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Two commonly used programming languages in geosciences are R and Python. Both languages come with inherent advantages and disadvantages. While Python is a generalpurpose language with a readable syntax, R is built by statisticians and encompasses their specific language. In contrast with more nominative and static programming languages, like C++ or Java, both R and Python are heavily used in non-informatics disciplines, due to their relative ease of use. Notwithstanding the lower performance of both languages compared to the aforementioned languages, they allow fast prototyping and implementation of algorithms in research contexts. However, depending on the aim of the research, (geo-)scientists will frequently decide between using Python, favoring replicability and accessibility, or R, containing fast-programmable cutting-edge reporting tools. One of the main differences between both languages is that, for Python, with five libraries one can complete most research (Numpy, Pandas, Scipy, Scikit-learn, and Matplotlib), while for R, one should find the right package in the +12.000 available packages on CRAN. The availability of the many packages for R is at the same time one of its greatest strengths. In this research, three common analyses in geosciences are performed in both R and Python. The first analysis highlights the unsupervised classification of (Sentinel-2) satellite imagery. The second series of analysis concerns the transformation of planimetric coordinates from one coordinate reference system to another system. As a final analysis, a series of standard topographic parameters are derived for an equidistant digital elevation model extracted from ASTER- and SRTM- data (EU-DEM). Next to the quantitative benchmarking of the processing time for each analysis, the output of each calculation is investigated as a qualitative benchmark, with a focus on the comprehensibility of the numerical/character output and the visualizations. [ABSTRACT FROM AUTHOR]

Digital elevation models play a crucial role in the analysis of complex spatial challenges. Given the international dimension of many environmental applications, elevation data have been integrated in the European INSPIRE directive (Annex II, Elevation theme). As a result, many types of data are offered in various formats by mapping agencies and governmental organizations within the European Union. Notwithstanding the national effort to publish these data under open and public licenses, and in compliance with international data standards, the models are often projected in national coordinate reference systems, containing local orthometric heights. In order to use these models for cross-boundary applications, a transformation of locally projected coordinates and local elevations to international coordinates and ellipsoidal heights is required. Planimetric coordinate transformations are regularly performed using analytical conversion models, whereas altimetric transformations make use of geoidal models or simple vertical shifts. In this paper, digital elevation models from the Netherlands and the Flemish Region (Belgium) are transformed using the corresponding geoidal transformation grids and a simple vertical shift. Both data sets are independently acquired and processed within the framework of governmental mapping campaigns. The projected Dutch RD coordinates (EPSG:28992) and Belgian Lambert ’72 coordinates (EPGS:31370) are converted to ellipsoidal WGS84 coordinates (EPGS:4326) using open-source OGR tools. In order to obtain ellipsoidal heights from the elevation models, NLGEO08 and hBG18 are used for the conversion of Dutch and Belgian elevations respectively. Based on a quality assessment of both data sets in overlapping areas, it is stated that the mean difference and standard deviation of the elevations are within the accuracy levels of the used elevation models (0.07 ± 0.16 m). [ABSTRACT FROM AUTHOR]