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Aerial Image Dataset (AID)



The Aerial Image Dataset (AID) is a scene classification dataset consisting of 10,000 RGB images, each with a resolution of 600x600 pixels. These images have been extracted using Google Earth and cover various scenes from regions and countries around the world. AID comprises 30 different scene categories, with several hundred images per class.

The new dataset is made up of the following 30 aerial scene types: 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 and viaduct. All the images are labelled by the specialists in the field of remote sensing image interpretation, and some samples of each class are shown in Fig.1. In all, the AID dataset has a number of 10000 images within 30 classes.

The dataset is designed for the evaluation of aerial scene classification algorithms and models. It is considered a relatively easy dataset, with approximately 90% accuracy achievable using a VGG-16 architecture.



└──  data
     β”œβ”€β”€ Airport
     β”‚   β”œβ”€β”€ airport_1.png
     β”‚   β”œβ”€β”€ airport_2.png
     β”‚   β”œβ”€β”€ ...
     β”‚   └── airport_360.png
     β”œβ”€β”€ BareLand
     β”‚   β”œβ”€β”€ bareland_1.png
     β”‚   β”œβ”€β”€ ...
     β”‚   └── bareland_310.png
     β”œβ”€β”€ ...
     └── Viaduct


  • Total Number of Images: 10,000
  • Image Resolution: 600x600 pixels
  • Scene Categories: 30
  • Dataset Size: 2.6GB


If you use the Aerial Image Dataset (AID) in your research, please consider citing the following publication:

  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}

Paper with code:

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