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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': Forest |
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'1': River |
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'2': Highway |
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|
'3': AnnualCrop |
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|
'4': SeaLake |
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|
'5': HerbaceousVegetation |
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|
'6': Industrial |
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|
'7': Residential |
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|
'8': PermanentCrop |
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|
'9': Pasture |
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|
splits: |
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|
- name: train |
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|
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: |
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|
- config_name: default |
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|
data_files: |
|
|
- split: train |
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|
path: data/train-* |
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|
- split: validation |
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|
path: data/validation-* |
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|
- split: test |
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path: data/test-* |
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|
license: mit |
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|
size_categories: |
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|
- 10K<n<100K |
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task_categories: |
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|
- image-classification |
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--- |
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# EuroSAT-RGB Dataset |
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### Dataset Description |
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The dataset comprises JPEG composite chips extracted from Sentinel-2 satellite imagery, |
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representing the Red, Green, and Blue bands. |
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It encompasses 27,000 labeled and geo-referenced images across 10 Land Use and Land Cover (LULC) classes |
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## Dataset Structure |
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Splits : Train 80% Validation 10% Test 10% |
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(Kept the original dataset's label distribution consistent in each split) |
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## Citation |
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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 |
