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image
imagewidth (px)
64
64
label
class label
10 classes
1Forest
7Residential
2HerbaceousVegetation
7Residential
1Forest
0AnnualCrop
8River
0AnnualCrop
1Forest
7Residential
4Industrial
8River
5Pasture
4Industrial
9SeaLake
6PermanentCrop
1Forest
4Industrial
1Forest
0AnnualCrop
4Industrial
9SeaLake
1Forest
2HerbaceousVegetation
4Industrial
1Forest
0AnnualCrop
0AnnualCrop
1Forest
1Forest
3Highway
8River
2HerbaceousVegetation
1Forest
6PermanentCrop
1Forest
7Residential
9SeaLake
4Industrial
3Highway
9SeaLake
6PermanentCrop
4Industrial
3Highway
4Industrial
8River
2HerbaceousVegetation
6PermanentCrop
0AnnualCrop
9SeaLake
0AnnualCrop
0AnnualCrop
7Residential
0AnnualCrop
4Industrial
5Pasture
9SeaLake
1Forest
2HerbaceousVegetation
2HerbaceousVegetation
2HerbaceousVegetation
3Highway
8River
8River
5Pasture
0AnnualCrop
2HerbaceousVegetation
2HerbaceousVegetation
7Residential
3Highway
6PermanentCrop
7Residential
5Pasture
2HerbaceousVegetation
1Forest
4Industrial
2HerbaceousVegetation
8River
2HerbaceousVegetation
0AnnualCrop
6PermanentCrop
9SeaLake
1Forest
0AnnualCrop
4Industrial
4Industrial
9SeaLake
2HerbaceousVegetation
1Forest
2HerbaceousVegetation
8River
1Forest
6PermanentCrop
3Highway
4Industrial
3Highway
2HerbaceousVegetation
6PermanentCrop
7Residential
1Forest

EuroSAT Dataset

Overview

This dataset contains satellite images from the EuroSAT datasetThe dataset consists of RGB images with 10 different classes, each representing a distinct type of land use.

Dataset Summary

  • Classes: 10 (e.g., Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial, Pasture, Permanent Crop, Residential, River, Sea/Lake)
  • Number of Images: 27,000+ images split into training and validation sets
  • Image Size: 64x64 pixels, 3 channels (RGB)
  • Data Augmentation: Random flipping, random rotation, and normalization

Usage

This dataset is ideal for training and fine-tuning image classification models, specifically for applications in remote sensing, urban planning, environmental monitoring, and agricultural management.

Key Features

  • Preprocessing: Images were preprocessed using TensorFlow, including resizing, normalizing, and applying augmentation techniques to improve model robustness.
  • Model: Fine-tuned with Vision Transformer (ViT) to leverage attention-based mechanisms for superior performance in image classification tasks.

Dataset Structure

  • Training Set: Approximately 80% of the dataset
  • Validation Set: Approximately 20% of the dataset
  • Format: Images are stored as PNG files with associated labels.

Labels

  • Class labels are encoded as integers and correspond to the following categories:
    1. Annual Crop
    2. Forest
    3. Herbaceous Vegetation
    4. Highway
    5. Industrial
    6. Pasture
    7. Permanent Crop
    8. Residential
    9. River
    10. Sea/Lake

Practical Applications

  • Urban Planning: Identifying residential and industrial areas from satellite imagery.
  • Agriculture: Monitoring crop types and assessing agricultural land use.
  • Environmental Protection: Tracking changes in natural land covers like forests and rivers.

How to Use

To use this dataset, you can directly load it through the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("MuafiraThasni/eurosat-dataset-with-image")

Reference

EuroSAT dataset is originally proposed in:

@misc{helber2019eurosatnoveldatasetdeep,
      title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification}, 
      author={Patrick Helber and Benjamin Bischke and Andreas Dengel and Damian Borth},
      year={2019},
      eprint={1709.00029},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1709.00029}, 
}
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