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AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Articles | Volume 2
AGILE GIScience Ser., 2, 11, 2021
https://doi.org/10.5194/agile-giss-2-11-2021
AGILE GIScience Ser., 2, 11, 2021
https://doi.org/10.5194/agile-giss-2-11-2021

  04 Jun 2021

04 Jun 2021

Extraction of linear structures from digital terrain models using deep learning

Ramish Satari1, Bashir Kazimi2, and Monika Sester2 Ramish Satari et al.
  • 1Institute of Fluid Mechanics and Environmental Physics in Civil Engineering, Leibniz Universität Hannover, Germany
  • 2Institute of Cartography and Geoinformatics, Leibniz Universität Hannover, Germany

Keywords: Semantic Segmentation, Digital Terrain Models, GIS: Geographic Information Systems, Deep Learning

Abstract. This paper explores the role deep convolutional neural networks play in automated extraction of linear structures using semantic segmentation techniques in Digital Terrain Models (DTMs). DTM is a regularly gridded raster created from laser scanning point clouds and represents elevations of the bare earth surface with respect to a reference. Recent advances in Deep Learning (DL) have made it possible to explore the use of semantic segmentation for detection of terrain structures in DTMs. This research examines two novel and practical deep convolutional neural network architectures i.e. an encoder-decoder network named as SegNet and the recent state-of-the-art high-resolution network (HRNet). This paper initially focuses on the pixel-wise binary classification in order to validate the applicability of the proposed approaches. The networks are trained to distinguish between points belonging to linear structures and those belonging to background. In the second step, multi-class segmentation is carried out on the same DTM dataset. The model is trained to not only detect a linear feature, but also to categorize it as one of the classes: hollow ways, roads, forest paths, historical paths, and streams. Results of the experiment in addition to the quantitative and qualitative analysis show the applicability of deep neural networks for detection of terrain structures in DTMs. From the deep learning models utilized, HRNet gives better results.

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