Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 3,188 Bytes
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---
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-*
---
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