Datasets:

cm93
/

eurosat

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Forest
            '1': River
            '2': Highway
            '3': AnnualCrop
            '4': SeaLake
            '5': HerbaceousVegetation
            '6': Industrial
            '7': Residential
            '8': PermanentCrop
            '9': Pasture
  splits:
    - name: train
      num_bytes: 73654547.8
      num_examples: 21600
    - name: validation
      num_bytes: 9213645.6
      num_examples: 2700
    - name: test
      num_bytes: 9201991.7
      num_examples: 2700
  download_size: 91902630
  dataset_size: 92070185.1
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: mit
size_categories:
  - 10K<n<100K
task_categories:
  - image-classification

EuroSAT-RGB Dataset

Dataset Description

The dataset comprises JPEG composite chips extracted from Sentinel-2 satellite imagery, representing the Red, Green, and Blue bands. It encompasses 27,000 labeled and geo-referenced images across 10 Land Use and Land Cover (LULC) classes

Dataset Structure

Splits : Train 80% Validation 10% Test 10% (Kept the original dataset's label distribution consistent in each split)

Citation

Helber, P., Bischke, B., Dengel, A., & Borth, D. (2018). EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification [Data set]. In EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (Vol. 12, Number 7, pp. 2217–2226). Zenodo. https://doi.org/10.5281/zenodo.7711810