Patrick Schratz

TL;DR: In this article, the effects of spatial autocorrelation on hyperparameter tuning and performance estimation by comparing several widely used machine-learning algorithms such as boosted regression trees (BRT), k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) with traditional parametric algorithms, such as logistic regression (GLM) and semi-parametric ones like generalized additive models (GAM) in terms of predictive performance.

TL;DR: The R (R Core Team, 2019) package mlr3 is a complete reimplementation of the mlr (Bischl et al., 2016) package that leverages many years of experience and learned best practices to provide a state-of-the-art system that is powerful, flexible, extensible, and maintainable.

TL;DR: Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features and the presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution.

TL;DR: Considering the critical importance of the quality of input data for landslide susceptibility, the authors investigated the performance improvements that can be achieved by different globally available digitized data sets, and concluded that the performance of different digitised data sets can be significantly improved.

TL;DR: RQGIS supports the seamless integration of Python code using reticulate from within R for improved extendability, and offers a wider range of geoalgorithms, and is often easier to use due to various convenience functions.