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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 3
AGILE GIScience Ser., 3, 32, 2022
AGILE GIScience Ser., 3, 32, 2022
10 Jun 2022
10 Jun 2022

Representing Vector Geographic Information As a Tensor for Deep Learning Based Map Generalisation

Azelle Courtial1, Guillaume Touya1, and Xiang Zhang2 Azelle Courtial et al.
  • 1LASTIG, Univ Gustave Eiffel, ENSG, IGN, F-94160 Saint-Mande, France
  • 2School of Geospatial Engineering and Sciences, Sun Yat-Sen University, Guangzhou 510275, China

Keywords: Cartography, Deep learning , Map generalisation

Abstract. Recently, many researchers tried to generate (generalised) maps using deep learning, and most of the proposed methods deal with deep neural network architecture choices. Deep learning learns to reproduce examples, so we think that improving the training examples, and especially the representation of the initial geographic information, is the key issue for this problem. Our article extracts some representation issues from a literature review and proposes different ways to represent vector geographic information as a tensor.We propose two kinds of contributions: 1) the representation of information by layers; 2) the representation of additional information. Then, we demonstrate the interest of some of our propositions with experiments that show a visual improvement for the generation of generalised topographic maps in urban areas.

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