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multi-class-classification
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English
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image
imagewidth (px) 64
64
| label
class label 10
classes |
---|---|
1Forest
|
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7Residential
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2HerbaceousVegetation
|
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7Residential
|
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1Forest
|
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0AnnualCrop
|
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8River
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0AnnualCrop
|
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1Forest
|
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7Residential
|
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4Industrial
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8River
|
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5Pasture
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4Industrial
|
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9SeaLake
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6PermanentCrop
|
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1Forest
|
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4Industrial
|
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1Forest
|
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0AnnualCrop
|
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4Industrial
|
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9SeaLake
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1Forest
|
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2HerbaceousVegetation
|
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4Industrial
|
|
1Forest
|
|
0AnnualCrop
|
|
0AnnualCrop
|
|
1Forest
|
|
1Forest
|
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3Highway
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8River
|
|
2HerbaceousVegetation
|
|
1Forest
|
|
6PermanentCrop
|
|
1Forest
|
|
7Residential
|
|
9SeaLake
|
|
4Industrial
|
|
3Highway
|
|
9SeaLake
|
|
6PermanentCrop
|
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4Industrial
|
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3Highway
|
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4Industrial
|
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8River
|
|
2HerbaceousVegetation
|
|
6PermanentCrop
|
|
0AnnualCrop
|
|
9SeaLake
|
|
0AnnualCrop
|
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0AnnualCrop
|
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7Residential
|
|
0AnnualCrop
|
|
4Industrial
|
|
5Pasture
|
|
9SeaLake
|
|
1Forest
|
|
2HerbaceousVegetation
|
|
2HerbaceousVegetation
|
|
2HerbaceousVegetation
|
|
3Highway
|
|
8River
|
|
8River
|
|
5Pasture
|
|
0AnnualCrop
|
|
2HerbaceousVegetation
|
|
2HerbaceousVegetation
|
|
7Residential
|
|
3Highway
|
|
6PermanentCrop
|
|
7Residential
|
|
5Pasture
|
|
2HerbaceousVegetation
|
|
1Forest
|
|
4Industrial
|
|
2HerbaceousVegetation
|
|
8River
|
|
2HerbaceousVegetation
|
|
0AnnualCrop
|
|
6PermanentCrop
|
|
9SeaLake
|
|
1Forest
|
|
0AnnualCrop
|
|
4Industrial
|
|
4Industrial
|
|
9SeaLake
|
|
2HerbaceousVegetation
|
|
1Forest
|
|
2HerbaceousVegetation
|
|
8River
|
|
1Forest
|
|
6PermanentCrop
|
|
3Highway
|
|
4Industrial
|
|
3Highway
|
|
2HerbaceousVegetation
|
|
6PermanentCrop
|
|
7Residential
|
|
1Forest
|
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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
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},
}
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