## 4 Critical Literature Review
Facing this problem, filtering methods about semantic segmentation and image stitching techniques were applied to automatically recognize and filter out images containing important objects [47; 62]. Another challenge is the variety of this type of scenario, which is constantly changing with temporary resources such as cranes and materials. UAVs are unable to track the location of dynamic objects on the construction site, limiting the safety inspection functions. In addition, a continuous process of capturing and processing images is required to provide performance information that must be communicated to the professionals who manage the construction project. It must be a fast process in order to get useful results in a real time change control [12]. For this reason, these types of tasks have been combined with building information modelling (BIM) systems which can provide preliminary information on the geometry and appearance of the views. BIM is a methodology for creating digital design simulations, managing all the information involved in an architectural project in a coordinated manner. These 3D models can be enriched with information acquired by UAVs about performance, time, costs, safety and productivity in construction sites. The combination of digital documentation acquired by an UAV together with BIM has demonstrated positive opportunities for the execution of daily work, short-term planning and communication and coordination between site teams [46; 63]. Risk prevention and improvement of safety on construction sites is another field in which great results have been obtained using both techniques [48; 49; 64; 65]. [PERSON] et al. (2015) presented a novel method to automatically identify fall and collapse hazards related to excavations on construction sites. Once the danger points were identified, the BIM was redesigned with an installation of safety equipment [48]. Once the work has been completed, the UAVs are still useful for monitoring and inspecting buildings in use. The usual photogrammetric techniques are applied to buildings-in-use to generate three-dimensional, orthophotos and orthomosaic models of facades and roofs affected by various pathologies [66; 67; 68; 69; 70]. The newest methodologies are applied to the analysis of the structural health of buildings in other complex environments where there is no GPS coverage [71]. Building monitoring to assess structural damage using UAVs takes on a key role in the field of remote sensing because of the rapid and effective response that these systems can provide to assess the ageing and deterioration of damaged buildings [72]. The main challenge of using images as a basis for the damage assessment process lies in the subjectivity of manual identification of damage from remote sensing. For example, damage can be seen on the envelope of a building that does not compromise its structural health. For this reason, new methods have been designed to extract more information from remote sensing data in a reliable and automated way [43]. Segmentation techniques and object-based approaches were subsequently applied to the three-dimensional models to detect vectors, shapes, textures and segments of the photographed buildings [73; 74; 75; 42]. Methods such as object-based image analysis (OBIA) became popular because of their ability to incorporate image elements such as colours, sizes, shapes, textures and patterns in the segmentation process [76]. They were applied directly to images to extract more detailed characteristics of cracks and holes affecting structural elements of a building [77]. In the most recent publications, machine learning and deep learning are the most widely used methodologies [78; 79]. The algorithms used in machine learning allow complex patterns to be identified through a large number of parameters. These algorithms parse data and constantly train them with the input of new variables to predict future scenarios and to acquire information automatically.