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During past 25 years, laser scanning has evolved from an experimental method into a fully autonomous family of Earth remote sensing methods. Now this group of methods provides the most accurate and detailed spatial data sets, while the cost of data is constantly falling, the number of measuring instruments (laser scanners) is constantly growing. The volumes of data that will be obtained during the surveys in the coming decades will allow the creation of the first sub-global coverage of the planet. However, the flip side of high accuracy and detail is the need to store fantastically large volumes of three-dimensional data without loss of accuracy. At the same time, the ability to work with the specified data in both 2D and 3D mode should be improved. Standard storage methods (file method, geodatabases, archiving, etc) solve the problem only partially. At the same time, there are some other alternative methods that can remove current restrictions and lead to the emergence of more flexible and functional spatial data infrastructures. One of the most flexible and promising ways of laser data storage and processing are quadtree and octree-based approaches. Of course, these approaches are more complicated than typical file data structures, that are commonly used for LIDAR data storage, but they allow users to solve some typical negative features of point datasets (processing speed, non-topological spatial structure, limited precision, etc.). |
