https://github.com/shama-llama/cropland-mapping
Cropland mapping from multichannel spectral data using deep learning
https://github.com/shama-llama/cropland-mapping
computer-science conv-lstm cosc-6041 cropland-mapping machine-learning
Last synced: about 1 month ago
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Cropland mapping from multichannel spectral data using deep learning
- Host: GitHub
- URL: https://github.com/shama-llama/cropland-mapping
- Owner: shama-llama
- License: mit
- Created: 2025-02-23T13:26:18.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-08-03T10:56:23.000Z (11 months ago)
- Last Synced: 2025-08-03T12:30:34.022Z (11 months ago)
- Topics: computer-science, conv-lstm, cosc-6041, cropland-mapping, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 31.9 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Cropland Mapping
[](https://docs.python.org/3.12/)
[](https://docs.pytorch.org/docs/2.7/)
[](https://scikit-learn.org/stable/whats_new/v1.7.html)
[](https://colab.research.google.com/github/shama-llama/cropland-mapping/blob/main/notebooks/cmap_msi.ipynb)
[](https://opensource.org/licenses/MIT)
This project uses a deep learning framework for crop classification using multichannel inputs from Sentinel-2, combining high-resolution RGB imagery with vegetation indices (VIs) such as NDVI and GNDVI to capture both spatial and phenological crop characteristics. The model used is a convolutional long short-term memory (ConvLSTM) architecture to process time-series sequences of fused sensor data that integrates spatial features and temporal dependencies across multiple seasons. Input normalization, channel-wise sensor fusion, and data augmentation are used to mitigate class imbalance inherent in the dataset. It is evaluated on the [Canadian Cropland Dataset](https://github.com/bioinfoUQAM/Canadian-cropland-dataset-github) and the model achieves ~90% classification accuracy.
## Architecture
## Findings
| Class | Precision | Recall | F1-Score | Support |
| :--- | :--- | :--- | :--- | :--- |
| BARLEY | 0.82 | 0.75 | 0.78 | 36 |
| CANOLA | 0.94 | 0.97 | 0.96 | 66 |
| CORN | 0.89 | 0.91 | 0.90 | 55 |
| MIXEDWOOD | 0.94 | 1.00 | 0.97 | 32 |
| OAT | 0.91 | 0.88 | 0.89 | 67 |
| ORCHARD | 0.98 | 0.92 | 0.95 | 51 |
| PASTURE | 0.86 | 0.91 | 0.89 | 47 |
| POTATO | 0.90 | 0.94 | 0.92 | 67 |
| SOYBEAN | 0.94 | 0.89 | 0.92 | 104 |
| SPRING_WHEAT | 0.56 | 0.71 | 0.62 | 7 |
| **Accuracy** | | | **0.91** | **532** |
| **Macro Avg** | **0.87** | **0.89** | **0.88** | **532** |
| **Weighted Avg** | **0.91** | **0.91** | **0.91** | **532** |
> There is visible overfitting starting from the 4th epoch onwards which should be addressed in later works.
## Project Setup
This project uses `uv` for package management. `uv` is an extremely fast Python package and project manager, written in Rust that can be used as a drop-in replacement for `pip`, `pip-tools`, `pipx`, `poetry`, `pyenv`, `twine`, `virtualenv`.
- **`uv` Installation**
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
- **Clone the Repository:**
```bash
git clone https://github.com/shama-llama/cropland-mapping.git
cd cropland-mapping
```
- **Create a Virtual Environment and Install Dependencies with `uv`:**
```bash
uv venv
uv pip install -e .
```
- **Activate the Virtual Environment:**
```bash
source .venv/bin/activate
```
- **Launch Jupyter Notebook:**
```bash
jupyter notebook
```
Navigate to the `notebooks/` directory to run the analysis.
## Notebooks
- `cmap_dataset.ipynb`: Data preparation with minor preprocessing and loading into HDF5 for better portability.
- `cmap_eda.ipynb`: Exploratory data analysis.
- `cmap_msi.ipynb`: ConvLSTM model training and evaluation.
## Model
The trained ConvLSTM model weights are saved in the `models/` directory.
```python
torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.load_state_dict(torch.load('models/best_model.pth', map_location=device))
```
## References
> A. A. B. Jacques, A. B. Diallo, and E. Lord, “The Canadian Cropland Dataset: A New Land Cover Dataset for Multitemporal Deep Learning Classification in Agriculture,” June 04, 2023, arXiv: arXiv:2306.00114. doi: [10.48550/arXiv.2306.00114](https://doi.org/10.48550/arXiv.2306.00114).
>
> M. O. Turkoglu et al., “Crop mapping from image time series: Deep learning with multi-scale label hierarchies,” Remote Sensing of Environment, vol. 264, p. 112603, Oct. 2021, doi: [10.1016/j.rse.2021.112603](https://doi.org/10.1016/j.rse.2021.112603).
>
> D. Darwish, “Improving Techniques for Convolutional Neural Networks Performance,” European Journal of Electrical Engineering and Computer Science, vol. 8, pp. 1–16, Jan. 2024. Number: 1.
>
> J. M. Johnson and T. M. Khoshgoftaar, “Survey on deep learning with class imbalance,” Journal of Big Data, vol. 6, pp. 1–54, Dec. 2019. Number: 1 Publisher: SpringerOpen.
>
> C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, pp. 1–48, Dec. 2019. Number: 1 Publisher: SpringerOpen.
## License
This project is licensed under the terms of the [MIT](LICENSE) open source license.