Romania, due to its historical background, has many vestigial belongings to the cultural heritage, out of which the most noticeable (by number, age, architecture, symbolism and representation) are the wooden churches. Under these circumstances the role of the current study is to emphasize certain practical aspects (focused on the need to assess, preserve, restore and valorize) regarding the use of modern technology based on photogrammetry and 3D scanning. The case study was represented by the “Saint Martyrs Constantin Brȃncoveanu and His Sons” wooden church from Oradea Municipality, Bihor County, Romania; the edifice being included on the list of historical monuments ever since 2010. The final goal being to create 3D models for both the exterior and interior of the monument for research, restoration in case of need, sharing for education, entertainment, tourism purposes etc.

Aim of the study: Today, completely new forms of geo-information systems are becoming increasingly popular. New technological possibilities allow these systems to be adapted to the requirements and needs of societies. This paper is an extensive literature review of the use of geodata in various scientific fields, in STEM (science, technology, engineering, and mathematics) disciplines. However, as there is no universal agreement as to which disciplines are included in the STEM, the authors also included related fields such as geography or transport. Material and methods: Already a preliminary analysis of the Web of Science Core Collection database has shown that geodata is used very widely, in every scientific discipline analysed (with varying degrees of sophistication). The main objective of the paper is to provide a comprehensive analysis of the usage of geodata in five areas: bathymetric measurements, satellite geodata, aerial retrieved geodata, levelling networks, and GIS data. Results and conclusions: The results showed the increasing availability of data that can contribute to a better understanding and management of our planet. Geodata as a tool is overly broad and general, and it is used or might be used in every discipline of science.

This study evaluates the EUREF Permanent Network (EPN) station position time series of approximately 200 GNSS stations subject to the Repro 2 reprocessing campaign in order to characterize the dominant types of noise and amplitude and their impact on estimated velocity values and associated uncertainties. The visual inspection on how different noise model represents the analysed data was done using the power spectral density of the residuals and the estimated noise model and it is coherent with the calculated Allan deviation (ADEV)-white and flicker noise. The velocities resulted from the dominant noise model are compared to the velocity obtained by using the Median Interannual Difference Adjusted for Skewness (MIDAS). The results show that only 3 stations present a dominant random walk noise model compared to flicker and powerlaw noise model for the horizontal and vertical components. We concluded that the velocities for the horizontal and vertical component show similar values in the case of MIDAS and maximum likelihood estimation (MLE), but we also found that the associated uncertainties from MIDAS are higher compared to the uncertainties from MLE. Additionally, we concluded that there is a spatial correlation in noise amplitude, and also regarding the differences in velocity uncertainties for the Up component.

The seasonal signal determined by the Global Navigation Satellite System (GNSS), which is captured in the coordinate time series, exhibits annual and semi-annual periods. This signal is frequently modelled by two periodic signals with constant amplitude and phase-lag. The purpose of this study is to explore the implication of different types of geophysical events on the seasonal signal in three stages—in the time span that contains the geophysical events, before and after the geophysical event, but also the stationarity phenomena, which is analysed on approximately 200 reference stations from the EPN network since 1995. The novelty of the article is demonstrated by correlating three different types of geophysical events, such as earthquakes with a magnitude greater than 6° on the Richter scale, landslides, and volcanic activity, and analysing the variation in amplitude of the seasonal signal. The geophysical events situated within a radius of 30 km from the epicentre showed a higher seasonal value than when the timespan did not contain a geophysical event. The presence of flicker and random walk noise was computed using overlapping Hadamard variance (OHVAR) and the non-stationary behaviour of the time series of the CORS coordinates in the time frequency analysis was done using continuous wavelet transform (CWT).

For the error analysis of the GPS velocity component we have to consider many factors, including the determination of the stochastic model. In this article we intended to analyse the results obtained by using data gathered during a period of 18 months. The presence of the noise contained within the GPS station coordinate time series is recommended to be fitted by using a linear trend to the coordinate’s time series and after this procedure to model the noise properties of the residuals. For the noise determination we used a first-order Gauss-Markov model – FOGM. To see the behaviour of the chosen noise model, we used the power spectra density of the residuals and the log likelihood values and we compared the results with those from the power law plus white noise model. In the estimation process we used the semiannual sinusoidal signal to express the seasonal influence. The amplitudes and phase lag of the seasonal variation were also computed. Although the noise that has a large contribution in a relatively short data span – shorter than 2.5 years – is considered to be white noise, using the average we have shown that the presence of coloured noise has to be taken into account.