Multi-dimensional data discovery

Abstract

Laser scanner systems provide economical and efficient three dimensional geopositioning data. These systems have had an increasingly large impact on Topographic Information Systems (TIS) and Geographic Information Systems (GIS). LIght Detection And Ranging (LIDAR) is an optical remote sensing laser scanner that measures the properties of scattered light to determine the range to, and other information about distant targets. LIDAR returns are stored, in a multi-dimensional data set, called a point cloud, containing a location (X,Y,Z) and a number of other attributes (e.g. time, intensity, frequency, etc) for each point. The data volumes created by this technology are enormous and have led for a need for full dimensional data indexing and integrated data applications interpolation algorithms. A single region may have many point clouds collected from multiple sources over different times and with different resolution/densities. While there is general consensus on file structure and metadata each sensor/platform data product is uniquely tailored to the vendors specific applications often with specific data fields unfilled. Discovery of these point clouds over an area of interest is manually intensive due the lack of multi-dimensional discovery software.