Poly-GAN: Regularizing Polygons with Generative Adversarial Networks

Lasith Niroshan, James D. Carswell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a data-driven and Deep Learning (DL) based approach for regularizing OpenStreetMap building polygon edges. The study introduces a building footprint regularization technique (Poly-GAN) that utilises a Generative Adversarial Network model trained on irregular building footprints and OSM vector data. The proposed method is particularly relevant for map features predicted by Machine Learning (ML) algorithms in the GIScience domain, where information overload remains a significant problem in many cartographic/LBS applications. It addresses the limitations of traditional cartographic regularization/generalization algorithms, which can struggle with producing both accurate and minimal representations of multisided built environment objects. Furthermore, future work proposes a way to test the method on even more complex object shapes to address this limitation.

Original languageEnglish
Title of host publicationWeb and Wireless Geographical Information Systems - 20th International Symposium, W2GIS 2023, Proceedings
EditorsMir Abolfazl Mostafavi, Géraldine Del Mondo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages179-193
Number of pages15
ISBN (Print)9783031346118
DOIs
Publication statusPublished - 2023
Event20th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2023 - Quebec City, Canada
Duration: 12 Jun 202313 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13912 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2023
Country/TerritoryCanada
CityQuebec City
Period12/06/2313/06/23

Keywords

  • Generative Adversarial Networks
  • Geographic Information System
  • OpenStreetMap
  • Polygon Regularization

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