README.md

metadata

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:

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}, 
}