This study evaluates the performance of multi-constellation GNSS Precise Point Positioning (PPP) for offshore kinematic observations in the challenging environment of Antarctica using the data collected on a moving ship during the 6th Turkish Antarctic Expedition in 2022. The analysis involved two open-source PPP software solutions, PRIDE PPP-AR, and Ginan, which were used to process GNSS data from both GPS-only and multi-GNSS (GPS, GLONASS, Galileo, BeiDou) constellations. The results indicate that PRIDE PPP-AR generally provided better horizontal and vertical positional accuracy than Ginan software, achieving improvements of up to approximately 33% in height component root mean square error (RMSE) for both constellation solutions. While the mean value of horizontal position differences for PRIDE PPP-AR in multi-GNSS solution was 4 cm, while 5 cm for Ginan. The anticipated advantages of multi-GNSS over GPS-only solutions were not consistently observed for Ginan software in 2D position and height components. However, PRIDE PPP-AR demonstrated improved internal consistency with multi-GNSS solution, achieving an RMSE value of 5 cm for horizontal positioning compared to 6 cm for Ginan. The general results of the study reveal that the PPP technique, which does not require any reference station’s GNSS data, can achieve almost the same accuracy as the differential positioning technique. With this superiority, the PPP technique is an ideal positioning technique, especially in remote marine environments and polar regions, where geodetic infrastructure is inadequate and environmental conditions are difficult

The use of the Precise Point Positioning (PPP) technique has become very advantageous with the development of GNSS positioning technology. It is possible to get highly accurate position information without the need of any reference station data using...

The international service that ensures access to data and products of global navigation satellite systems (GNSSs), known as the IGS, runs a real-time service (RTS) project to support users who need real-time access to precise products. Thanks to the RTS project, it is now possible to obtain real-time precise point positioning (RT-PPP) solutions. RT-PPP can be used in many real-time positioning applications that require a high level of accuracy, efficiency, and flexibility, including earth sciences, atmosphere sciences, marine sciences, natural hazards, and many more. In this study, we tested the impact of different worldwide RTS products and satellite configurations on the performance of RT-PPP accuracy, as well as convergence time, in the Antarctic’s challenging environment and extreme atmospheric conditions. We applied RT-PPP solutions using real-time precise products (satellite orbit/clock corrections, and biases to conduct the real-time PPP) provided by the IGS and NAVCAST (a real-time PPP positioning service) based on different GNSS constellations: GPS-only, Galileo-only, and a combination of GPS and Galileo. In this way, the performance of two different real-time (RT) services was compared with each other. At the same time, the effectiveness of the Galileo global navigation satellite system for RT-PPP was also tested, and the Galileo system’s contribution to the GPS-only RT-PPP solution was investigated. The PPP-WIZARD software was used to process the corrections and GNSS data from a reference station in the Antarctic region. Although GPS-only, Galileo-only, and multi-GNSS solutions obtained from both RT services were found to have very close accuracy to each other, the combination of the GPS and Galileo systems produced better accuracy than when using the GPS system alone. According to the numerical results of this study, it was concluded that the real-time PPP technique gave promising results in such a challenging environment of the Antarctic region. However, we also observe that the RT-PPP technique requires a stable and robust internet connection, which might limit its usefulness in remote regions. Overall, we found the RT-PPP technique to be a viable alternative to conventional relative GNSS positioning techniques, especially in areas where continuously operating reference networks or similar networks are lacking.

Road networks are crucial to urban infrastructure and significantly affect transportation, traffic management, and emergency response. Besides, accurate mapping is essential for detecting road networks effectively, but traditional methods like manual digitization and field surveys often struggle in fast-changing urban environments. Remote sensing and deep learning techniques have emerged as effective alternatives, although initial road segmentation faced challenges such as limited image resolution. Recent advances in satellite technology have alleviated these issues by providing ultra-high-resolution (sub-meter) imagery, which is vital for accurately representing road networks. Deep learning models like U-Net have enhanced road segmentation by accurately capturing complex features. This research examines the effectiveness of multi-resolution satellite imagery for road segmentation. This study aims to analyze the accuracy assessment of road segmentation using Sentinel-2 imagery (10 m resolution) and ultra-high-resolution Pléiades Neo imagery (sub-meter resolution). Ground truth data from the Google Maps API were used for validation. Among the tested resolutions, Pléiades Neo at 30 cm achieved the highest accuracy, with an F-score of 0.87. Pléiades Neo at 15 cm resolution followed closely with an F-score of about 0.85. Pléiades Neo at 1 m resolution (upscaled data) showed a moderate decline (F-score of 0.82), while Sentinel-2 had the lowest performance (F-score of 0.78). Overall, Pléiades Neo at 30 cm resolution offers the best balance of accuracy and data efficiency for road segmentation.

Because of the inclined-orbit of GNSS constellations that are not cover the Polar Regions, the polar gaps occur between certain latitudes and therefore in these regions the satellite observations are limited around the zenith direction. In addition, from summer to winter season, the daylight and weather conditions vary tremendously in the Polar Regions. In the context of this study, the PPP accuracy performance was tested as a function of winter and summer seasons, GPS-only and GPS&GLONASS constellations, PPP-AR and PPP-Float solution strategies, static and kinematic processing modes, varying occupation times (1h, 2h, 4h, 8h, 12h and daily), and increasing latitudes towards the South Pole at the OHI3, ROTH, MCM4, and AMU2 GNSS stations in the Antarctica continent. Besides, the effect of the ambiguity solution strategies and the used constellations in the process on PPP convergence time was also examined. In the assessment results of the study, it was revealed that the PPP-AR strategy, additional GLONASS system to GPS constellation, and increased occupation times improved the static and kinematic positioning accuracy. Besides, although similar accuracies were obtained in both seasons, the position accuracy was slightly better in winter. Regarding the investigation on convergence time, the PPP-AR solution using the GPS&GLONASS constellations improved the convergence time by 66% comparing to the GPS-only PPP-Float solution. Finally, according to the assessment of the PPP-AR accuracy performance depending on the increasing latitude towards the South Pole, it has been observed that the 2D position accuracy remained stable for three stations except for AMU2. Besides, the vertical position accuracy decreased as it approaches the South Pole and the GLONASS system contributed to the improvement of the accuracy.