A general-purpose framework for parallel processing of large-scale LiDAR data

Li, Zhenlong; Hodgson, Michael E.; Li, Wenwen

doi:10.1080/17538947.2016.1269842

Cited by 38 publications

(32 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2012, Lewis et al [4] proposed a framework for LiDAR data storage, segmentation, and web-based streaming. Over the following years, Mao and Cao [5] proposed a method of remote 3D visualization of LiDAR data using HyperText Markup Language version 5 (HTML5) technologies, Maravelakis et al [6] presented the Web-based point-cloud viewer utilising the Three.js Web Graphics Library (WebGL) Abstraction Layer, Nale [7] designed a framework for processing, querying, and web-based streaming of LiDAR data, Li et al [8] proposed a framework for online LiDAR data processing that is based on Apache Hadoop, while von Schwerin et al [9] presented a system for 3D visualization of pre-processed LiDAR data using WebGL. More recently, Schmiemann et al [10] proposed a method for online mapping of data directly obtained form Unmanned Aerial Vehicles (UAV's), Huang and Wang proposed a system for online processing and the visualization of LiDAR point clouds using WebGL [11], while Bohak et al [12] presented a framework for online 3D visualization of LiDAR data while using Three.js and Potree.…”

Section: Introductionmentioning

confidence: 99%

Integration, Processing and Dissemination of LiDAR Data in a 3D Web-GIS

Kulawiak

Łubniewski

2019

IJGI

View full text Add to dashboard Cite

The rapid increase in applications of Light Detection and Ranging (LiDAR) scanners, followed by the development of various methods that are dedicated for survey data processing, visualization, and dissemination constituted the need of new open standards for storage and online distribution of collected three-dimensional data. However, over a decade of research in the area has resulted in a number of incompatible solutions that offer their own ways of disseminating results of LiDAR surveys (be it point clouds or reconstructed three-dimensional (3D) models) over the web. The article presents a unified system for remote processing, storage, visualization, and dissemination of 3D LiDAR survey data, including 3D model reconstruction. It is built with the use of open source technologies and employs open standards, such as 3D Tiles, LASer (LAS), and Object (OBJ) for data distribution. The system has been deployed for automatic organization, processing, and dissemination of LiDAR surveys that were performed in the city of Gdansk. The performance of the system has been measured using a selection of LiDAR datasets of various sizes. The system has shown to considerably simplify the process of data organization and integration, while also delivering tools for easy discovery, inspection, and acquisition of desired datasets.

show abstract

Section: Introductionmentioning

confidence: 99%

Integration, Processing and Dissemination of LiDAR Data in a 3D Web-GIS

Kulawiak

Łubniewski

2019

IJGI

View full text Add to dashboard Cite

show abstract

“…However, it was costly to set the hardware environment. [14], [15] and [16] introduced a general solution for point cloud data processing based on Hadoop. In terms of cost, Hadoop had advantages, however, studies had limitations on analytic operations.…”

Section: Introductionmentioning

confidence: 99%

A Framework Development for Mapping and Detecting Changes in Repeatedly Collected Massive Point Clouds

Yoon

Park

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

On a construction site, progress deviations can cause fatal damages to the project and stakeholders. Therefore, accurately monitoring and managing the construction environment is essential. Efficiently detecting and recognizing the changes will be the key factor for monitor and management goals. In this research, we present a framework for detecting and recognizing changes in repeatedly collected, massive 3D point cloud data from a mobile laser scanning system. The framework mainly consists of three parts; 1) mapping system; 2) analysis system; 3) Hadoop platform. Collecting point clouds is repeatedly executed to detect the changes over different time epochs. For collecting point cloud data, a mobile laser scanning system was developed based on Robot Operating System (ROS). Detecting changes between repeatedly collected point clouds have been processed based on Hadoop platform. Finally, detected changes are then implemented to a semantic mapping process which is based on deep learning. Developed framework have potential for wide application in massive point cloud data processing, construction site monitoring, street level change detection, and facility management.

show abstract

“…Extracting information from LiDAR data efficiently, requires new approaches for storing, managing and processing big data collections. This challenge is addressed by in the contribution of Li, Hodgson, and Li (2018) with the title 'A General-purpose Framework for Parallel Processing of Large-scale LiDAR Data'. They evaluate the benefits of a general-purpose framework, which uses a data decomposition and parallelization strategy for efficiently processing the data on parallel machines.…”

Section: Innovation In Geoprocessing For a Digital Earthmentioning

confidence: 99%