Topology-Preserving Cartographic Displacement System
An autonomous AI engine that resolves visual clutter in maps while preserving network topology and spatial relationships. Built for the Axes Systems Masai Hackathon.
Challenge: Ungeneralized Street Network Displacement (Problem 3)
Dense urban street networks often suffer from visual clutter when displayed at smaller scales, making maps difficult to read and navigate. Traditional displacement methods frequently break valid topological relationships, creating disconnected networks that are unusable for professional routing and analysis.
The MapSense AI Solution:
Our system delivers a robust, automated solution that balances visual clarity with strict data integrity:
- Resolved 165 Visual Conflicts: Successfully separated overlapping features.
- 12.74% Clarity Improvement: Measurable increase in map legibility.
- 100% Topology Preservation: Maintained all network connections without breakage.
- High Performance: Processes complex datasets in under 2 seconds.
Team Name: MapSense AI
Our team consists of 3 enthusistic students who love to code and love learning new things and Andhra University Professor who love to solve complex cartographic challenges.
- Bhaskar Kumar Thakur (Data Analyst and IIT Madras Student)
- Subrata Choudhury (Web Developer and Software Engineering Student)
- Chodiboyina Poorna Shekar (AI/ML IIT Guwahati Student)
- Lakshmi Narasimha Rao (Andhra University Professor)
- Supervising Team Advisor
- Topology-Aware Algorithms: Uses NetworkX graph theory to ensure connectivity is never compromised.
- Force-Directed Physics: Simulates repulsive forces between conflicts and attractive spring forces to maintain shape.
- Priority-Weighted Logic: Allows critical infrastructure (Primary Roads) to resist movement more than local streets.
- Elastic Anchoring: Shared intersections act as flexible anchors to prevent feature detachment.
- Catmull-Rom Smoothing: Applies professional cartographic smoothing for a polished aesthetic.
- Conflict Detection: Spatial overlap analysis with detailed severity reporting.
- Clarity Scoring: A quantitative mathematical measure (0-100%) of map legibility.
- Real-Time Analytics: Live dashboard showing iteration-by-iteration progress.
- Topology Verification: Automated checks to guarantee network integrity.
- Side-by-Side Comparison: Split-view mode to inspect Before vs. After states simultaneously.
- Swipe Comparison Slider: Interactive slider to reveal displacement results.
- Glassmorphism Design: A modern, professional dark-mode UI.
- Interactive Controls: Fine-tune specific layer priorities and opacity.
- Playback Animation: deeply understand the algorithm's decisions by watching the displacement unfold.
- RESTful Design: Comprehensive endpoints for integration.
- WKT Support: Upload and process industry-standard Well-Known Text files.
- Live Status: Real-time processing feedback via polling.
A professional command center for cartographic displacement.
Split view showing precise conflict resolution while maintaining connectivity.
Detailed analytics ensuring data quality and algorithmic performance.
MapSense AI is built on a modern, scalable stack designed for performance and reliability.
Frontend (React + TypeScript) Built with React 18 and Vite for lightning-fast performance. Uses Zustand for efficient state management and Leaflet for high-fidelity map rendering. The UI is crafted with Tailwind CSS and Shadcn/UI for a premium experience.
Backend (FastAPI + Python) Powered by Python 3.12 and FastAPI. leverages NetworkX for graph topology, Shapely for geometric operations, and NumPy for high-speed numerical computations.
- Python 3.12+
- Node.js 18+
1. Clone the Repository
git clone https://github.com/p4r1ch4y/mapsense-ai.git
cd mapsense-ai2. Backend Setup
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
cd backend
pip install -r requirements.txt
uvicorn main:app --reload --port 80023. Frontend Setup
cd frontend
npm install
echo "VITE_API_BASE_URL=http://localhost:8002" > .env
npm run devProblem 3 Benchmark
| Metric | Initial State | Final State | Improvement |
|---|---|---|---|
| Visual Conflicts | 165 | 144 | -21 resolved |
| Clarity Score | 68.91% | 81.65% | +12.74% |
| Topology Status | Valid | Valid | Preserved |
| Processing Time | - | < 2s | Real-time |
Our algorithm demonstrates that it is possible to achieve significant visual clarity improvements without sacrificing the topological integrity essential for navigation and analysis.
Special thanks to the Axes Systems x MasaiVerse Hackarena 3.O Hackathon organizers for providing this challenging problem statement.
- Masai School for the platform and support.
- Axes Systems for the complex challange
- Open Source Community for the incredible tools (NetworkX, Shapely, React).
If you find this project useful, please consider giving it a star! ⭐
Built with ❤️ by the MapSense AI Team ( Bhaskar, Shekar & Subrata ) for better maps
MapSense AI - Making maps clearer, one displacement at a time.