3D Data Fusion for Historical Analyses of Heritage Buildings Using Thermal Images: The Palacio de Colomina as a Case Study

[EN] In the framework of built heritage monitoring techniques, a prominent position is occu- pied by thermography, which represents an efficient and non-invasive solution for these kinds of investigations, allowing the identification of phenomena detectable only in the non-visible range of the electromagnetic spectrum. This is of extreme interest, especially considering the possibility of integrating the radiometric information with the 3D models achievable from laser scanning or pho- togrammetric techniques, characterised by a high spatial resolution. This paper aims to illustrate how combining different geomatics techniques (in particular, by merging thermal images, laser scanning point clouds, and traditional visible colour photogrammetric data) can efficiently support historical analyses for studying heritage buildings. Additionally, a strategy for generating HBIM models starting from the integration of 3D thermal investigations and historical sources is proposed, concerning both the multi-temporal modification of the volumes of the building and the individual architectural elements. The case study analysed for the current research was the Palacio de Co- lomina in Valencia, Spain, a noble palace¿now the headquarters of a university¿that, during the last few centuries, has been subjected to considerable transformations in terms of rehabilitation works and modification of its volume.This research was funded by the Italian Ministry of Education, University, and Research (MIUR) through a PhD research scholarship (doctoral programme in Architectural and Landscape Heritage), and by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie, grant number 101032333.Patrucco, G.; Gómez Gil, AM.; Adineh, A.; Rahrig, M.; Lerma, JL. (2022). 3D Data Fusion for Historical Analyses of Heritage Buildings Using Thermal Images: The Palacio de Colomina as a Case Study. Remote Sensing. 14(22):1-23. https://doi.org/10.3390/rs14225699123142