{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T12:44:34Z","timestamp":1768826674679,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MCIN\/ AEI\/ 10.13039\/501100011033\/","award":["RTI2018-099638-B-I00"],"award-info":[{"award-number":["RTI2018-099638-B-I00"]}]},{"name":"MCIN\/ AEI\/ 10.13039\/501100011033\/","award":["1265116\/2020"],"award-info":[{"award-number":["1265116\/2020"]}]},{"DOI":"10.13039\/501100007064","name":"University of Ja\u00e9n (via ERDF funds)","doi-asserted-by":"publisher","award":["RTI2018-099638-B-I00"],"award-info":[{"award-number":["RTI2018-099638-B-I00"]}],"id":[{"id":"10.13039\/501100007064","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007064","name":"University of Ja\u00e9n (via ERDF funds)","doi-asserted-by":"publisher","award":["1265116\/2020"],"award-info":[{"award-number":["1265116\/2020"]}],"id":[{"id":"10.13039\/501100007064","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The widespread use of LiDAR technologies has led to an ever-increasing volume of captured data that pose a continuous challenge for its storage and organization, so that it can be efficiently processed and analyzed. Although the use of system files in formats such as LAS\/LAZ is the most common solution for LiDAR data storage, databases are gaining in popularity due to their evident advantages: centralized and uniform access to a collection of datasets; better support for concurrent retrieval; distributed storage in database engines that allows sharding; and support for metadata or spatial queries by adequately indexing or organizing the data. The present work evaluates the performance of four popular NoSQL and relational database management systems with large LiDAR datasets: Cassandra, MongoDB, MySQL and PostgreSQL. To perform a realistic assessment, we integrate these database engines in a repository implementation with an elaborate data model that enables metadata and spatial queries and progressive\/partial data retrieval. Our experimentation concludes that, as expected, NoSQL databases show a modest but significant performance difference in favor of NoSQL databases, and that Cassandra provides the best overall database solution for LiDAR data.<\/jats:p>","DOI":"10.3390\/rs14112623","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T05:25:42Z","timestamp":1653974742000},"page":"2623","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Strategies for the Storage of Large LiDAR Datasets\u2014A Performance Comparison"],"prefix":"10.3390","volume":"14","author":[{"given":"Juan A.","family":"B\u00e9jar-Martos","sequence":"first","affiliation":[{"name":"Cntro de Estudios Avanzados en TIC, University of Ja\u00e9n, 23071 Ja\u00e9n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7692-454X","authenticated-orcid":false,"given":"Antonio J.","family":"Rueda-Ruiz","sequence":"additional","affiliation":[{"name":"Cntro de Estudios Avanzados en TIC, University of Ja\u00e9n, 23071 Ja\u00e9n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0958-990X","authenticated-orcid":false,"given":"Carlos J.","family":"Ogayar-Anguita","sequence":"additional","affiliation":[{"name":"Cntro de Estudios Avanzados en TIC, University of Ja\u00e9n, 23071 Ja\u00e9n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3075-6963","authenticated-orcid":false,"given":"Rafael J.","family":"Segura-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Cntro de Estudios Avanzados en TIC, University of Ja\u00e9n, 23071 Ja\u00e9n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1423-9496","authenticated-orcid":false,"given":"Alfonso","family":"L\u00f3pez-Ruiz","sequence":"additional","affiliation":[{"name":"Cntro de Estudios Avanzados en TIC, University of Ja\u00e9n, 23071 Ja\u00e9n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"ref_1","unstructured":"Leica Geosystems (2022, May 22). ScanStation P40, P30 and P16 Comparison Chart. Available online: https:\/\/leica-geosystems.com\/en-us\/products\/laser-scanners\/-\/media\/00ac56bc2a93476b8fe3d7c1795040ac.ashx."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2015.10.012","article-title":"Geospatial Big Data handling theory and methods: A review and research challenges","volume":"115","author":"Li","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","unstructured":"Chen, R., and Xie, J. (2008). Open Source Databases and their Spatial Extensions, Springer."},{"key":"ref_4","first-page":"1","article-title":"SPSLiDAR: Towards a multi-purpose repository for large scale LiDAR datasets","volume":"36","year":"2022","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_5","unstructured":"(2019). Poux, Florent. The Smart Point Cloud: Structuring 3D Intelligent Point Data. [Ph.D. Thesis, Universite de Liege]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Br\u00e4unl, T. (2020). Lidar sensors. Robot Adventures in Python and C, Springer.","DOI":"10.1007\/978-3-030-38897-3"},{"key":"ref_7","unstructured":"Evans, M.R., Oliver, D., Zhou, X., and Shekhar, S. (2014). Spatial Big Data. Case studies on volume, velocity, and varitety. Big Data: Techniques and Technologies in Geoinformatics, CRC Press."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.bdr.2015.01.003","article-title":"Geospatial Big Data: Challenges and opportunities","volume":"2","author":"Lee","year":"2015","journal-title":"Big Data Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.bdr.2015.01.001","article-title":"Reference architecture and classification of technologies, products and services for Big Data systems","volume":"2","author":"Pakkala","year":"2015","journal-title":"Big Data Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3841","DOI":"10.1109\/JSTARS.2019.2944952","article-title":"Geospatial Big Data: Ew paradigm of remote sensing applications","volume":"12","author":"Deng","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sugimoto, K., Cohen, R.A., Tian, D., and Vetro, A. (2017, January 12\u201315). Trends in efficient representation of 3D point clouds. Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/APSIPA.2017.8282059"},{"key":"ref_12","unstructured":"Turner, M.D., and Kamerman, G.W. (2019). Advances in LiDAR point cloud processing. Laser Radar Technology and Applications XXIV, SPIE."