Geomatics for heritage

In line with the evolving role of spatial and geographic dimensions in many fields of knowledge and in the management of human activities for the land, Geomatics oriented towards heritage now represents a resource to support knowledge, analysis, and forecasting of future dynamics related to the built heritage and landscape.

Research trajectories within the DPA focus on the study of 3D metric survey methods based on images (also multispectral) and/or distances (LiDAR) as primary data, acquired from aerial, terrestrial, or submerged platforms; innovative techniques, such as Artificial Intelligence (AI), support applications and models always from the perspective of user-oriented fruition. Research can also focus on computer management systems based on georeferenced spatial databases, belonging to the GIS or HBIM fields, including intelligent systems identified as Digital Twins.

The approaches are brought together by objectives that promote and develop criteria and tools for knowledge, valorisation, and/or conservation projects for heritage (from the urban aggregate to complex architectural structures and the detail scale) and aimed at a continuous dialogue with other disciplines in the doctoral course.

ERC sectors

  • PE8_3 - Civil engineering, architecture, maritime/hydraulic engineering, geotechnics, waste treatment 
  • PE6_8 - Computer graphics, computer vision, multi media, computer games 
  • SH5_13 - Computational Modelling and Digitisation in the Cultural Sphere 
  • SH2_12 - GIS, spatial analysis; big data in political, geographical and legal studies
  • PE2_17 - Metrology and measurement

Key words

 

  • CH (Cultural Heritage) 3D survey and modelling
  • Information systems (HBIM – Heritage Building Information Modelling and GIS  - Geographical Information Systems/Science)
  • UAV, close-range, and underwater multi-sensor photogrammetry (visible, thermal, multi-spectral)
  • LiDAR ((Light Detection and Ranging) Technologies and MMT (mobile mapping technologies)
  • Artificial intelligence approaches for Cultural Heritage (deep learning/machine learning techniques)