On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction

arXiv:2404.12575v1 Announce Type: new
Abstract: Recent geospatial machine learning studies have shown that the results of model evaluation via cross-validation (CV) are strongly affected by the dissimilarity between the sample data and the prediction locations. In this paper, we propose a method to quantify such a dissimilarity in the interval 0 to 100%, and from the perspective of the data feature space. The proposed method is based on adversarial validation, which is an approach that can check whether sample data …

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