A typical approach to exploring Light Detection and Ranging (LIDAR) datasets is to extract features using pre-defined
segmentation algorithms. However, this approach only provides a limited set of features that users can investigate. To
expand and represent the rich information inside the LIDAR data, we introduce a linked feature space concept that
allows users to make regular, conjunctive, and disjunctive discoveries in non-uniform LIDAR data by interacting with
multidimensional transfer functions. We achieve this by providing interactions for creating multiple scatter-plots of
varying axes, establishing chains of plots based on selection domains, linking plots using logical operators, and viewing
selected brushing results in both a 3D view and selected scatter-plots. Our highly interactive approach to visualizing
LIDAR feature spaces facilitates the users' ability to explore, identify, and understand data features in a novel way. Our
approach for exploring LIDAR data can directly lead to better understanding of historical LIDAR datasets, and increase
the turnaround time and quality of results from time-critical LIDAR collections after urban disasters or on the battlefield.
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