This study aims to compare three different structured light scanner systems to generate accurate 3D human face models. Among these systems, the most dense and expensive one was denoted as the reference and the other two that were low cost and low resolution were compared according to the reference system. One female face and one male face were scanned with three light scanner systems. Point-cloud filtering, mesh generation, and hole-filling steps were carried out using a trial version of commercial software; moreover, the data evaluation process was realized using CloudCompare open-source software. Various filtering and mesh smoothing levels were applied on reference data to compare with other low-cost systems. Thus, the optimum reduction level of reference data was evaluated to continue further processes. The outcome of the presented study shows that low-cost structured light scanners have a great potential for 3D object modeling, including the human face. A considerable cheap structured light system has been used due to its capacity to obtain spatial and morphological information in the case study of 3D human face modeling. This study also discusses the benefits and accuracy of low-cost structured light systems.

Global Navigation Satellite System (GNSS) signals arrive at the Earth in a nonlinear and slightly curved way due to the refraction effect caused by the troposphere. The troposphere delay of the GNSS signal consists of hydrostatic and wet parts. In particular, tropospheric wet delay prediction and interpolation are more difficult than those of the dry component due to the rapid temporal and spatial variation of the water vapor content. Wet delay estimation and interpolation with a sufficient accuracy is an important issue for all parameters obtained by GNSS positioning techniques. In particular, in real-time positioning applications, errors caused by interpolation of the wet troposphere delay are reflected in the height component of about 1 to 2 cm. Furthermore, the amount of water vapor in the troposphere is very important information in weather forecast applications obtained as a function of wet delay. Therefore, real-time monitoring of the troposphere can be achieved with a higher resolution and accuracy by utilizing neural network models for interpolation of the wet tropospheric delay. In addition, in the absence of the GNSS station, wet delays can be interpolated by means of the surrounding stations to the desired location. In this study, a back propagation artificial neural network (BPNN) model based on meteorological parameters obtained from The New Austrian Meteorological Measuring Network (TAWES) was used to interpolate wet troposphere delay. Analysis was carried out at 40 reference stations of the Echtzeit Positionierung Austria (EPOSA) GNSS Network covering the whole of Austria. The interpolation of zenith wet delays based on the artificial neural network was performed by using latitude, longitude, altitude and meteorological parameters (temperature, pressure, weighted mean temperature, and water vapor pressure). These parameters were then subtracted from the artificial neural network model one by one and six different artificial neural networks were designed. In addition, the linear interpolation method (LIN) and inverse distance weighted interpolation method (IDW) were used as conventional interpolation methods. In order to investigate the effect of the network density on interpolation methods, three networks, including 40, 30, and 20 reference stations, were formed and the increased distance effect on interpolation methods was evaluated. In addition, analyses were conducted in winter, spring, and summer to evaluate the seasonal effects on interpolation methods. According to the statistical analysis, the root mean square error (RMSE) values of the IDW, LIN, and BPNN methods were found to be 12.6, 13.4, and 5.9 mm, respectively.

Antarctica plays a key role in the hydrological cycle of the Earth’s climate system, with an ice sheet that is the largest block of ice that reserves Earth’s 90% of total ice volume and 70% of fresh water. Furthermore, the sustainability of the region is an important concern due to the challenges posed by melting glaciers that preserve the Earth’s heat balance by interacting with the Southern Ocean. Therefore, the monitoring of glaciers based on advanced deep learning approaches offers vital outcomes that are of great importance in revealing the effects of global warming. In this study, recent deep learning approaches were investigated in terms of their accuracy for the segmentation of glacier landforms in the Antarctic Peninsula. For this purpose, high-resolution orthophotos were generated based on UAV photogrammetry within the Sixth Turkish Antarctic Expedition in 2022. Segformer, DeepLabv3+ and K-Net deep learning methods were comparatively analyzed in terms of their accuracy. The results showed that K-Net provided efficient results with 99.62% accuracy, 99.58% intersection over union, 99.82% precision, 99.76% recall and 99.79% F1-score. Visual inspections also revealed that K-Net was able to preserve the fine details around the edges of the glaciers. Our proposed deep-learning-based method provides an accurate and sustainable solution for automatic glacier segmentation and monitoring.