RSI-CB256 / README.md
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metadata
dataset_info:
  features:
    - name: label_1
      dtype:
        class_label:
          names:
            '0': transportation
            '1': other objects
            '2': woodland
            '3': water area
            '4': other land
            '5': cultivated land
            '6': construction land
    - name: label_2
      dtype:
        class_label:
          names:
            '0': parking lot
            '1': avenue
            '2': highway
            '3': bridge
            '4': marina
            '5': crossroads
            '6': airport runway
            '7': pipeline
            '8': town
            '9': airplane
            '10': forest
            '11': mangrove
            '12': artificial grassland
            '13': river protection forest
            '14': shrubwood
            '15': sapling
            '16': sparse forest
            '17': lakeshore
            '18': river
            '19': stream
            '20': coastline
            '21': hirst
            '22': dam
            '23': sea
            '24': snow mountain
            '25': sandbeach
            '26': mountain
            '27': desert
            '28': dry farm
            '29': green farmland
            '30': bare land
            '31': city building
            '32': residents
            '33': container
            '34': storage room
    - name: image
      dtype: image
  splits:
    - name: train
      num_bytes: 4901667781.625
      num_examples: 24747
  download_size: 4198991130
  dataset_size: 4901667781.625
license: other
task_categories:
  - image-classification
  - zero-shot-image-classification

Dataset Card for "RSI-CB256"

Dataset Description

Licensing Information

For academic purposes.

Citation Information

Exploring Models and Data for Remote Sensing Image Caption Generation

@article{lu2017exploring,
  title        = {Exploring Models and Data for Remote Sensing Image Caption Generation},
  author       = {Lu, Xiaoqiang and Wang, Binqiang and Zheng, Xiangtao and Li, Xuelong},
  journal      = {IEEE Transactions on Geoscience and Remote Sensing},
  volume       = 56,
  number       = 4,
  pages        = {2183--2195},
  doi          = {10.1109/TGRS.2017.2776321},
  year={2018}
}