Category: AI/ML + GIS (Smart City & Remote Sensing)
Difficulty: Advanced
Time to Build: 6–8 weeks
Prerequisites: Python, Jupyter, Docker, basic deep learning and GIS concepts
Deliverables: SRS, architecture diagram, training notebooks, Docker setup, test report, working prototype
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Problem Statement & Expected Outcome
Problem Statement
Urban development and disaster response depend on accurate, up-to-date maps. Manual updates are slow and expensive. There is a need for an automated system that can detect newly constructed or modified roads from satellite imagery and instantly alert planners.
Expected Outcome
A deep-learning-based pipeline that:
- Extracts road networks from satellite images,
- Detects newly built or modified roads,
- Sends automated alerts and provides ready-to-use GIS outputs.
Abstract
This project deploys a computer-vision pipeline based on the open-source CRESI framework to detect and map roads from high-resolution satellite images. It compares current and historical imagery to identify new road segments and triggers real-time alerts through email, SMS, or webhooks. The entire workflow is containerized with Docker for reproducible training and deployment.
Details: User Stories & Acceptance Criteria
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As a city planner, I can receive automated notifications when new roads appear in my jurisdiction.
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As a disaster-response team member, I can quickly identify temporary access routes after floods or earthquakes.
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Acceptance Criteria: New roads are detected with >90% precision and alerts are dispatched within one hour of imagery ingestion.
Scope & Modules
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.
Proposed Architecture & Tech Stack
Recommended Stack
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Core: Python, Jupyter Notebooks
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Deep Learning: PyTorch / TensorFlow (as configured in CRESI)
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Containerization: Docker (CPU or GPU mode)
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Database: PostGIS or spatially enabled PostgreSQL
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Visualization: Leaflet or Mapbox for interactive maps
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Alerting: FastAPI with email/SMS/webhook integrations
Key Features
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Fully containerized training and inference for reproducibility
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High-precision road mask generation using semantic segmentation
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Automatic stitching and skeletonization of extracted roads
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Real-time change detection and alert dispatch
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Export of detected networks to standard GIS formats
Milestones & Timeline
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Week 1: Finalize SRS and collect initial satellite imagery
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Week 2: Set up Docker environment and run baseline inference
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Week 3: Prepare training data and run first training cycle
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Week 4: Implement change detection and road skeletonization
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Week 5: Develop alert generation module and integrate email/SMS/webhooks
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Week 6: Build and test visualization dashboard
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Week 7: Final testing with live imagery and accuracy tuning
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Week 8: Documentation and final presentation
Who It’s For
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Students & Capstone Teams: An impactful AI+GIS project for B.Tech/M.Tech theses.
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Smart City Planners: Automated, low-latency road network updates.
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Disaster Response Agencies: Rapid route detection in emergencies.
Progress Checklist
- Docker container built and running
- Training data prepared and masks generated
- Model trained and validated
- Change detection pipeline tested
- Alerts sent and verified
- Visualization dashboard deployed
Resources & Links
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