Unsupervised Patch‐Based Ecoregion Classification in Austria Using DEM ‐Derived Features, Deep Learning, and Climate Data

ABSTRACT Digital elevation models (DEMs) are widely used to characterize terrain and support applications ranging from geomorphology to environmental management. While traditional analyses rely on handcrafted geomorphometric features, advances in machine learning enable richer, data‐driven representations of topographic surfaces. In this study, we present a multi‐source feature fusion approach that integrates statistical descriptors of elevation, derived terrain attributes, deep features extracted via a ResNet encoder, and climate variables from weather datasets. Using 5175 randomly sampled patches, we generated a comprehensive feature set and applied unsupervised clustering to identify ecoregional units. Results indicate that combining DEM‐derived features with climate data produces clusters that are both quantitatively robust, as indicated by internal validity indices, and exhibit meaningful spatial distribution when compared with known ecoregions.