Turning Unseen Terrain into Intelligent Decisions
TerrainIQ is an AI-powered terrain segmentation and tactical risk analysis system built for:
- Law Enforcement
- Emergency Response
- Disaster Rescue
- Off-road Mobility
It uses a UNet (ResNet34) deep learning model to convert terrain images into actionable mobility intelligence.
- Pixel-Level Terrain Segmentation (10 Classes)
- Intelligent Risk Scoring Engine
- Vehicle Suitability Analysis
- Drone Surveillance Index
- Emergency Deployment Simulation
- Tactical Deployment Intelligence (Delhi Police Mode)
- Python
- Streamlit (Interactive Web Dashboard)
- Jupyter Notebook (Model Development & Experimentation)
- PyTorch
- Torchvision
- U-Net (Encoder–Decoder Architecture)
- ResNet34 Encoder
- segmentation-models-pytorch
- NumPy
- OpenCV
- Matplotlib
- Intersection over Union (IoU)
- Pixel-wise Semantic Segmentation Accuracy
- Validation IoU:
0.3900
The model achieves stable multi-class terrain segmentation accuracy on validation data drawn from the same distribution as the training set. This demonstrates effective feature learning and convergence of the U-Net (ResNet34) architecture.
- Cross-Environment IoU:
0.2390
When evaluated on unseen terrain environments, performance decreases due to domain shift between training and test data. This reflects real-world deployment challenges where terrain textures, lighting conditions, and environmental distributions differ.
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The gap between validation and cross-environment IoU highlights the following:
- Terrain appearance variability
- Lighting and environmental shifts
- Class imbalance in rare terrain categories
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Despite distribution shift, the model maintains meaningful segmentation capability, enabling reliable downstream decision-making in:
- Risk Scoring
- Vehicle Suitability Analysis
- Tactical Deployment Simulation
The observed generalization gap demonstrates strong in-distribution learning while identifying opportunities for future enhancement through domain adaptation techniques, stronger augmentation pipelines, and multi-environment training strategies.
- Synthetic Digital Twin Dataset
- Generated using Duality AI Falcon
git clone https://github.com/your-username/TerrainIQ.git
cd TerrainIQ
pip install -r requirements.txt
streamlit run app.py- Upload terrain image
- AI performs segmentation
- Risk engine calculates terrain score
- Tactical module simulates deployment
- System outputs final operational decision
- Real-time video segmentation
- Edge AI deployment (Jetson Nano)
- Path planning integration
- Multi-sensor fusion
Deployed Application:
https://terrainiq-e4jrqkq6cjbx2qq7au4xkw.streamlit.app/
- Sakshi Mittal – AI & Systems Developer
- Saumya Dwivedi – AI & Systems Developer
AI doesn’t just detect terrain.
It enables safe autonomy beyond roads.