Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Sub-tasks:
multi-class-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
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-* | |
--- | |