---
language:
- en
tags:
- image-classification
- aerial-imagery
- robotics
- computer-vision
datasets:
- aid_dataset
metrics:
- accuracy
- f1
model-index:
- name: Aerial Image Classification CNN
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: AID (Aerial Image Dataset)
      type: aid
    metrics:
      - type: accuracy
        value: 0.9280
        name: Test Accuracy
      - type: f1
        value: 0.93
        name: Macro F1
---

# Model Card for Aerial Image Classification (CNN & Classic ML)

## Model Details

### Model Description
This repository contains two types of models for classifying aerial images from the **AID dataset**:
1.  **Convolutional Neural Network (CNN):** A lightweight ResNet-based model.
2.  **Classic Machine Learning:** A Bag of Features (BoF) pipeline using SIFT descriptors and Softmax Regression.

These models were developed as part of a machine learning assignment to evaluate deep learning approaches against classical computer vision methods.

- **Model types:** 
    - CNN (PyTorch)
    - BoVW + ML algorithm (Scikit-learn/Joblib)
- **Language(s):** English
- **Resources:** 
    - CNN: ~1M parameters, ~16MB.
    - Classic ML: ~100MB (includes vocabulary).

### Model Architecture
The architecture consists of an initial convolution layer followed by **three residual blocks**.
- **Residual Blocks:** Enable deeper feature extraction without degradation.
- **Layers:** Convolution, Batch Normalization, Max-Pooling.
- **Input:** $600 \times 600$ pixel images (resized as needed by the pipeline).

## Uses

### Direct Use
The model is intended for classifying high-resolution aerial scenes into one of 30 categories. It is suitable for:
- Autonomous UAV navigation and mapping.
- Environmental monitoring.
- Land use classification.

### Downstream Use
This model can be fine-tuned on other aerial or satellite imagery datasets.

## Training Data

The model was trained on the **Aerial Image Dataset (AID)**.
- **Source:** Google Earth imagery.
- **Size:** 10,000 images.
- **Classes:** 30 (e.g., Airport, Beach, Forest, Industrial, etc.).
- **Split:** 90% Training / 10% Test.

## Performance

The CNN significantly outperformed classical Machine Learning methods (SVM, Random Forest, etc.) evaluated on the same dataset.

| Metric | Value |
| :--- | :--- |
| **Test Accuracy** | **92.80%** |
| **Macro Average** | 0.93 |
| **Weighted Average** | 0.93 |

### Comparison with Classical Methods
| Model | Test Accuracy |
| :--- | :--- |
| **CNN (This Model)** | **0.9280** |
| SVM (RBF Kernel) | 0.7120 |
| Softmax Regression | 0.6580 |
| Random Forest | 0.5680 |
| Naïve Bayes | 0.5280 |

## Limitations

- **Data Bias:** The model is trained on Google Earth imagery (AID), so it may not generalize perfectly to aerial images with significantly different sensors, resolutions, or lighting conditions.
- **Scope:** Limited to the 30 classes defined in the AID dataset.

## How to Get Started

You can use the provided `demo.ipynb` notebook for a complete example. Below is a snippet to load both models.

### 1. Load Classic ML Model
```python
from huggingface_hub import hf_hub_download
import joblib

# Download model
model_path = hf_hub_download(
    repo_id="JavideuS/aid-image-classification",
    filename="classicML/models/bovw_softmax.pkl"
)

# Load pipeline
bundle = joblib.load(model_path)
pipeline = bundle['pipeline']
label_encoder = bundle['label_encoder']

# Predict
# pipeline.predict(["path/to/image.jpg"])
```

### 2. Load CNN Model
```python
import torch
from NeuralNets.model import PiattiCNN # Ensure you have the model definition
from huggingface_hub import hf_hub_download

# Download checkpoints
checkpoints_path = hf_hub_download(
    repo_id="JavideuS/aid-image-classification",
    filename="neuralNet/models/PiattiVL_v0.69.pth"
)

# Load model
checkpoints = torch.load(checkpoints_path, map_location='cpu')
model = PiattiCNN(num_classes=checkpoints['num_classes'])
model.load_state_dict(checkpoints['model_state_dict'])
model.eval()

# Inference
# ...
```

## Citation

If you use this model or the AID dataset, please cite the original dataset paper:

```bibtex
@article{aid_dataset,
  title={AID: A Scene Classification Dataset},
  author={Xia, Gui-Song and et al.},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2017}
}
```