My research develops and applies geospatial methods that connect detailed observations—from UAVs, cameras, LiDAR and satellite sensors—with spatial models, machine learning and decision-support systems. The goal is to turn complex spatial data into evidence for safer structures, resilient landscapes and better-managed cultural and natural heritage.
Research themes
UAV photogrammetry and close-range sensing
Low-cost and high-resolution acquisition for structural inspection, surface change, open-pit monitoring, archaeological documentation and three-dimensional reconstruction.
Remote sensing and Earth observation
Multi-temporal satellite analysis of coasts, lakes, drought, climate indicators, land cover and environmental change using optical and SAR imagery.
3D geoinformation and BIM–GIS integration
Interoperable building and city models, IFC–CityJSON conversion, HBIM–GIS integration and three-dimensional geographic information systems.
GeoAI and spatial machine learning
Deep-learning object detection, explainable severity modelling, spatial risk analysis and data enrichment from UAV, satellite and geographic datasets.
Cultural heritage documentation
Photogrammetric, LiDAR and procedural methods for archaeological sites, historic buildings, digital preservation, HBIM and multi-hazard heritage assessment.
Spatial planning and resilient environments
GIS-based multicriteria decision support for urban accessibility, afforestation, adaptive reuse, green infrastructure and climate-responsive planning.
Methods
From sensing to spatial evidence
Photogrammetry · UAV sensing · LiDAR · Optical and SAR remote sensing · GIS and spatial statistics · Deep learning · Explainable machine learning · 3D city modelling · BIM/HBIM · Multicriteria decision analysis · Change detection · Spatial risk modelling