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Clever sampling methods can be used to improve the handling of big data andincrease its usefulness. The subject of this study is remote sensing,specifically airborne laser scanning point clouds representing differentclasses of ground cover. The aim is to derive a supervised learning model forthe classification using CARTs. In order to measure the effect of differentsampling methods on the classification accuracy, various experiments withvarying types of sampling methods, sample sizes, and accuracy metrics have beendesigned. Numerical results for a subset of a large surveying project coveringthe lower Rhine area in Germany are shown. General conclusions regardingsampling design are drawn and presented.

Effects of Sampling Methods on Prediction Quality. The Case of Classifying Land Cover Using Decision Trees