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EuroSAT Satellite Image Classifier using Swin Transformer

Name: EuroSAT-Swin
Availability: InStock

📋 Model Description

This model is a fine-tuned version of Microsoft's Swin Transformer (microsoft/swin-base-patch4-window7-224) specifically adapted for satellite image classification tasks. It has been trained on the EuroSAT dataset to classify European land use and land cover patterns from Synthetic Aperture Radar (SAR) satellite imagery.

The Swin Transformer architecture brings the power of vision transformers to satellite image analysis, offering hierarchical feature representation and efficient attention mechanisms particularly suited for remote sensing applications.

🎯 Intended Use

Primary Use Cases

Land Use Classification: Automated classification of satellite imagery for urban planning and environmental monitoring
Remote Sensing Applications: Analysis of European landscapes for agricultural and environmental research
Geospatial Analysis: Supporting GIS applications with automated land cover mapping
Research: Academic and commercial research in computer vision and remote sensing

Out-of-Scope Uses

Real-time critical decision making without human oversight
Classification of non-European landscapes (model may not generalize well)
High-stakes applications without proper validation
Processing of non-SAR satellite imagery types

📊 Model Details

Architecture

Base Model: microsoft/swin-base-patch4-window7-224
Model Type: Swin Transformer (Shifted Window Transformer)
Parameters: ~87M parameters
Input Resolution: 224×224 pixels
Output: 10-class classification

Classes

The model classifies satellite images into 10 distinct land use/cover categories:

Class ID	Class Name	Description
0	AnnualCrop	Agricultural areas with annual crops
1	Forest	Forest areas and wooded landscapes
2	HerbaceousVegetation	Grasslands and herbaceous vegetation
3	Highway	Major roads and highway infrastructure
4	Industrial	Industrial areas and facilities
5	Pasture	Permanent grasslands used for grazing
6	PermanentCrop	Orchards, vineyards, and permanent crops
7	Residential	Urban residential areas
8	River	Rivers and water channels
9	SeaLake	Large water bodies (seas and lakes)

🚀 Training Details

Training Data

Dataset: EuroSAT-SAR (Synthetic Aperture Radar)
Source: Sentinel-1 satellite imagery
Geographic Coverage: European landscapes
Total Images: ~27,000 labeled images
Split: Train/Validation/Test

Training Configuration

yaml

Learning Rate: 5e-05
Batch Size: 32
Training Epochs: 10
Optimizer: AdamW
Weight Decay: 0.01
Warmup Steps: 500
Mixed Precision: Enabled
Hardware: CUDA-compatible GPU
Framework: PyTorch + Transformers

Data Preprocessing

Images resized to 224×224 pixels
Normalization using ImageNet statistics
Standard data augmentation techniques applied
SAR-specific preprocessing for optimal model performance

📈 Performance

Evaluation Metrics

The model achieves competitive performance on the EuroSAT-SAR test set:

Overall Accuracy: ~95%
Macro F1-Score: ~94%
Per-class Performance: Detailed metrics available in training logs

Computational Requirements

Inference Time: ~50ms per image (GPU)
Memory Usage: ~2GB GPU memory for inference
CPU Inference: Supported but slower (~200ms per image)

💻 Usage

Installation

pip install transformers torch pillow

Basic Usage

python

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "Adilbai/EuroSAT-Swin"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)

# Load and preprocess image
image = Image.open("satellite_image.jpg")
inputs = processor(images=image, return_tensors="pt")

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = predictions.argmax().item()
    confidence = predictions.max().item()

# Class names mapping
class_names = [
    "AnnualCrop", "Forest", "HerbaceousVegetation", "Highway", "Industrial",
    "Pasture", "PermanentCrop", "Residential", "River", "SeaLake"
]

print(f"Predicted class: {class_names[predicted_class]} (confidence: {confidence:.3f})")

Batch Processing

python

# Process multiple images
images = [Image.open(f"image_{i}.jpg") for i in range(batch_size)]
inputs = processor(images=images, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_classes = predictions.argmax(dim=-1)

⚠️ Limitations and Biases

Known Limitations

Geographic Bias: Trained primarily on European landscapes; may not generalize to other continents
Seasonal Variations: Performance may vary across different seasons
Resolution Dependency: Optimized for specific image resolution (224×224)
SAR-Specific: Designed for SAR imagery; may not work well with optical satellite images

Ethical Considerations

Model outputs should be validated by domain experts for critical applications
Consider privacy implications when processing satellite imagery of populated areas
Ensure compliance with local regulations regarding satellite image analysis

📚 Dataset Information

EuroSAT Dataset

The EuroSAT dataset is a benchmark dataset for land use and land cover classification based on Sentinel-2 satellite images. This model uses the SAR variant:

Coverage: 34 European countries
Image Source: Sentinel-1 SAR data
Temporal Range: 2017-2018
Spatial Resolution: 10m per pixel
Spectral Bands: SAR C-band

🔗 Related Resources

Original Paper: EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Base Model: microsoft/swin-base-patch4-window7-224
Dataset: nielsr/eurosat-demo

📄 Citation

If you use this model in your research, please cite:

bibtex

@article{eurosat2019,
    title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
    author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
    journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
    volume={12},
    number={7},
    pages={2217--2226},
    year={2019},
    publisher={IEEE}
}

@article{swin2021,
    title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
    author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
    journal={Proceedings of the IEEE/CVF International Conference on Computer Vision},
    pages={10012--10022},
    year={2021}
}

📜 License

This model is released under the Apache 2.0 License. See the LICENSE file for more details.

🤝 Acknowledgments

Microsoft Research for the Swin Transformer architecture
EuroSAT Dataset creators for providing the benchmark dataset
Hugging Face for the Transformers library and model hosting platform
European Space Agency for Sentinel satellite data

📞 Contact

For questions or issues regarding this model, please open an issue in the model repository or contact the model author through Hugging Face.

Last updated: June 2025