The article provides an overview of the main features of geospatial data generated by LIDAR mapping systems with close attention to two categories - manned and unmanned aerial systems. The analysis is based on a comparison of several overlapping laser and imagery datasets acquired on the same territory by a full-sized LIDAR system and a small UAV system. An analysis and assessment of the quality and information content of airborne laser scanning materials is carried out, and the most important parameters have been determined, which define the optimal coverage and quality of the data required for solving a variety of production problems of Rosneft Oil Company. The work aimed to determine the criteria for the use of each technology in the process of creating topographic maps, forest inventory and building information model. The main features of LIDAR point clouds characterizing their integrity and quality is explored. The parameters are investigated including point density, spatial distribution, and attributive information. The results of a direct feature comparison of remote sensing data of two scanning systems are presented. The analysis is based on a matching of overlapping sets of laser data and images captured within the same area using a full-size LIDAR system for manned aircraft and a small system for unmanned aerial vehicles. A conclusion is made on the influence of the characteristics and information content of these scanning systems on the quality, homogeneity and detailing of the final model. A comprehensive concept of cyclic application of airborne laser scanning and digital aerial photography technologies at oil and gas fields of production enterprises of Rosneft Oil Company proposed. In addition, it is shown that differences in approaches to collecting data, varying their combinations, allow using the materials obtained to solve a wide range of tasks of the Company.
References
1. Shaohui Sun, Automatic 3D building detection and modeling from airborne LiDAR point clouds: Ph.D. Dissertation, Rochester Institute of Technology, 2013, URL: https://scholarworks.rit.edu/theses/960/
2. Shumeyko S.A., Sologubov D.S., Photogrammetric technology for 3D modeling complex production facilities (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2018, no. 10, pp. 98–101.
3. Skvortsov A.V., Triangulyatsiya Delone i ee primenenie (Delaunay triangulation and its application), Tomsk: Publ. of Tomsk University, 2002, 130 p.
4. Skvortsov A.V., Mirza N.S., Algoritmy postroeniya i analiza triangulyatsii (Algorithms for constructing and analyzing triangulation), Tomsk: Publ. of Tomsk University, 2006, 168 p.
5. USGS National geospatial program standards and specifications, Lidar Base Specification, 2020, URL: https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/Lidar-Base-Speci...
6. Kodors S., Point distribution as true quality of LiDAR point cloud, Baltic Journal of Modern Computing, 2017, V. 5, DOI:10.22364/bjmc.2017.5.4.03
7. Ullrich A., Sampling the world in 3D by airborne LiDAR — Assessingthe information content of LiDAR point clouds, Photogrammetric Week, 2013, URL: http://www.ifp.unistuttgart.de/publications/phowo13/index.en.html
8. Naus T., Unbiased LiDAR data measurement (Draft), 2008, URL: https://www.asprs.org/a/society/committees/lidar/Unbiased_measurement.pdf
9. Medvedev E.M., Danilin I.M., Mel'nikov S.R., Lazernaya lokatsiya zemli i lesa (Laser location of land and forest), Moscow - Krasnoyarsk: Publ. of Geolidar, Geoskosmos, Sukachev Institute of forest SB RAS, 2007.
10. Antonov A., Scanning laser rangefinders (LiDAR) (In Russ.), Sovremennaya elektronika, 2016, no. 1, pp. 10–15.
11. Shumeyko S.A., Filin
