Scalable 3D mapping of cities using computer vision and signals of opportunity

Basiri, A. , Lines, T. and Pereira, M. F. (2023) Scalable 3D mapping of cities using computer vision and signals of opportunity. International Journal of Geographical Information Science, 37(7), pp. 1470-1495. (doi: 10.1080/13658816.2023.2191674)

Abstract

Three-dimensional (3D) maps are used extensively in a variety of applications, from air and noise pollution modelling to location-based services such as 3D mapping-aided Global Navigation Satellite Systems (GNSS), and positioning and navigation for emergency service personnel, unmanned aerial vehicles and autonomous vehicles. However, the financial cost associated with creating and updating 3D maps using the current state-of-the-art methods such as laser scanning and aerial photogrammetry are prohibitively expensive. To overcome this, researchers have proposed using GNSS signals to create 3D maps. This paper advances that family of methods by proposing and implementing a novel technique that avoids the difficult step of directly classifying GNSS signals into line-of-sight and non-line-of-sight classes by utilising edge detection techniques adapted from computer vision. This prevents classification biases and increases the range of environments in which GNSS-based 3D mapping methods can be accurately deployed. Being based on the patterns of blockage and attenuation of GNSS signals that are freely and globally available to receive by many mobile phones, makes the proposed technique a free, scalable and accessible solution. This paper also identifies some key indicators affecting data collection scalability and efficiency of the 3D mapping solution.

• Item Type: Articles
• Additional Information: The authors received the support from the UK Research and Innovation Future Leaders Fellowship “Indicative Data: Extracting 3D Models of Cities from Unavailability and Degradation of Global Navigation Satellite Systems (GNSS)”, grant number MR/S01795X/2, the Royal Society Research Grant, grant reference RSG/R1/180396.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lines, Mr Terence and Pereira, Miguel Fidel and Basiri, Professor Ana
• Authors: Basiri, A., Lines, T., and Pereira, M. F.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: International Journal of Geographical Information Science
• Publisher: Taylor & Francis
• ISSN: 1365-8816
• ISSN (Online): 1365-8824
• Published Online: 27 March 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in International Journal of Geographical Information Science 37(7):1470-1495
• Publisher Policy: Reproduced under a Creative Commons license

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