nD-PointCloud Data Management: continuous levels, adaptive histograms, and diverse query geometries

In the Geomatics domain, a point cloud refers to a data set which records the coordinates and other attributes of a huge number of points. Conceptually, each of these attributes can be regarded as a dimension, representing a specific type of information. Apart from routinely concerned spatio-temporal dimensions for coordinates, other dimensions...

Organizing and visualizing point clouds with continuous levels of detail

Point clouds contain high detail and high accuracy geometry representation of the scanned Earth surface parts. To manage the huge amount of data, the point clouds are traditionally organized on location and map-scale; e.g. in an octree structure, where top-levels of the tree contain few points suitable for small scale overviews and lower...

Executing convex polytope queries on nD point clouds

Efficient spatial queries are frequently needed to extract useful information from massive nD point clouds. Most previous studies focus on developing solutions for orthogonal window queries, while rarely considering the polytope query. The latter query, which includes the widely adopted polygonal query in 2D, also plays a critical role in...

An efficient nd-point data structure for querying flood risk

Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical flood inundation maps and tables for loss analysis. Original...