Automating Survey-to-CAD Workflows with AI-Powered Civil Engineering Intelligence
Civil engineering teams spend significant time converting raw survey points into CAD drawings. Manual drafting is slow, repetitive, and dependent on technician interpretation. Red Buffer built TerraCAD’s AI intelligence layer to clean survey data and predict engineering linework automatically.
ROLE
AI and machine learning pipeline design, survey correction workflow development, graph-based connectivity prediction, and CAD intelligence automation.
TOOL
Python, PyTorch, PyTorch Geometric, NumPy, scikit-learn, Microsoft Azure A100 GPU VM, LibreDWG, ODA File Converter, GitLab
DURATION
Multi-phase engagement with the Phase 1 intelligence layer completed and Phase 2 Civil 3D integration planned.
Our Approach
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Survey Data Cleaning and Error Correction
Built preprocessing workflows to correct mislabeled feature codes, shifted coordinates, and noisy survey points using heuristic thresholding and multi-head neural networks.
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Ground Truth Generation from CAD Drawings
Used historical 2D engineering drawings converted from DWG and DXF formats to create training datasets and establish accurate connectivity references.
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Graph Neural Network Connectivity Prediction
Developed a Graph Neural Network using PyTorch Geometric that models survey points as graph nodes with spatial and semantic embeddings to predict engineering linework connectivity automatically.
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Scalable AI Training and Evaluation
Trained models on Azure A100 GPU infrastructure while using NumPy and scikit-learn for evaluation and performance validation achieving strong connectivity accuracy and recall metrics.
Why It Matters
This project automated one of the most manual stages of surveying and civil engineering converting raw field measurements into connected CAD-ready linework. The two-stage architecture combining survey data cleaning and graph-based connectivity prediction established a scalable foundation for future Civil 3D integration and broader CAD automation.
Outcome
89% Connectivity Prediction Accuracy
Successfully automated engineering linework generation from cleaned survey datasets.
80% Recall on Clean Data
Achieved high-quality detection and linkage of engineering survey features.
Reduced Manual CAD Effort
Automated correction and connectivity prediction minimized repetitive drafting workflows.
Scalable AI Foundation for Civil 3D Integration
Established the intelligence layer required for future CAD automation and engineering workflows.
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