3D map creation using crowdsourced GNSS data

Lines, T. and Basiri, A. (2021) 3D map creation using crowdsourced GNSS data. Computers, Environment and Urban Systems, 89, 101671. (doi: 10.1016/j.compenvurbsys.2021.101671)

Abstract

3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lines, Mr Terence and Basiri, Professor Ana
• Authors: Lines, T., and Basiri, A.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: Computers, Environment and Urban Systems
• Publisher: Elsevier
• ISSN: 0198-9715
• ISSN (Online): 1873-7587
• Published Online: 19 June 2021
• Copyright Holders: Copyright © 2021 Crown Copyright
• First Published: First published in Computers, Environment and Urban Systems 89: 101671
• Publisher Policy: Reproduced under a Creative Commons License

Related URLs

• Pre-print

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
312992		Indicative Data: Extracting 3D Models of Cities from Unavailability and Degradation of Global Navigation Satellite Systems (GNSS)	Ana Basiri	UK Research and Innovation ( UKRI) (UKRI)	MR/S01795X/2	GES - Geography