---
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

## Performance Metrics

- **Accuracy**: Achieved X% accuracy on the validation set
- **F1-Score**: Achieved X% F1-score across all classes

## 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("your_username/eurosat-land-use-classification")
```

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': AnnualCrop
          '1': Forest
          '2': HerbaceousVegetation
          '3': Highway
          '4': Industrial
          '5': Pasture
          '6': PermanentCrop
          '7': Residential
          '8': River
          '9': SeaLake
  splits:
  - name: train
    num_bytes: 110865976.0
    num_examples: 21600
  - name: validation
    num_bytes: 36199366.0
    num_examples: 5400
  download_size: 149255745
  dataset_size: 147065342.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
---