SPSLiDAR: towards a multi-purpose repository for large scale LiDAR datasets

Abstract

The widespread use of LiDAR technology in a multitude of domains has produced a growing availability of massive high-resolution point datasets that demand new approaches for efficient organization and storage, filtering using different spatio-temporal criteria, selective/progressive visualization, processing and analysis, and collaborative editing. Ideally, LiDAR data coming from multiple sources and organized in different datasets should be accessible in a simple, uniform, and ubiquitous way to comply with the FAIR principle proposed by the Open Geospatial Consortium: Findable, Accessible, Interoperable, and Reusable. With this goal in mind, we present SPSLiDAR, a conceptual model with a simple interface for repositories of LiDAR data that can be adapted to the needs of different applications. SPSLiDAR includes aspects, such as the arrangement of related datasets into workspaces on a world scale, support for overlapping datasets with different resolutions or acquired at different times, and hierarchical organization of point data, enabling levels of detail and selective download. We also describe in detail an implementation of this model aimed at visualization and downloading of large datasets using the MongoDB database. Finally, we show some experimental results of this implementation using real data, such as its space requirements, upload latency, access latency, and throughput.

Keywords:

• LiDAR
• big data
• point cloud
• spatial data structure
• NoSQL

Author contributions

Antonio J. Rueda-Ruiz, Rafael J. Segura-Sánchez, and Carlos J. Ogáyar Anguita: concept, research and methodology. Antonio J. Rueda-Ruiz and Juan A. Béjar-Martos: implementation. Juan A. Béjar-Martos, Antonio J. Rueda-Ruiz, Rafael J. Segura-Sánchez, and Carlos J. Ogáyar Anguita: experimentation. Antonio J. Rueda-Ruiz, Carlos J. Ogáyar-Anguita, Rafael J. Segura-Sánchez, and Jorge Delgado-Garcia: validation. Antonio J. Rueda-Ruiz, Carlos J. Ogáyar-Anguita, and Rafael J. Segura-Sánchez: writing–original draft. Antonio J. Rueda-Ruiz, Carlos J. Ogáyar-Anguita, and Rafael J. Segura-Sánchez: writing–review and editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and code availability statement

The source code of the SPSLiDAR implementation, together with the scripts and links to the datasets used in the experimentation can be found in the following GitHub repository: https://github.com/spslidar/spslidar.

Additional information

Funding

This result is part of the research project RTI2018-099638-B-I00 funded by MCIN/AEI/10.13039/501100011033/ and ERDF funds ‘A way of doing Europe’. Also, the work has been funded by the University of Jaén (via ERDF funds) through the research project 1265116/2020.

Notes on contributors

Antonio J. Rueda-Ruiz

Antonio J. Rueda-Ruiz received a Ph.D. degree in computer science from the University of Málaga, in 2004. He is currently an Associate Professor of computer science at the University of Jaén, Spain. His main interests are related to Computer Graphics, focused on designing geometric algorithms, processing 3D laser scanned data, GPU computing, and Computational Archaeology.

Carlos J. Ogáyar-Anguita

Carlos J. Ogáyar-Anguita received the M.S. degree in computer science and the Ph.D. degree in computer science from the University of Granada, in 2001 and 2006, respectively. He is currently an Associate Professor of computer science at the University of Jaén, Spain. His researches focus are on GPU computing, geometric algorithms, virtual reality, and 3D scanned data processing.

Rafael J. Segura-Sánchez

Rafael J. Segura-Sánchez received the M.S. degree in computer science from the University of Granada and the Ph.D. degree in computer science from the University of Granada, in 2001. He is currently a Full Professor at the University of Jaén, Spain. He has been working on several topics related to computer graphics, including solid modeling, computational geometry, virtual and augmented reality, and GPGPU.

Juan A. Béjar-Martos

Juan A. Béjar-Martos received a Bachelor’s Degree in Computer Science from the University of Jaén in 2019 and a Master’s Degree in Computer Science from the University of Jaén in 2021. He contributed to the project by working on the development and testing of the server application. He is currently working as a software engineer in KX Systems. His fields of interest are software development and big data.

Jorge Delgado-Garcia

Jorge Delgado-García received his Ph.D. in Geology (Geostatistics) from the University of Granada (Spain) in 1993. He is currently an Associate Professor in cartographic, geodetic, and photogrammetric engineering with the University of Jaén (Spain). He is currently working on several international projects related to Land Administration funded by the World Bank. His main interests are photogrammetric (image and laser) data acquisition, processing, and 3D modeling.