Agent skill · research science · datadrivenconstruction
progress-monitoring-cv
Monitor construction progress using computer vision. Analyze site photos and drone imagery to track work completion, detect safety issues, and compare against BIM models.
Why this skill is useful
Adds executable scripts for analyzing construction images and detecting safety issues that are not commonly found in public resources.
What it needs
Requires python3 installed locally. About 5k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
Progress Monitoring with Computer Vision Overview This skill implements computer vision for construction progress monitoring. Analyze site images automatically to track completion, detect hazards, and compare physical progress against planned work. Applications: Progress percentage estimation Safety compliance detection (PPE, barriers) As-built vs BIM comparison Material and equipment tracking Quality defect detection Quick Start Progress Detection System Core Progress Analyzer Time-Lapse Analysis Quick Reference Analysis Type Method Output -------------- -------- -------- Phase Detection Color analysis + Object detection Construction phase Progress % Element counting + Phase base Completion percentage Safety Check Color detection (PPE) + Worker count Safety observations Quality Check Edge detection + Anomaly detection Quality issues BIM Comparison Structural similarity Similarity score Resources OpenCV: https://opencv.org PyTorch: https://pytorch.org YOLO: https://ultralytics.com/yolov8 DDC Website: https://datadrivenconstruction.io Next Steps See 4d-simulation for schedule comparison See data-visualization for progress dashboards See risk-assessment-ml for delay prediction
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/progress-monitoring-cv