resisc45

  • Description:

RESISC45 dataset 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.

  • Homepage: http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html
  • Source code: tfds.image.resisc45.Resisc45
  • Versions:
    • 3.0.0 (default): No release notes.
  • Download size: Unknown size
  • Dataset size: Unknown size
  • Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/manual/resisc45/):
    Dataset can be downloaded from OneDrive: https://1drv.ms/u/s!AmgKYzARBl5ca3HNaHIlzp_IXjs After downloading the rar file, please extract it to the manual_dir.
  • Auto-cached (documentation): No
  • Splits:
Split Examples
'train' 31,500
  • Features:
FeaturesDict({
    'filename': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(256, 256, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=45),
})
@article{Cheng_2017,
   title={Remote Sensing Image Scene Classification: Benchmark and State of the Art},
   volume={105},
   ISSN={1558-2256},
   url={http://dx.doi.org/10.1109/JPROC.2017.2675998},
   DOI={10.1109/jproc.2017.2675998},
   number={10},
   journal={Proceedings of the IEEE},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
   year={2017},
   month={Oct},
   pages={1865-1883}
}