| --- |
| license: mit |
| tags: |
| - satellite-imagery |
| - semantic-segmentation |
| - change-detection |
| - pytorch |
| - mgnrega |
| - earth-observation |
| --- |
| |
| 🔗 **[View the Full Project Source Code on GitHub](https://github.com/YuvrajC7/pixel_srishti/)** |
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| --- |
|
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| # PIXEL-Srishti: Geospatial Auditing Models 🌍 |
|
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| This repository contains the core custom-trained vision models for **PIXEL-Srishti** (Jaldrishti). |
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| These models are designed for Edge Inference (running locally on standard laptops) to audit MGNREGA projects, detect illegal construction, and track water body development without relying on high-bandwidth cloud APIs. |
|
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| ## Models Included |
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| ### 1. Land Cover Semantic Segmentation (`latest_seg_model.pth`) |
| * **Architecture:** DeepLabV3+ (with ResNet34 backbone and ASPP) |
| * **Dataset:** Trained on LandCover.ai |
| * **Purpose:** Classifies every pixel in a satellite image into 5 strict classes to identify exactly what is on the ground. |
| * **Classes:** |
| 1. Background |
| 2. Buildings (Infrastructure) |
| 3. Woodlands (Vegetation) |
| 4. Water Bodies (Ponds/Lakes) |
| 5. Roads |
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| ### 2. Structural Change Detection (`latest_cd_model.pth`) |
| * **Architecture:** Siamese U-Net |
| * **Purpose:** Takes a "Before" and "After" satellite image (Optical or SAR) and extracts deep structural features to output a binary mask of exact terrain alterations. |
| * **Use Case:** Validating if a contractor actually dug a new pond or moved earth, while safely ignoring seasonal color changes or shadows. |
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| ## How to Use (Inference) |
| These weights are designed to be dynamically hot-swapped into VRAM by our custom Python Orchestrator to prevent Out-Of-Memory (OOM) errors on 8GB GPUs. |
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| To run them in the PIXEL-Srishti backend, place both files into the `pixel_srishti_ml/checkpoints/` directory: |
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|
| ```python |
| import torch |
| |
| # Load Siamese U-Net (Change Detection) |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
| checkpoint_cd = torch.load('checkpoints/latest_cd_model.pth', map_location=device) |
| |
| # Load DeepLabV3+ (Segmentation) |
| checkpoint_seg = torch.load('checkpoints/latest_seg_model.pth', map_location=device) |
