  Due to the environmental impacts caused by the construction industry,
repurposing existing buildings and making them more energy-efficient has become
a high-priority issue. However, a legitimate concern of land developers is
associated with the buildings' state of conservation. For that reason, infrared
thermography has been used as a powerful tool to characterize these buildings'
state of conservation by detecting pathologies, such as cracks and humidity.
Thermal cameras detect the radiation emitted by any material and translate it
into temperature-color-coded images. Abnormal temperature changes may indicate
the presence of pathologies, however, reading thermal images might not be quite
simple. This research project aims to combine infrared thermography and machine
learning (ML) to help stakeholders determine the viability of reusing existing
buildings by identifying their pathologies and defects more efficiently and
accurately. In this particular phase of this research project, we've used an
image classification machine learning model of Convolutional Neural Networks
(DCNN) to differentiate three levels of cracks in one particular building. The
model's accuracy was compared between the MSX and thermal images acquired from
two distinct thermal cameras and fused images (formed through multisource
information) to test the influence of the input data and network on the
detection results.
