Data mining for recognition of spatial distribution patterns of building heights using airborne lidar data Abstract   There is an increasing demand for spatial big data visualisation in Geographic Information Systems (GIS) in building construction and urban development. Exploring building height patterns is required to obtain and visualize essential information about spatiotemporal vertical urban developments due to the trends towards increasing building heights in different urban fabrics. While metrics to characterize horizontal patterns of urban fabric using spectral information exist, theoriticalbased metrics identifying the patterns of vertical urban developments using height values are still scarce. In addition, there is a lack of reliable methods to analyze height information for modeling the distribution of building heights and to automatically detect threedimensional urban patterns. In this paper, we propose to apply the spatial statistics of Local Moranxe2x80x99s I (LMI),     G   i    xe2x88x97      and Kernel Density Estimation (KDE) on building heights to explore vertical urban patterns through detecting the concentration of relatively higher buildings. The proposed methods were applied on two different airborne lidar point cloud data sets. The results show overall good performance of LMI and Gi* methods compared to KDE. It is also found that there is a higher level of agreement between clusters of relatively higher buildings derived by the autocorrelation statistics of LMI and Gi*, compared with the patterns derived from the Kernel density. For the lower accuracies obtained from the KDE, the authors suggest to use either LMI or Gi* for this kind of study. The spatial closeness of clusters of higher buildings to major roads, defined by the mean distances of the clusters to major roads, were investigated and based on the Analysis of Variance (ANOVA) and Tukeyxe2x80x99s tests, the mean distances were found to be shorter than for all other buildings. Lastly, an analysis of clusters of the relatively higher buildings showed varying land uses for the two case studies.

Unsupervised Segmentation Of Urban 3D Point Cloud Based On Lidarimage As one of the most vital fields in 3D scene understanding, segmentation of lidar point cloud has verity of applications such as unmanned vehicle. In order to deal with the problem of limited local computing resources, this paper proposes a twostep unsupervised learningfree method to segment sparse 3D point cloud with low computational demands. The 3D point cloud data is projected into a lidarspecific 2D coordinate system called lidarimage. For unmanned vehicles, road is a special kind of object, so the algorithm divides road firstly by using improved lidarhistogram. The algorithm further examines the geometric connection between adjacent points in 3D space, thus dividing nonroad points into independent subsets. Experiments on KITTI dataset validate the proposed algorithm outperforms similar methods.