| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - image-classification |
| language: |
| - en |
| tags: |
| - aerial |
| size_categories: |
| - 10K<n<100K |
| --- |
| # AID Dataset (Aerial Image Dataset) |
|
|
| ## Overview |
|
|
| The Aerial Image Dataset (AID) is a large-scale benchmark for aerial scene classification. The dataset contains 10,000 RGB aerial images collected from Google Earth imagery and annotated into 30 semantic scene categories. It was created to advance research in remote sensing and aerial image understanding. |
|
|
| ## Statistics |
|
|
| | Property | Value | |
| |----------|--------:| |
| | Images | 10,000 | |
| | Classes | 30 | |
| | Resolution | 600 × 600 pixels | |
| | Source | Google Earth Imagery |【 |
|
|
| ## Classes |
|
|
| - Airport |
| - Bare Land |
| - Baseball Field |
| - Beach |
| - Bridge |
| - Center |
| - Church |
| - Commercial |
| - Dense Residential |
| - Desert |
| - Farmland |
| - Forest |
| - Industrial |
| - Meadow |
| - Medium Residential |
| - Mountain |
| - Park |
| - Parking |
| - Playground |
| - Pond |
| - Port |
| - Railway Station |
| - Resort |
| - River |
| - School |
| - Sparse Residential |
| - Square |
| - Stadium |
| - Storage Tanks |
| - Viaduct |
|
|
|
|
| ## Dataset Characteristics |
|
|
| - High intra-class variability |
| - Images collected from different countries and regions |
| - Multiple spatial resolutions |
| - Variations in illumination, viewpoint, orientation, and scale |
| - Expert-annotated labels |
|
|
|
|
| ## Applications |
|
|
| - Aerial Scene Classification |
| - Remote Sensing |
| - Land-Use Classification |
| - Earth Observation |
| - Deep Learning Research |
| - Computer Vision Benchmarking |
|
|
| ## Source |
|
|
| Official dataset page: |
|
|
| https://captain-whu.github.io/AID/ |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{xia2017aid, |
| title={AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification}, |
| author={Xia, Gui-Song and Hu, Jingwen and Hu, Fan and Shi, Baoguang and Bai, Xiang and Zhong, Yanfei and Zhang, Liangpei and Lu, Xiaoqiang}, |
| journal={IEEE Transactions on Geoscience and Remote Sensing}, |
| volume={55}, |
| number={7}, |
| pages={3965--3981}, |
| year={2017}, |
| publisher={IEEE} |
| } |
| ``` |
