Automatic Road Extraction and Alert Generation for New Roads

• As a city planner, I can receive automated notifications when new roads appear in my jurisdiction.

• As a disaster-response team member, I can quickly identify temporary access routes after floods or earthquakes.

• Acceptance Criteria: New roads are detected with >90% precision and alerts are dispatched within one hour of imagery ingestion.
  

Module 1: Data Preparation

Pre-process satellite images, create masks, and generate folds for training.

Module 2: Model Training & Inference

Train deep-learning models (e.g., UNet/ResNet) inside Docker containers using prepared configs.

Module 3: Change Detection

Compare outputs from current and previous imagery to detect new or altered roads.

Module 4: Alert Generation

Send email/SMS/webhook alerts when road changes cross a configurable threshold.

Module 5: Visualization Dashboard

Display new and existing roads on a live map with export options to GeoJSON or Shapefile.

• Core: Python, Jupyter Notebooks

• Deep Learning: PyTorch / TensorFlow (as configured in CRESI)

• Containerization: Docker (CPU or GPU mode)

• Database: PostGIS or spatially enabled PostgreSQL

• Visualization: Leaflet or Mapbox for interactive maps

• Alerting: FastAPI with email/SMS/webhook integrations
  

• Fully containerized training and inference for reproducibility

• High-precision road mask generation using semantic segmentation

• Automatic stitching and skeletonization of extracted roads

• Real-time change detection and alert dispatch

• Export of detected networks to standard GIS formats
  

• Week 1: Finalize SRS and collect initial satellite imagery

• Week 2: Set up Docker environment and run baseline inference

• Week 3: Prepare training data and run first training cycle

• Week 4: Implement change detection and road skeletonization

• Week 5: Develop alert generation module and integrate email/SMS/webhooks

• Week 6: Build and test visualization dashboard

• Week 7: Final testing with live imagery and accuracy tuning

• Week 8: Documentation and final presentation
  

• Students & Capstone Teams: An impactful AI+GIS project for B.Tech/M.Tech theses.

• Smart City Planners: Automated, low-latency road network updates.

• Disaster Response Agencies: Rapid route detection in emergencies.