The Use of Minimal Geometries in Automated Building Generalization

Lupa, Michał; Szombara, Stanisław; Kozioł, Krystian; Chromiak, Michał

doi:10.1007/978-3-319-99987-6_30

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…Some propose to transform the building into a raster grid (Hui-lian et al, 2005) to use morphological operators (dilations and erosions) on pixels. Others use the skeleton of the polygon to generate a minimal, simplified geometry (Meijers, 2016;Lupa et al, 2018). Finally others adopted strategies based on optimisation (Haunert and Wolff, 2010) or machine learning (Cheng et al, 2013) to find the edges that should be simplified.…”

Section: Building Generalisation In Urban Areasmentioning

confidence: 99%

Generative Adversarial Networks to Generalise Urban Areas in Topographic Maps

Courtial

Touya

Zhang

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This article presents how a generative adversarial network (GAN) can be employed to produce a generalised map that combines several cartographic themes in the dense context of urban areas. We use as input detailed buildings, roads, and rivers from topographic datasets produced by the French national mapping agency (IGN), and we expect as output of the GAN a legible map of these elements at a target scale of 1:50,000. This level of detail requires to reduce the amount of information while preserving patterns; covering dense inner cities block by a unique polygon is also necessary because these blocks cannot be represented with enlarged individual buildings. The target map has a style similar to the topographic map produced by IGN. This experiment succeeded in producing image tiles that look like legible maps. It also highlights the impact of data and representation choices on the quality of predicted images, and the challenge of learning geographic relationships.

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Section: Building Generalisation In Urban Areasmentioning

confidence: 99%