## 4 Critical Literature Review
Some techniques used in machine learning for this purpose were support vector machine (SVM) and random forest (RF) [80; 81]. Deep learning uses neural network architectures such as convolutional neural networks (CNN). CNNs can learn the characteristics and representation of the pixel values of an image automatically and can give results in real time. They require less training if a sufficient number of starting datasets are assumed [82]. [PERSON] et al. (2018) and [PERSON] et al. (2019) trained several CNNs with images of different resolutions obtaining more accurate results in building damage classification by merging several resolution layers [43; 44]. These techniques optimize the process of identifying structural damage in images captured by UAVs. The use of UAVs to assess urban damage in emergency situations after natural disasters such as floods [83], tornadoes and hurricanes [84] and earthquakes [85], has also proved to be very useful because of the feasibility of remote sensing for search and rescue tasks and estimates of material and economic losses [86]. In addition to high-resolution digital RGB cameras for aerial image acquisition, there are other types of payloads that allow data to be obtained by laser or radiation. The optical sensors which use range data like laser scanning technologies (for example, the LiDAR--Light Detection and Ranging) allow generate point clouds with 3D coordinates. The mapping is achieved through laser pulses towards the target and the measurement of its reflection with a sensor. The literature focused on the generation of point clouds and 3D models of buildings and terrains obtained more detailed results with laser scanning technology than with aerial photogrammetry techniques [87; 68]. [PERSON] et al. (2019) applied terrestrial laser scanning and photogrammetry techniques to cultural heritage buildings for classification, maintenance and surveillance. They compared both techniques and stated that laser scanning provides higher positional accuracy than photogrammetry [88]. These techniques has been commonly used for documentation and maintenance of historical heritage buildings [89; 90; 91; 88]. Laser scanning technologies also proved their viability in urban planning tasks. High resolution remote sensing is an effective instrument for the production of three-dimensional digital models of cities that can be useful in the field for cadastral mapping and monitoring of public spaces in use [92; 93]. Through the use of UAVs and LiDAR, precise urban maps were generated using 3D point clouds with segmentation techniques for the rapid identification of structures such as streets, facades, cars, trees, etc. [94]. Diagnostics of accessibility to urban environments were carried out by automatically detecting and classifying elements of urban land (roads, pavements, steps, risers and kerbs) based on 3D models processed with segmentation and machine learning techniques [95]. These techniques were supported by BIM to reduce calculation time in the creation of city information models (CIM) [96]. Some building sustainable new methodologies highlight the importance of monitoring the use period of the building to maintain their energy efficiency [97, 98]. Advanced tools such as infrared cameras in UAVs allow professionals to perform energy inspections in buildings quickly and accurately, while reducing operational costs and minimising security risks. Using UAVs, energy inspections can be carried out to document the energy performance of buildings, visualise heat transfer using infrared images and create digital models through thermal patterns using 3D photogrammetry [99]. Thermal cameras capture the infrared energy emitted by the surface of an object [100]. This type of energy is absorbed and emitted by all materials and is directly related to the temperature of the object [101]. The most common indicator of anomalies, defects or failures in any physical element is the change in temperature, something easily identifiable with this type of cameras.