- Journal Article
Rights / licenseCreative Commons Attribution 4.0 International
Since landforms composing land surface vary in their properties and appearance, their shaded reliefs also present different visual impression of the terrain. In this work, we adapt a U-Net so that it can recognize a selection of landforms and can segment terrain. We test the efficiency of 10 separate models and apply an ensemble approach, where all the models are combined to potentially outperform single models. Our algorithm works particularly well for block mountains, Prealps, valleys, and hills, delivering average precision and f1 values above 60%. Segmenting plateaus and folded mountains is more challenging, and their precision values are rather scattered due to smaller areas available for training. Mountains formed by erosion processes are the least recognized landform of all because of their similarities with other landforms. The highest accuracy of one of the 10 models is 65%, while the accuracy of the ensemble is 61%. We apply relief shading techniques that were found to be efficient regarding specific landforms within corresponding segmented areas and blend them together. Finally, we test the trained model with the best accuracy on other mountainous areas around the world, and it proves to work in other regions beyond the training area. Show more
Journal / seriesISPRS International Journal of Geo-Information
Pages / Article No.
Subjectlandform recognition; convolutional neural networks; relief shading; geomorphology; machine learning; geoAI
Organisational unit03466 - Hurni, Lorenz / Hurni, Lorenz
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