Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
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:
- Annual Crop
- Forest
- Herbaceous Vegetation
- Highway
- Industrial
- Pasture
- Permanent Crop
- Residential
- River
- 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:
from datasets import load_dataset
dataset = load_dataset("your_username/eurosat-land-use-classification")