## 3 Application of Advanced Surveying Methods (_Advanced Surveying_)  
The introduction of modern surveying tools has significantly expanded the collection of precise and detailed digital geospatial data on infrastructure projects. For instance, surveying tools such as Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicles (UAVs) have enabled the whole construction workflow to proceed in a model-centric format, ranging from storing pavement structural condition data to improving inspection operation performance schedules [29; 31; 42; 60; 61; 62]. Studies have discovered that most digitally archived asset information from both pre-and post-construction can be critical for surveying and future project developments [63; 64; 65]. Employing LiDAR for capturing faster and more accurate geospatial data from construction sites provides substantial advantages over the existing traditional methods [55; 66; 67]. LiDAR is proven to be very practical in situations such as bridge mapping, pavement-subsidence measurements, road detection, etc. [48; 68; 69]. Mounted-on-drones are remotely operated airborne vehicles that are equipped with high-resolution cameras to capture detailed visual data for scoping and surveying operations [54]. Several UAV utilizations in the construction industry include those for project assessments, aerial surveys, site mapping, enhanced 3D modeling [70], and vehicle tracking [71]. In the field of transportation infrastructure management, UAVs can be useful for condition assessment to capture aerial pictures from pavement surfaces due to their high flexibility, portability, and cost-effectiveness [72; 73; 74; 30; 75; 76]. Digital images concerning pavement distress such as cracks, rutting, or patching failure can be captured by mounting a high-resolution camera on a drone from a fixed height [77; 78]. Researchers and practitioners have used less expensive substitutes such as mounted cameras on vehicles and smartphones to capture pavement conditions [79; 80]. Subsequently, image-processing techniques were adopted to detect potholes and pavement distresses on the surface of the pavements [81; 82; 83; 84; 85; 86]. It is worth mentioning that innovative approaches such as video processing, laser scanners, and point cloud data were also utilized to detect, generate, and evaluate 3D information models of the terrain condition [53; 87] and roadway elements [33; 40; 58; 88; 89]. A new method utilizing a mobile laser scanner (MLS) has been presented as providing more information including road segmentation, potential crack point detection based on point elevation, crack point clustering using a region-growing algorithm, and extraction of crack geometric attributes [34]. The vertical and horizontal clearances of highway viaducts and gantries can be automatically estimated using MLS point clouds for routing oversized transport items, infrastructure reconstruction, maintenance, and settling legal claims after incidents [90]. The point cloud data of road infrastructure was mapped via the built-in camera and LiDAR sensors integrated into the iPhone 14 Pro using real-time kinematic positioning systems, which consequently improved the geo-referencing accuracy [91]. For the maintenance of existing railway infrastructure, a platform that combines 2D panoramic virtual reality photos and a 3D model generated using a 3D scanner has also been demonstrated [18]. The possibility of settlement, which is a maintenance condition evaluation item for fill-dam bodies, was investigated using point clouds based on the Unmanned Aerial System (UAS) from motion and terrestrial laser scanner point clouds [19]. An automated technique employed 3D point cloud tiles with trajectory points to construct Industry Foundation Class (IFC) models of roadways and determine their alignment and width [92]. Table 2 presents a thorough comparison of various technological tools utilized for surveying purposes, based on documented literature.