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--- |
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license: apache-2.0 |
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tags: |
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- pytorch |
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- image-segmentation |
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- remote-sensing |
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- earth-observation |
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- mangroves |
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- carbon-credits |
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- climate |
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datasets: |
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- sentinel-2 |
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- global-mangrove-watch |
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language: |
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- en |
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metrics: |
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- iou |
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pipeline_tag: image-segmentation |
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--- |
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# NatureCode-Mangroves |
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**Ultimate mangrove monitoring model for carbon credits, biocredits, and nature credits.** |
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## Model Description |
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NatureCode-Mangroves is a state-of-the-art deep learning model for mangrove ecosystem monitoring using satellite imagery. It combines: |
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- **Swin Transformer backbone** for global context understanding |
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- **Mamba (State-Space) blocks** for efficient long-range dependencies |
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- **UNet decoder** with attention gates |
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- **Multi-task learning** with specialized output heads |
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## Performance |
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| Metric | Value | |
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|--------|-------| |
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| Mangrove IoU | **94.91%** | |
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| Parameters | 40.2M | |
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| Training Data | 96 real Sentinel-2 tiles | |
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## Intended Use |
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This model is designed for: |
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- Mangrove extent mapping (segmentation) |
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- Carbon stock estimation |
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- Change detection over time |
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- Biodiversity assessment |
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- Blue carbon project monitoring |
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### Carbon Credit Standards |
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The model outputs are compatible with: |
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- Verra VCS VM0033 (Tidal Wetland Methodology) |
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- Gold Standard Mangrove Methodology |
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- IPCC Tier 2/3 guidelines |
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- Blue Carbon Initiative |
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## Usage |
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```python |
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import torch |
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from naturecode_mangroves import create_model |
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# Load model |
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model = create_model({'in_channels': 19}) |
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checkpoint = torch.load('pytorch_model.bin', map_location='cpu') |
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model.load_state_dict(checkpoint['state_dict']) |
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model.eval() |
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# Prepare input (19 channels: 10 optical + 4 indices + 3 SAR + 2 extra) |
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# See prepare_input() function for details |
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input_tensor = torch.randn(1, 19, 256, 256) |
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# Run inference |
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with torch.no_grad(): |
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outputs = model(input_tensor) |
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segmentation = outputs['segmentation'] # (1, 2, 256, 256) |
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mangrove_prob = torch.softmax(segmentation, dim=1)[:, 1] # Mangrove probability |
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``` |
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## Input Format |
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The model expects 19-channel input: |
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1. **Bands 0-9**: Sentinel-2 optical bands (B02, B03, B04, B05, B06, B07, B08, B8A, B11, B12) |
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2. **Bands 10-13**: Spectral indices (NDVI, NDWI, MNDWI, CMRI) |
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3. **Bands 14-16**: SAR channels (VV, VH, VV/VH ratio) - can be simulated from optical |
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4. **Bands 17-18**: Extra features (NDVI duplicate, NIR-SWIR average) |
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## Output Heads |
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The model produces multiple outputs: |
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- `segmentation`: Binary mangrove/non-mangrove segmentation |
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- `agb`: Above-Ground Biomass estimation (Mg/ha) |
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- `soc`: Soil Organic Carbon (Mg C/ha) |
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- `health`: Ecosystem health score (0-1) |
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- Additional heads for species, change detection, etc. |
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## Training Data |
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Trained on real Sentinel-2 imagery from: |
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- Sundarbans (India/Bangladesh) |
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- Mekong Delta (Vietnam) |
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- Amazon Delta (Brazil) |
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- Niger Delta (Nigeria) |
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- Queensland (Australia) |
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- Florida (USA) |
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- And 50+ other global mangrove regions |
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Labels derived from: |
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- Global Mangrove Watch (GMW) v4.0 |
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- Spectral index-based pseudo-labels |
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- Expert validation |
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## Limitations |
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- Optimized for 10m resolution Sentinel-2 imagery |
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- Best performance in tropical/subtropical mangrove regions |
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- SAR channels are simulated when real SAR not available |
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- Carbon estimates require calibration for specific regions |
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## Citation |
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```bibtex |
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@software{naturecode_mangroves_2026, |
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author = {NatureCode Team}, |
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title = {NatureCode-Mangroves: Deep Learning for Mangrove Ecosystem Monitoring}, |
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year = {2026}, |
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url = {https://huggingface.co/naturecode/mangroves} |
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} |
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``` |
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## License |
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Apache 2.0 |
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