Valeria Croce


Ph.D. candidate in Civil and Environmental Engineering
Master Degree in Building Engineering and Architecture

based in Pisa, Italy

About me

I am a Ph.D. student attending the International Doctorate in Civil and Environmental Engineering, promoted by the University of Florence and the University of Pisa (Italy).
I graduated in October 2018 in Civil Engineering and Architecture at the University of Pisa, with final mark 110/110 cum laude, and I currently work at the Department of Geomatics of the University of Pisa.
I am member of the ASTRO Laboratory (Scientific and Topographic Applications for Operative Survey), and I am carrying out my Ph.D. thesis in co-tutelle with the Ecole nationale supérieure d’arts et métiers Paris-Tech, Aix-en-Provence and with the MAP research unit (Models and Simulations for Architecture and Heritage) of the CNRS of Marseille, France.
My main research domains concern the development of digital information modeling systems for Cultural Heritage objects, with a view on Heritage-BIM and reality-based annotation platforms.
In 2019, I won a scholarship from the Université Franco-Italienne, in the framework of the VINCI2019 project, to boost the collaboration between Italian and French laboratories on the topic of semantic enrichment of digital heritage models and annotation transfer and exchange between different representation systems.

My Ph.D. co-tutelle

  • ASTRO Laboratory, University of Pisa, Italy
  • DICEA, University of Florence, Italy
  • MAP Modèles et Simultations pour l’Architecture et le Patrimoine, CNRS Marseille
  • Ecole Nationale Supérieure d’Arts et Métiers, LISPEN Laboratory, Aix-en-Provence (FR)
  • VINCI2019 scolarship, Université Franco-Italienne

My main interests

  • Artificial Intelligence applied to Cultural Heritage
  • Architectural drawing
  • Surveying, 2D/3D digital replicas
  • Laser scanning and photogrammetry
  • Heritage-Building Information Modeling (H-BIM)
  • Scan-to-BIM reconstruction
  • Parametric and free-form modeling
  • Semantic segmentation of heritage data