 5 |
| 2 | [Wuhan University](https://scholar4ai.com/institutions/wuhan-university.md) | 5 |
| 3 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 3 |
| 4 | [Nankai University](https://scholar4ai.com/institutions/nankai-university.md) | 3 |
| 5 | [Northwestern Polytechnical University](https://scholar4ai.com/institutions/northwestern-polytechnical-university.md) | 3 |
| 6 | State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing | 3 |
| 7 | Chang'an University | 2 |
| 8 | [Harbin Institute of Technology](https://scholar4ai.com/institutions/harbin-institute-of-technology.md) | 2 |
| 9 | [Huawei Technologies (China)](https://scholar4ai.com/companies/huawei-technologies-china.md) | 2 |
| 10 | [Huazhong University of Science and Technology](https://scholar4ai.com/institutions/huazhong-university-of-science-and-technology.md) | 2 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 36 |
| 2 | United States | 4 |
| 3 | India | 3 |
| 4 | Singapore | 2 |
| 5 | United Arab Emirates | 1 |
| 6 | Australia | 1 |
| 7 | Bangladesh | 1 |
| 8 | Egypt | 1 |
| 9 | Spain | 1 |
| 10 | France | 1 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | DOTA | 14 |
| 2 | [object detection](https://scholar4ai.com/keywords/object-detection.md) | 11 |
| 3 | oriented object detection | 7 |
| 4 | remote sensing images | 7 |
| 5 | aerial images | 6 |
| 6 | remote sensing object detection | 6 |
| 7 | small object detection | 6 |
| 8 | HRSC2016 | 5 |
| 9 | [semantic segmentation](https://scholar4ai.com/keywords/semantic-segmentation.md) | 5 |
| 10 | DOTA dataset | 4 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 681 | 11.2 | [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://doi.org/10.1109/tpami.2021.3117983) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2021-10-06 | Jian Ding, Nan Xue, Gui-Song Xia et al. |
| 2 | 372 | 6.8 | [SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing](https://doi.org/10.1109/tpami.2022.3166956) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2022-04-12 | Xue Yang, Junchi Yan, Wenlong Liao et al. |
| 3 | 245 | 9.1 | [A comprehensive survey of deep learning-based lightweight object detection models for edge devices](https://doi.org/10.1007/s10462-024-10877-1) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2024-08-10 | Payal Mittal |
| 4 | 200 | 8.0 | [LSKNet: A Foundation Lightweight Backbone for Remote Sensing](https://doi.org/10.1007/s11263-024-02247-9) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 2024-10-07 | Yuxuan Li, Xiang Li, Yimain Dai et al. |
| 5 | 173 | 3.0 | [mSODANet: A network for multi-scale object detection in aerial images using hierarchical dilated convolutions](https://doi.org/10.1016/j.patcog.2022.108548) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 2022-01-23 | Vishnu Chalavadi, Jeripothula Prudviraj, Rajeshreddy Datla et al. |

## Used together with

Papers use DOTA / xView most often with ImageNet (14), MS COCO (12) and Kaggle (5); it has 11 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.

*What papers use DOTA / xView together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 14 | 4.7 |
| [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | Dataset | 12 | 10.1 |
| [Kaggle](https://scholar4ai.com/datasets/kaggle.md) | Dataset | 5 | 6.8 |
| [KITTI](https://scholar4ai.com/datasets/kitti.md) | Dataset | 5 | 8.7 |
| [PASCAL VOC](https://scholar4ai.com/datasets/pascal-voc.md) | Dataset | 5 | 11.8 |
| [Scene classification (NWPU-RESISC45, UC Merced, AID)](https://scholar4ai.com/datasets/scene-classification-nwpu-resisc45-uc-merced-aid.md) | Dataset | 5 | 81.7 |
| [ADE20K](https://scholar4ai.com/datasets/ade20k.md) | Dataset | 3 | 16.7 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 3 | 1.7 |
| [nuScenes / Waymo Open / Argoverse](https://scholar4ai.com/datasets/nuscenes-waymo-open-argoverse.md) | Dataset | 3 | 8.0 |
| [Skeleton and pose (NTU RGB+D, Human3.6M)](https://scholar4ai.com/datasets/skeleton-and-pose-ntu-rgb-d-human3-6m.md) | Dataset | 3 | 14.7 |
| [ultralytics/ultralytics](https://scholar4ai.com/code/github-ultralytics-ultralytics.md) | Code repository | 3 | 12.7 |

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

## Cite this page

Scholar4AI. (2026). DOTA / xView. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/dota-xview

```bibtex
@misc{scholar4ai-datasets-dota-xview,
  author = {{Scholar4AI}},
  title = {DOTA / xView},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/dota-xview}},
  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/).