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The generation of 3D models of buildings has been proved as a useful procedure for multiple applications related to energy, from energy rehabilitation management to design of heating systems, analysis of solar contribution to both heating and lighting of buildings. In a greater scale, 3D models of buildings can be used for the evaluation of heat islands, and the global thermal inertia of neighborhoods, which are essential knowledge for urban planning. This paper presents a complete methodology for the generation of 3D models of buildings at big-scale: neighborhoods, villages; including thermographic information as provider of information of the thermal behavior of the building elements and ensemble. The methodology involves sensor integration in a mobile unit for data acquisition, and data processing for the generation of the final thermographic 3D models of urban environment.

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The application of Deep Learning (DL) models using the measurements acquired by Non-Destructive Testing (NTD) tools as input data stands as a versatile solution for highly automated analysis. However, DL models using thermal images as input data are quite scarce when it comes to analysing defects in medium-and large-scale bodies. Therefore, this paper proposes the application of a thermal criterion and a DL model, Mask R-CNN, in thermal images acquired from different infrastructures with thermal bridges and moisture. The thermal criterion is first applied to the input data, showing its utility to improve DL models performance.

This work constitutes a novel contribution of engineering and the university, carried out through analysis of the text content from the main sources of information on climate change and its effects, consisting of the declarations of the major Climate Summits (COP.1 to COP.22) and the reports of the IPCC (Intergovernmental Panel on Climate Change, 1990, 1995, 2001, 2007 and 2014).