My research combines geotechnical engineering, spatial computing, and engineering digitalisation. My core focus is on developing automated methods for rock mass characterisation using terrestrial LiDAR and drone photogrammetry, converting raw 3D point cloud data into structured geotechnical information without manual intervention.

Key research interests include:

• Automated discontinuity mapping: extracting joint orientation, spacing, and roughness from laser scan data using computer vision and Fourier-based analysis methods
• Photogrammetry for geotechnical assessment: applying structure-from-motion and UAV imagery to pit wall mapping, underground inspection, and terrain change detection
• LiDAR processing pipelines: developing open, reproducible workflows for classification, feature extraction, and volumetric analysis of large-scale point cloud datasets
• Digitalisation of geotechnical workflows: automating data collection, processing, and reporting pipelines for mining and civil engineering applications, reducing reliance on manual field interpretation
• AI and machine learning for rock characterisation: semantic segmentation and classification of subsurface features from 3D spatial data

Applied contexts include underground hard rock mining, open pit geotechnics, and civil infrastructure assessment. A parallel interest is the development of web-based and desktop platforms that make these spatial datasets accessible and interpretable to engineering teams without specialist software.