Preprint Article Version 1 This version is not peer-reviewed

Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction

Version 1 : Received: 10 April 2018 / Approved: 11 April 2018 / Online: 11 April 2018 (04:27:42 CEST)

How to cite: Kumar, P.; Lewis, P.; McElhinney, C.P. Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction. Preprints 2018, 2018040134 (doi: 10.20944/preprints201804.0134.v1). Kumar, P.; Lewis, P.; McElhinney, C.P. Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction. Preprints 2018, 2018040134 (doi: 10.20944/preprints201804.0134.v1).

Abstract

Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

Subject Areas

airborne laser scanning; geospatial database; data retrieval; road median; attributes

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