3D map creation using crowdsourced GNSS data

TL;DR

This paper introduces a cost-effective method to generate 3D maps using crowdsourced GNSS data, enabling height estimation of buildings with high accuracy without expensive equipment.

Contribution

It presents a novel approach leveraging GNSS signal patterns and a boot-strapped algorithm to create 3D maps, reducing reliance on costly laser scanning technologies.

Findings

Building height can be estimated with below 5 metre accuracy.

The method effectively creates 3D maps from publicly available GNSS data.

The approach meets CityGML standards for 3D urban modeling.

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.