Road Extrcation Model BAH

--- license: mit pipeline_tag: image-segmentation --- # Road Segmentation Model ## Overview This model performs road segmentation from satellite imagery using a deep learning semantic segmentation architecture. The model takes an RGB satellite image as input and predicts a binary road mask indicating road locations. ## Model Details ### Architecture * Framework: PyTorch * Deployment Format: TorchScript (.pt) * Task: Semantic Segmentation * Output: Binary road mask ### Input * RGB satellite image * Expected shape: 3 × H × W * Pixel values normalized to [0,1] ### Output * Single-channel road probability mask * Higher values indicate higher confidence of road presence ## Usage ```python import torch model = torch.jit.load("road_model_Deployment.pt") model.eval() prediction = model(image_tensor) ``` ## Performance | Metric | Score | | --------------- | -------------- | | IoU | Add Your Score | | Dice Score | Add Your Score | | Validation Loss | Add Your Score | ## Limitations * Performance may vary across geographic regions. * Small roads may be difficult to detect. * Results depend on image quality and resolution. ## Intended Uses ### Intended * Road extraction * Infrastructure mapping * Transportation analysis * Disaster response ### Not Intended * Safety-critical systems * Autonomous vehicle control * Real-time navigation ## Files * road_model_Deployment.pt * inference.py * requirements.txt ## License MIT License

Source context: 0 downloads · 1 likes · Pipeline image-segmentation · Repo manish2607/road_extrcation_model_BAH

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo manish2607/road_extrcation_model_BAH