Simplification and Generalization of Large Scale Data for Roads: A Comparison of Two Filtering Algorithms

Abstract

This paper reports the results of an in-depth study which investigated two algorithms for line simplification and caricatural generalization (namely, those developed by Douglas and Peucker, and Visvalingam, respectively) in the context ofa wider program of research on scale-free mapping. The use of large-scale data for man-designed objects, such as roads, has led to a better understanding of the properties of these algorithms and of their value within the spectrum of scale-free mapping. The Douglas-Peucker algorithm is better at minimal simplification. The largescale data for roads makes it apparent that Visvalingam's technique is not only capable of removing entire scale-related features, but that it does so in a manner which preserves the shape of retained features. This technique offers some prospects for the construction of scale-free databases since it offers some scope for achieving balanced generalizations of an entire map, consisting of several complex lines. The results also suggest that it may be easier to formulate concepts and strategies for automatic segmentation of in-line features using large-scale road data and Visvalingam's algorithm. In addition, the abstraction of center lines may be facilitated by the inclusion of additional filtering rules with Visvalingam's algorithm.

Towards cognitive evaluation of computer-drawn sketches

“…Visvalingam and Whyatt (1990) show that the widely cited mathematical evaluation of line generalisation algorithms by McMaster (1987) is appropriate for minimal simplification (approximation) of curves even if not for their caricatural generalisation. Visvalingam and Williamson (1995) show that the Douglas-Peucker algorithm seems to be better than Visvalingam's algorithm for minimal simplification, but that the latter produces better caricatures. The current P-stroke sketches use 25% to 30% of the DEM cells.…”

“…The significance of the point can be measured in various ways, as Visvalingam and Whyatt (1993) explain. Visvalingam and Dowson (1998) use the effective area metric that Visvalingam and Whyatt (1993) and Visvalingam and Williamson (1995) find to be the most effective for 2D lines. The effective area of a point is the area formed by the point and its two immediate neighbours.…”

Generalization-oriented Road Line Classification by Means of an Artificial Neural Network

“…In our case, the length of the road lines should also be greater than a certain threshold, because a short line presents fewer possibilities of segmentation and generalization. Observing previous studies of line generalization (see Table 1), the sample size is very variable, from the four elements of [7], [14], [25], [56] to those studies in which a large set of lines in a geographic scope is selected without specifying quantity ( [28], [29], [35], [38], [48], [50], [51]). This last option seems more adequate, since the validity of the results should be better and because it is closer to a real case of operating with a cartographic product (such as road elements of a sheet).…”

“…The sheets selected (four 947 sheets from MTN25 that correspond with one generalized sheet from MTN50) are the nearer to our research centre, and contain such a set of road lines that complies with criteria of sample size, inter-group variability and intra-group variability. Comparison between hierarchical and non-hierarchical Douglas-Peucker [50] Road set from a sheet of 500×500 m to 1:1,250 scale…”

A New Algorithm for Cartographic Simplification of Streams and Lakes Using Deviation Angles and Error Bands

“…Smoothing operators produce a line with a more aesthetically pleasing caricature [3]. Detailed explanations and criticisms about line simplification and smoothing algorithms could be obtained from the numerous review papers produced by, for example, White [4], Weibel [5], McMaster [6], Brassel and Weibel [7], Thapa [8], Li [9,10] and Visvalingam and Williamson [11].…”