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.

The integration of survey-grade Global Navigation Satellite Systems (GNSS) with photogrammetric measurement studies conducted with an aircraft has the potential to meet the need for the use of ground control points (GCP). In photogrammetric studies, there are different GNSS techniques to provide the precise positioning of the aircraft. The precise point positioning (PPP) technique has become an alternative method to post-processing kinematic (PPK) methods in the evaluation of GNSS data gathered with an aircraft at high speed. In this study, GNSS data gathered from two different flights conducted for photogrammetric surveys in Izmir, Turkey were processed with different software (RTKLIB, gLAB, CSRS-PPP, and GRAFNAV) through PPP technique with the ambiguity resolution (PPP-AR) method to analyse the performance of this method versus PPK. Three-dimensional (3D) positioning analyses of coordinate differences obtained from mapping flights demonstrated a variation pattern mainly between 0 and 6 cm for both kinematic PPP and PPK results.

In today's era, the address plays a crucial role as one of the key components that enable mobility in daily life. Address data are used by global map platforms and location-based services to pinpoint a geographically referenced location. Geocoding provided by online platforms is useful in the spatial tracking of reported cases and controls in the spatial analysis of infectious illnesses such as COVID-19. The first and most critical phase in the geocoding process is address matching. However, due to typographical errors, variations in abbreviations used, and incomplete or malformed addresses, the matching can seldom be performed with 100% accuracy. The purpose of this research is to examine the capabilities of machine learning classifiers that can be used to measure the consistency of address matching results produced by online geocoding services and to identify the best performing classifier. The performance of the seven machine learning classifiers was compared using several text similarity measures, which assess the match scores between the input address data and the services' output. The data utilized in the testing came from four distinct online geocoding services applied to 925 addresses in Türkiye. The findings from this study revealed that the Random Forest machine learning classifier was the most accurate in the address matching procedure. While the results of this study hold true for similar datasets in Türkiye, additional research is required to determine whether they apply to data in other countries.

In this study, the authors conducted a series of test measurements in wooded and urban areas and analyzed the results for three main objectives. The first objective is to compare the execution of the Loosely Coupled (LC) and satellite-based solutions in terms of accuracy. Compared to satellite-based solutions, the findings confirmed that the LC-based solutions enhanced accuracy by 1 cm in position and 6-7 cm in height components in the wooded area. In the urban area, LC-based solutions improved the position and height accuracies up to 6 cm and 44 cm, respectively. Also, LC-based solutions bridged the gaps and created a seamless solution in which the gaps reach almost 30% in the urban area trajectory. Secondly, the authors investigated the performance of the GPS-based and GNSS-based solutions. In the wooded area, the GNSS-based solution delivered 2 cm better accuracy in both position and height components than the GPS-based solution. In the urban area, the GNSS-based solution improved the accuracies up to 8 and 36 cm in position and height components, respectively. Also, the solution availability of the GNSS-based process is 10% better than the GPS-based solution. The third objective of this study is to test the performance of the PPP and PPK-based solutions in the two test areas. PPK-based solutions outperformed only 2 cm in position and height components compared to the PPP-based in the wooded area; however, in the urban area, the PPK-based solution improved the accuracies 4-5 dm and 1.1-1.5 meter level in position and height components, respectively. These results indicate that the PPP-based solutions offer a similar level of accuracy to the PPK-based solutions in the wooded area where the satellite visibility is high throughout the trajectory. However, the PPK-based solution provided better positioning accuracies in the urban environment with limited satellite visibility.

Despite GNSS (Global Navigation Satellite System) enables positioning, navigation and timing (PNT) almost everywhere, the development of applications like self-driving vehicles and indoor navigation requires extending accurate positioning to scenarios where GNSS either is not reliable or does garantee a sufficiently precise solution. Integrating inforamtion provided by different sensors is commonly accepted to be a quite viable way for such extension. In particular this work is part of a project aiming at investigating the positioning performance that can be obtained by integrating vision with radio-based systems and inertial sensors, which are commonly installed on many smart devices, such as smartphones. Furthermore, this work considers positioning in a collaborative scenario, where different interconnected platforms, i.e. unmanned aerial vehicles and pedestrians provided with smartphones, are moving on the same area. The results obtained in the considered tests show a good potential (submetric 2D positioning error) for what concerns the implemented strategies, where the integration of different technologies can ensure decent performance in a wider range of working cases.