This project proposes a Light-Weight Ensemble CNN for accurate and computationally efficient land cover classification using the EuroSAT Sentinel-2 dataset.
The ensemble integrates ShuffleNet V2, MobileNet V2, and EfficientNet-B1, each adapted for 4-channel RGB+NIR input.
- 98.00% accuracy
- 90% reduction in model size compared to VGG16
- Suitable for edge devices (drones, mobile/embedded systems)
- Uses transfer learning, feature fusion, and meta-classification
All experiments were performed in Kaggle Notebooks.
The project uses the EuroSAT Land Use and Land Cover Classification Dataset based on Sentinel-2 multispectral imagery.
| Property | Value |
|---|---|
| Total images | 27,000 |
| Image Size | 64×64 pixels |
| Channels | 13 (we use RGB + NIR = 4 channels) |
| Classes | 10 |
Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial, Pasture, Permanent Crop, Residential, River, SeaLake
- Z-score normalization
- Clipping to [-5, 5]
- Resizing to 224×224
- Augmentations: flips, rotations, jitter
- 5-Fold Cross Validation
- Hyperparameter tuning (20 trials × 6 epochs)
Backbones (4‑channel modified):
- ShuffleNet V2
- MobileNet V2
- EfficientNet-B1
Feature Fusion → 3584 dims
Meta-classifier:
3584 → 256 → Dropout → 128 → Dropout → 10
| Metric | Score |
|---|---|
| Accuracy | 98.00% |
| Precision (Macro Avg) | 97.90% |
| Recall (Macro Avg) | 98.00% |
| F1-Score (Macro Avg) | 97.94% |
| AUC-ROC (OvR) | 0.9990 |
| Metric | Ensemble | VGG16 |
|---|---|---|
| Accuracy (%) | 98.00 | 97.20 |
| Model Size | 42.5 MB | 512 MB |
| Params | 10.94M | 134M |
| Inference Time | 0.023s | 0.009s |
notebooks/
results/
data/
- The project was executed entirely in Kaggle Notebooks.
- No local setup required.
- Simply upload the notebooks, attach the EuroSAT dataset, and run all cells.
- EuroSAT dataset authors for providing the land cover dataset.
- Sentinel-2 mission by ESA for multispectral imagery.
This work was carried out in collaboration with:
- Tufan Kundu
- Mahajan Hemant Pandharinath
- Harsh Pandey
We thank all contributors for their support throughout the project.