Visual Analysis and Semantic Exploration of Urban LIDAR Change Detection

Abstract

Many previous approaches to detecting urban change from LIDAR point clouds interpolate the points into rasters, perform pixel-based image processing to detect changes, and produce 2D images as output. We present a method of LIDAR change detection that maintains accuracy by only using the raw, irregularly spaced LIDAR points, and extracts relevant changes as individual 3D models. We then utilize these models, alongside existing GIS data, within an interactive application that allows the chronological exploration of the changes to an urban environment. A three-tiered level-of-detail system maintains a scale-appropriate, legible visual representation across the entire range of view scales, from individual changes such as buildings and trees, to groups of changes such as new residential developments, deforestation, and construction sites, and finally to larger regions such as neighborhoods and districts of a city that are emerging or undergoing revitalization. Tools are provided to assist the visual analysis by urban planners and historians through semantic categorization and filtering of the changes presented.

Detection of Geometric Temporal Changes in Point Clouds

“…Montaut et al [MRMT05] propose an octree-based strategy, comparing three different distance measures between the octree cells using the points in each cells: the average distance from the nearest neighbour; the angle between the best fitting planes and the Hausdorff distance. Butkienicz et al [BCWR08] propose a solution for urban LIDAR change detection based on the projection of the point on a 2D Delaunay triangulation. Zeibak and Filin [ZF07] convert a 3D laser scan in a range panorama whose axes are the latitudinal and longitudinal scanning angles and the ranges are the intensity values.…”

“…Butkienicz et al. [BCWR08] propose a solution for urban LIDAR change detection based on the projection of the point on a 2D Delaunay triangulation. Zeibak and Filin [ZF07] convert a 3D laser scan in a range panorama whose axes are the latitudinal and longitudinal scanning angles and the ranges are the intensity values.…”

3D feature surface properties and their application in geovisualization

“…Distant objects receive a small amount of screen space, which may be too small for presenting information. For the task of change detection, Butkiewicz et al (2008) propose the use of additive splatting to highlight changes. In other contexts, levels of detail or generalization are used.…”

Automatic Detection of Buildings and Changes in Buildings for Updating of Maps

“…Vögtle and Steinle's method [12] compared laser DSMs in an object-based manner by analyzing building objects that were first extracted from the DSMs. Other studies using multitemporal laser scanner or image data have been presented, for example, by Jung [13], Vu et al [14], Butkiewicz et al [15], and Nakagawa and Shibasaki [16].…”