Author ORCID Identifier

https://orcid.org/0000-0002-9868-8338

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences, Geosciences, (multidisciplinary)

Publication Details

Conference: 19th International Symposium on Web and Wireless Geographical Information Systems

Type: Virtual

Published by Springer

https://link.springer.com/chapter/10.1007/978-3-031-06245-2_3

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 novelmap updatingmechanism 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 GenerativeAdversarial 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.

DOI

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

Funder

Technological University Dublin College of Arts and Tourism

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