README.md

---
annotations_creators:
- other
language:
- en
license:
- other
multilinguality:
- no
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-classification
---


# 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:
  0. Annual Crop
  1. Forest
  2. Herbaceous Vegetation
  3. Highway
  4. Industrial
  5. Pasture
  6. Permanent Crop
  7. Residential
  8. River
  9. 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:

```python
from datasets import load_dataset

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

## Reference
EuroSAT dataset is originally proposed in: 
```bibtex
@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}, 
}
```