Update README.md

README.md

@@ -1,3 +1,158 @@
----
-license: apache-2.0
----
+# Wetlands Segmentation Model (DeepLabv3+)
+
+## Model Description
+
+This repository contains a DeepLabv3+ model trained for wetlands segmentation from satellite imagery. The model is designed to identify wetland areas in multi-band satellite images, which is crucial for environmental monitoring, conservation planning, and climate change studies.
+
+### Model Architecture
+
+- **Base Architecture**: DeepLabv3+ with ResNet-50 backbone
+- **Input**: Multi-band satellite imagery (focusing on RGB bands)
+- **Output**: Binary segmentation mask (Wetland vs Background)
+- **Resolution**: 128×128 pixels
+
+## Use Cases
+
+- Environmental monitoring of wetland regions
+- Land use and land cover change analysis
+- Conservation planning and management
+- Climate change impact assessment
+- Hydrological modeling
+
+## Training Data
+
+The model was trained on a dataset of satellite imagery patches containing wetland regions. Each patch is 128×128 pixels and includes multiple spectral bands.
+
+### Dataset Structure
+```
+patches_data_allbands/
+├── train/
+│   ├── input/  # Satellite image patches (.tif)
+│   └── output/ # Segmentation masks (.tif)
+├── val/
+│   ├── input/
+│   └── output/
+└── test/
+    ├── input/
+    └── output/
+```
+
+### Data Preprocessing
+
+- Each TIF image contains multiple spectral bands
+- For this model, RGB bands (bands 1, 2, 3) were extracted
+- Images were normalized to the range [0, 1]
+- Masks were converted to binary format (0 = background, 1 = wetland)
+
+### Data Augmentation
+
+The following augmentations were applied during training:
+- Random horizontal flips (p=0.5)
+- Random vertical flips (p=0.5)
+- Random 90-degree rotations (p=0.5)
+- Padding to ensure 128×128 dimensions
+- Random cropping to maintain consistent size
+
+## Performance Metrics
+
+The model was evaluated using the following metrics:
+
+### Training Set
+- Average IoU: 0.2472
+- Background IoU: 0.9379
+- Wetland IoU: 0.2450
+- Mean IoU: 0.5915
+- Precision: 0.2620
+- Recall: 0.7908
+- F1 Score: 0.3936
+
+### Validation Set
+- Average IoU: 0.0489
+- Background IoU: 0.9515
+- Wetland IoU: 0.0481
+- Mean IoU: 0.4998
+- Precision: 0.0533
+- Recall: 0.3313
+- F1 Score: 0.0918
+
+### Test Set
+- Average IoU: 0.1550
+- Background IoU: 0.8977
+- Wetland IoU: 0.1558
+- Mean IoU: 0.5267
+- Precision: 0.1720
+- Recall: 0.6229
+- F1 Score: 0.2695
+
+## Known Limitations
+
+- The model shows signs of overfitting, with significantly better performance on the training set compared to validation and test sets
+- Limited to RGB bands analysis (future work could incorporate more spectral bands)
+- Performance varies based on the quality and resolution of input imagery
+- Binary segmentation only (wetland vs. non-wetland)
+
+## Usage
+
+Here's how to use the model for inference:
+
+```python
+import torch
+from torchvision import transforms
+import rasterio
+import numpy as np
+from model import DeepLabv3Plus  # Import your model architecture
+
+# Load the model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = DeepLabv3Plus(num_classes=2)  # Adjust based on your model configuration
+model.load_state_dict(torch.load("model_weights.pth", map_location=device))
+model.to(device)
+model.eval()
+
+# Function to preprocess and predict
+def predict_wetland(image_path):
+    # Read image using rasterio (get RGB bands)
+    with rasterio.open(image_path) as src:
+        red = src.read(1)
+        green = src.read(2)
+        blue = src.read(3)
+    
+    # Stack to create RGB image
+    image = np.dstack((red, green, blue)).astype(np.float32)
+    
+    # Normalize
+    if image.max() > 0:
+        image = image / image.max()
+    
+    # Convert to tensor and add batch dimension
+    image = torch.from_numpy(image.transpose(2, 0, 1)).float().unsqueeze(0)
+    image = image.to(device)
+    
+    # Get prediction
+    with torch.no_grad():
+        output = model(image)
+        prediction = torch.argmax(output, dim=1).squeeze(0).cpu().numpy()
+    
+    return prediction
+```
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```
+@software{wetlands_segmentation_deeplabsv3plus,
+  author = {dcrey7},
+  title = {Wetlands Segmentation using DeepLabv3+},
+  url = {https://huggingface.co/dcrey7/wetlands_segmentation_deeplabsv3plus},
+  year = {2025},
+}
+```
+
+## License
+
+This model is available under the Apache 2.0 license.
+
+## Contact
+
+For questions or feedback, please open an issue on this repository or contact the repository owner via HuggingFace.