Abstract: In this paper we present a deep learning (U-Net) based workflow for classifying linear dune landforms based on the discrete Laplacian convolution of a new global elevation dataset, the AW3D30 Digital Surface Model. Crest vectors were then derived for landscape pattern analysis. The U-Net crest classification model was trained and evaluated on sample data from dunefields across the Australian continent. The resulting crest vectors and dune defect placement were then evaluated in typical semi-arid and arid dune landscapes in eastern central Australia where high resolution (5 m horizontal) Digital Elevation Models (DEMs) are available (for three out of our four study sites) as a reference dataset. The method was applied to quantify dune pattern metrics for the entire Simpson Desert dunefield, Australia. The U-Net does a very good job of segmenting dune crests, even where dunes are less clear in the Laplacian map (intersection over union score ≈ 0.68). When crest vectors and dune defects (network nodes) were derived, the defect predictions were typically correct (0.4 to 0.79 correctness) but incomplete (0.02 to 0.64 completeness). Much of the residual error was traced to the resolution of the input data. Through the application to the Simpson Desert we nevertheless demonstrated that our method can be effectively used for regional-scale dune pattern analysis. Furthermore, we suggest that the combination of morphological filtering and a convolutional neural network could be readily adapted to target other geomorphic features, such as channel networks or geological lineaments.