Change Detection for Topographic Mapping Using Three- Dimensional Data Structures

Identifying significant changes to our urban areas is a prerequisite for accurate topographic mapping. This paper presents an approach based on octree data structures to identify change between two sets of point cloud data. The aim of the study was to establish if a method based on the comparison of point clouds could be used to detect simply where topographic change had occurred. As many of the changes that could occur are likely to result in a change of real world geometry (for example the construction or demolition of a building) the use of geometric data – rather than imagery alone as in other studies – is justified. Additionally, it means input data can be supplied directly from airborne lidar systems, or from aerial imagery using digital photogrammetric workstations which still remain the most commonly used apparatus for national mapping activities.Octrees are data structures that allow the partitioning of three-dimensional data into increasingly smaller units of space, using predefined criteria to control the level of subdivision (in this case a limit on the number of points in a node; and/or the total level of subdivision). Octrees have previously been used in applications where efficient searching and inspection of large volumes of three-dimensional data is required, such as in the rendering of computer graphics. By defining these structures, large data volumes of non-connected data (such as point clouds) can be efficiently managed and quickly compared with similar datasets collected at different epochs. In the study presented here, one approach compares entire octrees for differences between their structures, while a second approach compares individual data points to data contained in a reference octree.Two UK test areas form the basis of the study. Area 1 is the site of Heathrow Airport’s new Terminal Five which has seen significant development over the last five years. Area 2 is an urban/peri-urban area of Bournemouth consisting of both commercial and residential properties. In both cases, multi epoch data was provided by Ordnance Survey allowing point cloud data to be generated from imagery collected by an Intergraph DMC digital airborne sensor. High resolution photogrammetric processing was undertaken using BAE’s Socet Set and point cloud pre-processing using Terrasolid’s TerraScan suite. While it was possible to recognize significant and pre-identified areas of change using the methodology, a large number of false identifications were also observed, making it difficult to interpret the results without prior knowledge. The lack of success can partially be attributed to the quality of the input data. Slight variations in the point clouds, perhaps arising from poor image correlation during surface extraction, led to subtle variations in the structure of the octree and thus in the changes identified.