VAJRADRISTI Intelligent Terrain Vision & Risk Navigation Platform
VajraDristi is an AI-powered terrain intelligence and safety navigation system designed to analyze complex environments, detect risks, and generate safe navigation paths in real time. The platform transforms raw terrain data into actionable safety insights, enabling autonomous systems, rescue teams, and field operators to make confident and reliable decisions in unpredictable environments.
Project Overview
Navigating unstructured terrain such as disaster zones, forests, and off-road environments presents significant safety challenges. Hidden hazards like rocks, debris, unstable ground, and environmental changes can lead to accidents, mission delays, and operational failures.
VajraDristi addresses this challenge by combining computer vision, risk analysis, and intelligent navigation algorithms to provide:
Real-time terrain understanding Risk heatmap visualization Safe path generation Dynamic environment updates Reliable decision support Key Features
- Real-Time Terrain Segmentation
Detects terrain elements from images using deep learning models.
Identifies:
Rocks Bushes Ground Obstacles Logs Uneven terrain 2. Intelligent Risk Heatmap
Converts terrain segmentation into safety zones.
Risk Levels:
Green — Safe Yellow — Moderate Risk Red — High Risk 3. Dynamic Safe Path Generator
Calculates the safest route using a risk-aware path planning algorithm.
Uses:
A* navigation algorithm Risk-weighted cost function Real-time recalculation 4. Real-Time Alert System
Generates alerts when dangerous terrain conditions are detected.
Examples:
HIGH RISK DETECTED Obstacle Ahead Unsafe Path
- Explainable Decision System
Provides transparency into AI decisions.
Example:
Why High Risk?
Large rock detected Dense obstacle region Close proximity 6. Web-Based Visualization Dashboard
Displays system outputs in an interactive interface.
Shows:
Original terrain image Segmentation output Risk heatmap Safe path System Architecture User / Robot / Drone ↓ Camera / Sensor Input ↓ Image Processing ↓ AI Terrain Segmentation Model ↓ Risk Classification Engine ↓ Dynamic Safe Path Generator ↓ Visualization Dashboard ↓ Safe Navigation Decision Technology Stack
Frontend:
HTML CSS JavaScript Responsive Web UI
Backend:
Python Flask / FastAPI
Computer Vision:
OpenCV NumPy
Machine Learning:
Deep Learning Segmentation Model Risk Classification Logic
Navigation:
A* Path Planning Algorithm
Visualization:
Matplotlib Canvas Rendering Nationally Trusted Data Sources
VajraDristi intentionally prioritizes deterministic and reliable data sources for safety-critical decision-making.
The system does not rely on Large Language Models (LLMs) for navigation decisions.
Instead, it integrates nationally recognized APIs:
ISRO Bhuvan API
Provides:
Satellite imagery Terrain and geospatial data Environmental monitoring
IMD Weather API
Provides:
Weather forecasts Rainfall and storm alerts Environmental condition updates
NDMA Disaster API
Provides:
Disaster alerts Emergency notifications Risk zone information
This approach ensures:
Reliability Explainability Operational safety Real-world deployment readiness Installation Guide
Clone the repository:
git clone https://github.com/your-username/vajradristi.git cd vajradristi
Install dependencies:
pip install -r requirements.txt
Run the server:
python app.py
Open in browser:
http://localhost:5000 Input
The system accepts:
Terrain image Camera feed Map data
Example input:
terrain_image.jpg Output
The system generates:
Segmentation Map Risk Heatmap Safe Navigation Path
Example:
Original Image Segmentation Output Risk Heatmap Safe Path Performance Metrics
Terrain Segmentation Accuracy:
95%
Risk Classification Accuracy:
90%
Safe Path Generation Time:
2 seconds
Inference Time:
45 milliseconds
Challenges Faced Inconsistent segmentation results due to limited dataset diversity Risk heatmap misalignment issues Zig-zag path generation in early navigation models Performance delays with large map sizes Improvements Implemented Expanded dataset and applied data augmentation Standardized image resolution and coordinate mapping Implemented path smoothing algorithms Optimized grid-based navigation logic Added validation checks for system reliability Project Workflow Input Image ↓ Segmentation Model ↓ Risk Classification ↓ Heatmap Generation ↓ Safe Path Planning ↓ Visualization Repository Structure vajradristi/
├── app.py ├── model/ │ ├── segmentation_model.py │ └── risk_classifier.py │ ├── navigation/ │ └── path_planner.py │ ├── static/ │ ├── css/ │ ├── js/ │ └── images/ │ ├── templates/ │ └── index.html │ ├── requirements.txt ├── README.md Future Scope Real-time drone integration 3D terrain mapping Multi-agent navigation Predictive hazard detection Edge device deployment Reinforcement learning navigation Use Cases Disaster response Search and rescue operations Defense and security Autonomous vehicles Robotics navigation Environmental monitoring One-Line Summary
VajraDristi transforms complex terrain data into intelligent, real-time navigation decisions for safer and more reliable operations.