## 3 Innovative SHM Techniques
### Image and Computer-Vision Based Approach
#### 3.2.1 Photogrammetry  
Photogrammetry is a powerful technique for determining metrical and qualitative information (e.g., colour and texture) regarding 3D objects and landscapes, starting with 2D photographs [149,150]. Photogrammetric reconstruction techniques are based on different CV algorithms, such as structure from motion (SfM) and multi-view stereo (MVS) techniques, and they generally include the following steps: selecting common key points in two or more photos; calculating camera positions, orientations, and distortions; and reconstructing 3D information by intersecting key point locations to determine where objects exist in 3D space [146]. Today, these steps can be achieved semi-automatically with the aim of software that generates 3D reconstructions of image-captured objects in the form of sparse 3D point clouds. Different photogrammetric approaches are available today, from terrestrial photogrammetry, which uses images from ground-based cameras (e.g., Figure 11a) to aerial photogrammetry, based on images acquired with airborne devices, such as aircrafts and satellites, and, more recently, unmanned aerial vehicles (UAVs) (e.g., Figure 11b) [151]. Several studies demonstrated the potential of combining data provided by UAV and terrestrial photogrammetry [152], as well as various terrestrial SHM methods [153,154]. Indeed, UAV photogrammetry easily reaches almost every part of the analysed structure, speeding up and optimizing subsequent work. On the other hand, the terrestrial survey allows for the use of high-performing optics and cameras, which curently cannot be easily installed on commercial drones because of their high weight [152]. Photogrammetry applications to CHS are increasingly being used to gather 2D and 3D geometric information and for SHM purposes, such as detecting and monitoring alterations and damages on structures and eventually recording them on 3D models [157,158]. In recent years, numerous research articles have been published on this topic. [PERSON] et al. [159] investigated the health condition of a historical bridge in Spoleto (Italy) through photogrammetric scanning performed by a drone. The high-resolution images acquired by the drone were post-processed using the SfM technique to reconstruct the two different 3D models of the bridge, with different point clouds density. The low-density mesh model was used to define a 3D FE model for investigating the dynamic properties through modal analysis. The high-density mesh model was employed to map the crack and damage pattern, which is useful for periodically checking and verifying damage evolution. The authors emphasized the aerial photogrammetry potentiality, mainly when large structures must be analysed cheaply and quickly. However, this technique cannot replace the strength and accuracy of other more expensive techniques, such as the use of laser scanners. [PERSON] and [PERSON] [160] developed a procedure to detect damage on 3D models reconstructed from photogrammetric scanning. The procedure is based on specific algorithms for detecting cracks or alterations using false-colour maps and morphological filters. This technique was tested and validated in a case study of a historical building monitored for 18 months. According to the authors, this technique could be considered proper support for the damage assessment compared to traditional survey techniques. [PERSON] et al. [161] compared the results obtained from an ultra-lightweight drone (less than 300 g) equipped with a low-cost camera with 3D laser scanning to acquire geometrical data, as well as material and damage information from a huge historical building facade in Bologna (Italy), characterized by a very narrow operating space. The authors concluded that the UAV's photogrammetry survey, even though of inferior quality, can lead to almost complete acquisition of buildings placed in difficult urban environments, where both ground photogrammetry and laser scanning would provide unsatisfactory results. [PERSON] et al.