Abstract
Discrete global grid systems (DGGSs) have emerged in recent years as a new specification for working with global heterogeneous data sets in a Digital Earth framework. Point clouds originating from different sources usually have varying initial characteristics. This research aims to analyze to what extent a DGGS can be used to handle point clouds having varying coordinate systems, acquired at different levels of detail (densities), and at different times in the creation of a global map of point clouds. DGGSs, which are currently limited to a 2D (surface) space, are extended into 3D and 4D spaces to fully harness the multidimensional nature of point clouds. A continuous spatial indexing strategy, based on a space-filling curve, is then developed on an ellipsoidal model of the Earth and used to efficiently cluster and retrieve DGGS-based point clouds stored in a database. Finally, the queried points are visualized in a Web browser. The hierarchical, multi-resolution nature of a DGGS is exploited to achieve a variable-scale smooth-zoom visualization. The challenges and opportunities of point cloud integration in a DGGS are presented.
Highlights
Point clouds are becoming increasingly popular ways of mapping the Earth’s surface
Several contributions to the field are being offered with this thesis: first, to date, there have been no studies on using point clouds with DGGS; second, a DGGS has never been applied to the indexing of a global point cloud; third, it is shown how a DGGS can be used in higher than 2 dimensions; fourth, it is shown how the precision-encoding nature of a DGGS can be used for variable-scale smooth zoom visualization; and fifth a DGGS is compared and contrasted with conventional Coordinate Reference System (CRS)’s
The goal of this research is to analyze to which extent a DGGS is suitable for handling point clouds stemming from different origins with respect to location, time, and Levels Of Detail (LOD)’s and create a DGGS-based viewer allowing for visualization, analysis, and upload of global open point cloud data
Summary
Point clouds are becoming increasingly popular ways of mapping the Earth’s surface. Several technologies exist to generate these massive point clouds, with one of the major ones being LIDAR [Wang et al, 2018], which uses laser scanning to generate a digital 3D representation of the target of interest. One of the most significant challenges in point cloud data processing lies in handling its increasing data volume. A further challenge and one more important from a practical perspective is the provision of this enormous amount of point cloud data to the general public at free or reasonably low-cost rates. Point cloud data remains inaccessible, licensed and/or proprietary and out of reach of the general public. It is of enormous added value to research whether it is possible to establish an open-source platform for the accessibility of point clouds and to investigate the key challenges of point cloud data management in the creation of this platform
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