01 The challenge
Heritage conservation assessments in Malta traditionally required manual photography and visual identification of deterioration (cracks, erosion, staining) by architects and conservators. This manual mapping onto technical drawings proved time-consuming, subjective, and difficult to standardize across different assessors.
02 The solution
Neural AI developed LIMAP, a custom computer vision system trained on real deterioration cases from Maltese heritage sites. The system processes standard photographs without specialized equipment and automatically detects multiple surface decay types including cracks, biological growth, salt crystallization, and material loss.
03 Our approach
- AI model trained on authentic Maltese heritage deterioration data.
- Automated surface decay detection from standard digital photographs.
- Direct overlay capability onto AutoCAD drawings.
- Visual condition report generation for stakeholders.
- Cloud-based, scalable architecture.