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/978-3-030-58601-0_19","article-title":"Rethinking pseudo-LiDAR representation","volume":"Volume 12358","author":"Vedaldi","year":"2020","journal-title":"Computer Vision\u2014ECCV 2020"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Poux, F., and Billen, R. (2019). A Smart Point Cloud Infrastructure for intelligent environments. Laser Scanning: An Emerging Technology in Structural Engineering, CRC Press.","DOI":"10.1201\/9781351018869-9"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.cag.2011.01.004","article-title":"Out-of-core selection and editing of huge point clouds","volume":"35","author":"Scheiblauer","year":"2011","journal-title":"Comput. Graph."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1111\/cgf.12345","article-title":"Out-of-core construction of sparse voxel octrees","volume":"33","author":"Baert","year":"2014","journal-title":"Comput. Graph. Forum"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Breunig, M., Al-Doori, M., Butwilowski, E., Kuper, P.V., Benner, J., and Haefele, K.H. (2015). Out-of-core visualization of classified 3D point clouds. 3D Geoinformation Science, Springer International Publishing.","DOI":"10.1007\/978-3-319-12181-9"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1080\/13658816.2018.1549734","article-title":"Supporting multi-resolution out-of-core rendering of massive LiDAR point clouds through non-redundant data structures","volume":"33","author":"Deibe","year":"2019","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1111\/cgf.14134","article-title":"Fast out-of-core octree generation for massive point clouds","volume":"39","author":"Ohrhallinger","year":"2020","journal-title":"Comput. Graph. Forum"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.isprsjprs.2012.10.004","article-title":"One billion points in the cloud\u2014An octree for efficient processing of 3D laser scans","volume":"76","author":"Elseberg","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.cageo.2012.08.021","article-title":"Octree-based indexing for 3D point clouds within an Oracle Spatial DBMS","volume":"51","author":"Mosa","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9397","DOI":"10.1007\/s13369-019-03968-7","article-title":"Massive point cloud space management method based on octree-like encoding","volume":"44","author":"Lu","year":"2019","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huang, L., Wang, S., Wong, K., Liu, J., and Urtasun, R. (2020). OctSqueeze: Octree-structured entropy model for LiDAR compression. arXiv.","DOI":"10.1109\/CVPR42600.2020.00139"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s00371-012-0675-2","article-title":"An efficient multi-resolution framework for high quality interactive rendering of massive point clouds using multi-way kd-trees","volume":"29","author":"Goswami","year":"2013","journal-title":"Vis. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"373","DOI":"10.14358\/PERS.78.4.373","article-title":"An efficient point cloud management method based on a 3D R-tree","volume":"78","author":"Gong","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.cageo.2010.05.017","article-title":"Distributed data organization and parallel data retrieval methods for huge laser scanner point clouds","volume":"37","author":"Hongchao","year":"2011","journal-title":"Comput. Geosci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.cag.2015.02.005","article-title":"A modular software architecture for processing of Big Geospatial Data in the cloud","volume":"49","author":"Senner","year":"2015","journal-title":"Comput. Graph."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Deibe, D., Amor, M., and Doallo, R. (2018, January 10\u201313). Big Data storage technologies: A case study for web-based LiDAR visualization. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622589"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"209","DOI":"10.14358\/PERS.79.2.209","article-title":"LASzip","volume":"79","author":"Isenburg","year":"2013","journal-title":"Photogramm. Eng. Remote. Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.cageo.2013.01.019","article-title":"Sorted pulse data (SPD) library. Part I: A generic file format for LiDAR data from pulsed laser systems in terrestrial environments","volume":"56","author":"Bunting","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cao, C., Preda, M., and Zaharia, T. (2019, January 26\u201328). 3D point cloud compression: A survey. Proceedings of the 24th International Conference on 3D Web Technology, Los Angeles, CA, USA.","DOI":"10.1145\/3329714.3338130"},{"key":"ref_32","unstructured":"Boehm, J. (2014, January 4). File-centric organization of large LiDAR point clouds in a Big Data context. Proceedings of the Workshop on Processing Large Geospatial Data, Dallas, TX, USA."},{"key":"ref_33","unstructured":"Pandey, R. (2020). Performance Benchmarking and Comparison of Cloud-Based Databases MongoDB (NoSQL) vs. MySQL (Relational) Using YCSB, National College of Ireland."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"531","DOI":"10.5194\/isprsannals-II-3-W5-531-2015","article-title":"Point Cloud Server (PCS): Point clouds in-base management and processing","volume":"II-3\/W5","author":"Cura","year":"2015","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","unstructured":"(2022, May 22). Migrating Facebook to MySQL 8.0. Available online: https:\/\/engineering.fb.com\/2021\/07\/22\/data-infrastructure\/mysql."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.cag.2015.01.007","article-title":"Massive point cloud data management: Design, implementation and execution of a point cloud benchmark","volume":"49","author":"Ivanova","year":"2015","journal-title":"Comput. Graph."},{"key":"ref_37","unstructured":"Reitz, K. (2021, March 09). HTTP for Humans(TM)\u2014Requests 2.25.1 Documentation. Available online: https:\/\/docs.python-requests.org\/."},{"key":"ref_38","unstructured":"Fern\u00e1ndez, A. (2021, March 09). Loadtest 5.1.2. Available online: https:\/\/www.npmjs.com\/package\/loadtest."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2623\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:22:29Z","timestamp":1760138549000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2623"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,31]]},"references-count":38,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112623"],"URL":"https:\/\/doi.org\/10.3390\/rs14112623","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,31]]}}}