Road condition analysis is an important research topic in many fields (such as intelligent transportation, road safety, road design analysis, and traffic analysis) and depends on road geometry parameters such as longitudinal profile and cross-slope. In this study, the extraction of road geometry parameters by unmanned aerial vehicle (UAV) with LiDAR and by a mobile photogrammetric system (MPS) designed by our research group was investigated. The purpose of this study was to obtain geometric parameters (such as road longitudinal profile and cross-slope) by using digital terrain model (DTM) surfaces derived from point cloud data acquired using UAV-LiDAR and MPS. For this purpose, a framework was developed for the extraction and comparison of longitudinal and cross-sectional profiles. First, the ground filtering approach was used to extract ground points and DTM surfaces generated from an appropriate interpolation algorithm by using ground points. Cross-sectional/longitudinal profiles of the road sections were extracted and compared with reference data. A comparison of the longitudinal profiles obtained from DTMs derived from the MPS and from UAV-LiDAR revealed root mean square error values of 1.8 cm and 2.3 cm, respectively. The average deviation of cross-slopes for both surfaces was 0.19% and 0.18%, respectively. These results show that road geometric parameters can be obtained from DTM surfaces with high accuracy. It can be concluded from the results of this study that MPS can be a favorable alternative for studies on road geometry parameters extraction.

SUMMARY: The Global Positioning System (GPS), although it has existed for only 30 years, is an important source for active tectonics, resulting in estimates of plate motions very close to geologic estimates over millions of years. GPS is also used for elastic block models to calculate slip rates for a better understanding of Earth’s active crustal deformation. GPS-derived velocity fields may be used as the basis for clustering analysis to create a preliminary definition of block geometry. In this study, we used published horizontal velocity fields to evaluate the effects of data dependences on determining the optimum number of clusters with algorithms. For this purpose, we used different variations of velocity fields in Turkey and tested four different algorithms that are Davies–Bouldin index, the elbow method, GAP statistics algorithm and the silhouette method. We also clustered velocity components with the k-means technique and compared the results with previous studies.

Vital aspects of transportation networks, such as the extraction of road information and analysis of road conditions, have become increasingly important research topics as they outline the foundation of many applications such as high-precision mapping, infrastructure planning and maintenance, intelligent transportation, or road design analysis. Therefore, regularly obtaining accurate high-density point cloud data of infrastructures supports many transportation-based applications and provides up-to-date information for smart cities or digital twins. Low-cost smartphone platforms equipped with a variety of sensors provide new and powerful data acquisition capabilities that can be exploited in the geospatial field. For example, mobile phones are now capable of collecting valuable data to generate accurate models to support digital reconstruction of infrastructures. These platforms can provide simple and effective data acquisition, while offering useful geospatial data that can be an alternative to traditional measurement techniques. However, the sensor performance with respect to spatial accuracy of point clouds generated in different applications have not yet been fully investigated. Thus, this paper evaluates the feasibility of using the point clouds generated by the built-in camera and LiDAR sensors integrated into iPhone 14 Pro for extracting road-related information. Additionally, the use of the viDoc RTK Rover on the iPhone 14 Pro increases the platform positioning accuracy, consequently improving the georeferencing accuracy of the point clouds. To validate the performance of the point clouds obtained by the iPhone 14 Pro, a reference dataset of the road features was obtained by measuring with a single-point RTK-GNSS receiver, receiving corrections from the Turkish CORS network (TUSAGA-Aktif) which provides two to three centimetres of accuracy. In addition, reference point cloud data over the same area was obtained from different platforms such as Mobile LiDAR and UAS, and the road features were extracted from these dataset and performance validated. The data acquired by the iPhone 14 Pro was processed and evaluated with respect to the reference datasets. The advantages and disadvantages of using iPhone 14 Pro are analysed in detail and the findings are reported.

3D as-built models play a crucial role in urban dynamics within technical infrastructure projects. Enabling 3D data analysis in dynamic urban areas is advantageous for construction processes and management tasks, including production monitoring and layout planning. Point clouds represent a scene by deploying a collection of 3D points that include both the position and color information of each point. It is crucial to assign semantic information to these points, known as point cloud classification (PCC), for the documentation and monitoring of the 3D environment. Machine learning (ML) classifiers that use multiscale geometric features of each point are frequently utilized for point cloud classification (PCC). This study aims to assess the classification performance of ML classifiers in complex infrastructure domains and determine the geometric characteristics that are most effective in identifying target scenes. Random Forest (RF), eXtreme Gradient Boosting Machines (XGB), and Light Gradient Boosting Machines (LGBM) classifiers were utilized in two case studies. Based on the experimental tests, XGB demonstrated greater accuracy than RF and LGBM. Moreover, despite employing distinct approaches, these classifiers were found to generally agree on the importance of specific geometric features. Our findings offer guidance for classifying technical infrastructure elements in real-world scenarios.

The increasing population and accordingly the new settlement ultimately need for the utility. Location and attribute information of utilities significantly affect the maintenance, repair and construction of utilities. The lack of such information causes accidents resulting in material and moral damages in utility excavations ‘especially in the city’. The lack of information on utility works in our country raises problems such as damage to another utility during the work, being unable to complete the work on time, affecting vehicle and pedestrian traffic. This study has focused on the detectability of utilities with unknown location and attribute (diameter) using a ground penetrating radar (GPR) based on simulation model and field surveys. An antenna frequency of 300 MHz was chosen in both applications. The analysis of the data obtained from the simulation model revealed positive results in terms of the usability of the GPR for the determination of location and attributes in utility works. Furthermore, the results of the field studies demonstrated that if the dielectric constants of a utility element and of its location are close, data on the utility cannot be obtained; if the trench base is narrow, it gives a hyperbola reflection like pipeline; and if it is not analyzed carefully, this pseudo- reflection may lead to mistakes. The field study shows that if there are no continuous hyperbola reflections on consecutive radargrams, the possibility that the reflection may not be due to pipeline should be considered.

The main purpose of this paper is to present the results of external’ quality tests for global, regional and local geoid models. In this context, available geoid models were evaluated for the height transformation in three different geographic locations in Turkiye on the basis of ground truth GNSS/Leveling data. The differences between observed and computed quantities were investigated. The comparison results of the global, regional and local geoid models for three test areas were presented. The tested geoids are the national geoid model Turkiye-Geoid2003 (TG-03) released by the General Command of Mapping in 2003 which was computed using land and sea gravity data, topographic heights from digital terrain model and GPS/Leveling data, including EGM96 global geopotential model for long wavelengths, the ultra-high resolution model EGM2008 that was released by the US National Geospatial Intelligence Agency, the refined versions of these two geoid models and the GNSS/Leveling derived geometric models produced by the metropolitan municipalities. The comparison of results show that TG03 and EGM2008 models provide about 2 times lower accuracy than the precise local geoid models. In addition, the RMS values of the other two methods (IMPTG03 and IMPEGM2008) compared in the study were obtained as 2.45cm (Istanbul), 5.09cm (Izmır) and 4.77cm (Bursa) for IMPTG03, 2.61cm (Istanbul), 5.45cm (Izmır) and 5cm (Bursa) for IMPEGM2008. It seems that the results are sufficient for many engineering applications in the local areas and EGM2008 can be reach the accuracy of regional or local geoid models after the improvement procedure.