Resumo em Inglês:

Abstract: In Global Navigation Satellite Systems (GNSS), the effects of neutral atmosphere in electromagnetic signal propagation impacts directly on the quality of the final estimated position, leading to errors in the metric order. Using an atmospheric model is a good strategy to minimize these errors, because it becomes possible to obtain a neutral atmospheric delay with the same spatial and temporal resolution, taking into consideration particularities of the atmosphere treated by a numerical model. The regional model of the Center for Weather Forecasting and Climate Studies (CPTEC) used in this paper has a spatial resolution of 15 km and a temporal resolution of 3 hours. Usually, the delay prediction of 3 hours is interpolated in time to GNSS applications and this can influence the quality of the values obtained in each interpolated epoch. Higher temporal resolutions can lead to lower errors in the final position. In this paper, the quality of delay predictions is evaluated for this atmospheric model with resolutions of 6 and 3 hours. The estimated delay, derived from meteorological data in the same location as the geodetic data, is considered as “truth”. The temporal resolution of 3 hours shows better results than using 6 hours, particularly for the hydrostatic component in the initial prediction period, RMSE of 1.25 cm was reduced to 0.2 cm in NEIA station.

Resumo em Inglês:

Abstract: In 2018, the Brazilian Institute of Geography and Statistics (IBGE) defined the normal height values and geopotential numbers for the High Precision Altimetric Network (HPAN), creating the need for developing procedures able to adapt works correlated with the Brazilian Geodetic System (BGS). In that context, and considering the state and municipal altimetric networks, it is necessary to estimate normal heights. To this end, this paper proposes to estimate normal height values and geopotential numbers for the Altimetric Network of the Federal District (AN-DF). Computational procedures involving the Least Squares Method were established and applied in a geometric levelling survey, which includes 200 stations distributed throughout the study area. The obtained results allowed estimating the normal heights of the network stations with accuracy up to 0.032m. However, because significant differences were found in the adjusted and known values of some used HPAN stations, we recommend a rigorous analysis of these stations before using them. In any case considering that the geometric levelling and the applied procedures were carried out correctly, it is suggested that the values estimated for all stations of the AN-DF in this work may be used.

Resumo em Inglês:

Abstract: The Global Navigation Satellite System (GNSS) is used for precise positioning applications, such as surveying and geodesy. The aim of the present work is to evaluate the effectiveness of web-based relative positioning (RP) and precise point positioning (PPP) GNSS post-processing services using measurements of different satellite visibility obstacles. Within this framework, static GNSS observations were conducted at three control benchmarks selected taking the impact of natural and human-made obstacles on satellite signals into consideration. 3 hours of static GNSS observations in Istanbul, Turkey were repeatedly obtained from three control BMs over six days and were evaluated through two RP (AUSPOS, OPUS) and three PPP (CSRS-PPP, Magic-PPP, GAPS-PPP) web-based GNSS post-processing services. The 6-day average of the three control benchmark coordinates computed using the Bernese GPS software v5.0, and were accepted as true results. They were compared to the local coordinates acquired through the RP and PPP web-based GNSS post-processing services. The different satellite visibility conditions were found to have significant effects on the GNSS point positioning solutions. We also found that web-based GNSS post-processing services provide easy and effective solutions for geodetic positioning applications.

Resumo em Inglês:

Abstract: Building roof extraction has been studied for more than thirty years and it generates models that provide important information for many applications, especially urban planning. The present work aimed to model roofs only from point clouds using genetic algorithms (GAs) to develop a more automatized and efficient method. For this, firstly, an algorithm for edge detection was developed. Experiments were performed with simulated and real point clouds, obtained by LIDAR. In the experiments with simulated point clouds, three types of point clouds with different complexities were created, and the effects of noise and scan line spacing on the results were evaluated. For the experiments with real point clouds, five roofs were chosen as examples, each with a different characteristic. GAs were used to select, among the points identified during edge detection, the so-called ‘significant points’, those which are essential to the accurate reconstruction of the roof model. These points were then used to generate the models, which were assessed qualitatively and quantitatively. Such evaluations showed that the use of GAs proved to be efficient for the modeling of roofs, as the model geometry was satisfactory, the error was within an acceptable range, and the computational effort was clearly reduced.