%0 PDF %T A linked feature space approach to exploring LIDAR data. %A Harrison, Lane T.; Butkiewicz, Thomas.; Wang, Xiaoyu.; Ribarsky, William.; Chang, Remco. %D 2017-11-16T12:05:37.956-05:00 %8 2017-11-16 %I Tufts University. Tisch Library. %R http://localhost/files/r207v158s %X 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. Copyright 2010 Society of Photo-Optical Instrumentation Engineers (SPIE) One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. %[ 2018-10-09 %9 Text %~ Tufts Digital Library %W Institution