Improving the Sampling Strategy for Point-to-Point Line-Of-Sight Modelling in Urban Environments

Abstract

Visibility modelling calculates what an observer could theoretically see in the surrounding region based on a digital model of the landscape. In some cases it is not necessary, nor desirable, to compute the visibility of an entire region (i.e. a viewshed), but instead it is sufficient and more efficient to calculate the visibility from point-to-point, or from a point to a small set of points, such as computing the intervisibility of predators and prey in an agent based simulation. This paper explores how different line-of-sight (LoS) sample ordering strategies increases the number of early target rejections, where the target is considered to be obscured from view, thereby improving the computational efficiency of the LoS algorithm. This is of particular importance in dynamic environments where the locations of the observers, targets and other surface objects are being frequently updated. Trials were conducted in three UK cities, demonstrating a robust five-fold increase in performance for two strategies (hop, divide and conquer). The paper concludes that sample ordering methods do impact overall efficiency, and that approaches which disperse samples along the LoS perform better in urban regions than incremental scan methods. The divide and conquer method minimises elevation interception queries, making it suitable when elevation models are held on disk rather than in memory, while the hopping strategy was equally fast, algorithmically simpler, with minimal overhead for visible target cases.