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Post-analysis of OSM-GAN Spatial Change Detection

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Web and Wireless Geographical Information Systems (W2GIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13238))

Abstract

Keeping crowdsourced maps up-to-date is important for a wide range of location-based applications (route planning, urban planning, navigation, tourism, etc.). We propose a novel map updating mechanism that combines the latest freely available remote sensing data with the current state of online vector map data to train a Deep Learning (DL) neural network. It uses a Generative Adversarial Network (GAN) to perform image-to-image translation, followed by segmentation and raster-vector comparison processes to identify changes to map features (e.g. buildings, roads, etc.) when compared to existing map data. This paper evaluates various GAN models trained with sixteen different datasets designed for use by our change detection/map updating procedure. Each GAN model is evaluated quantitatively and qualitatively to select the most accurate DL model for use in future spatial change detection applications.

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Notes

  1. 1.

    https://osi.ie/.

  2. 2.

    https://docs.qgis.org/3.16/en/docs/user_manual/preamble/preamble.html.

  3. 3.

    https://www.crowdai.org/challenges/mapping-challenge.

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Acknowledgements

The authors wish to thank all contributors involved with the OpenStreetMap project. This research is funded by Technological University Dublin College of Arts and Tourism, SEED FUNDING INITIATIVE 2019–2020. The authors wish to acknowledge the Irish Centre for High-End Computing (ICHEC) for the provision of computational facilities and support. We also gratefully acknowledge Ordinance Servey Ireland for providing both raster and vector data for the experiments.

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Authors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Lasith Niroshan & James D. Carswell

Authors
  1. Lasith Niroshan
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  2. James D. Carswell
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Correspondence to Lasith Niroshan .

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Editors and Affiliations

  1. Institute of Science and Technology Austria, Klosterneuburg, Austria

    Farid Karimipour

  2. Universität Konstanz, Konstanz, Baden-Württemberg, Germany

    Sabine Storandt

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Niroshan, L., Carswell, J.D. (2022). Post-analysis of OSM-GAN Spatial Change Detection. In: Karimipour, F., Storandt, S. (eds) Web and Wireless Geographical Information Systems. W2GIS 2022. Lecture Notes in Computer Science, vol 13238. Springer, Cham. https://doi.org/10.1007/978-3-031-06245-2_3

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  • DOI: https://doi.org/10.1007/978-3-031-06245-2_3

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  • Print ISBN: 978-3-031-06244-5

  • Online ISBN: 978-3-031-06245-2

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