Utilising urban context recognition and machine learning to improve the generalisation of buildings

Abstract

The introduction of automated generalisation procedures in map production systems requires that generalisation systems are capable of processing large amounts of map data in acceptable time and that cartographic quality is similar to traditional map products. With respect to these requirements, we examine two complementary approaches that should improve generalisation systems currently in use by national topographic mapping agencies. Our focus is particularly on self-evaluating systems, taking as an example those systems that build on the multi-agent paradigm. The first approach aims to improve the cartographic quality by utilising cartographic expert knowledge relating to spatial context. More specifically, we introduce expert rules for the selection of generalisation operations based on a classification of buildings into five urban structure types, including inner city, urban, suburban, rural, and industrial and commercial areas. The second approach aims to utilise machine learning techniques to extract heuristics that allow us to reduce the search space and hence the time in which a good cartographical solution is reached. Both approaches are tested individually and in combination for the generalisation of buildings from map scale 1:5000 to the target map scale of 1:25 000. Our experiments show improvements in terms of efficiency and effectiveness. We provide evidence that both approaches complement each other and that a combination of expert and machine learnt rules give better results than the individual approaches. Both approaches are sufficiently general to be applicable to other forms of self-evaluating, constraint-based systems than multi-agent systems, and to other feature classes than buildings. Problems have been identified resulting from difficulties to formalise cartographic quality by means of constraints for the control of the generalisation process. The introduction of automated generalisation procedures in map production systems requires that generalisation systems are capable of processing large amounts of map data in acceptable time and that cartographic quality is similar to traditional map products. With respect to these requirements we examine two complementary approaches that should improve generalisation systems currently in use by national topographic mapping agencies. Our focus is particularly on self-evaluating systems, taking as an example those systems that build on the multi-agent paradigm. The first approach aims to improve the cartographic quality by utilising cartographic expert knowledge relating to spatial context. More specifically we introduce expert rules for the selection of generalisation operations based on a classification of buildings into five urban structure types, including inner city, urban, suburban, rural, and industrial and commercial areas. The second approach aims to utilise machine learning techniques to extract heuristics that allow to reduce the search space and hence the time in which a good cartographical solution is reached. Both approaches are t...

Template Matching and Simplification Method for Building Features Based on Shape Cognition

“…First, every adjusted template through the least squares method should satisfy the cartographic constraints, such as the shortest edge greater than 0.3 mm [6,14,42]. Second, the surface distance should be less than a given threshold, and the threshold is set according to the characteristics of different types of buildings.…”

“…Some of these structures are meant more to convey the symbolic meanings and culture characteristics behind map objects, than to convey an exact positioning [4]. Perceived as a group, the overall structure is closely related to the geographical context, is affected by the terrain, and is usually distributed in a dense, clustered pattern [5][6][7][8][9]. There are relatively stable and typical templates or pattern characteristics that can be effectively applied to building generalization [10,11].…”

Improving settlement selection for small-scale maps using data enrichment and machine learning

“…Alternatively, Ruas et al (2006) have learned rules from logs of a self-evaluating generalization system to improve its efficiency. Empirical data may also originate from spatial analysis (Steiniger et al, 2010) and manually be tagged by expert users (Mustière et al, 2000). Machine learning may be used in support of generalization for two purposes: to generate an initial set of rules, when no previously formalized knowledge exists (Weibel et al, 1995a;Plazanet et al, 1998) or -which is the more frequent case -to extend an initial rule set by evaluating the performance of an existing system (Mustière et al, 2000;Duchêne et al, 2005;Ruas et al, 2006;Sheeren et al, 2009).…”

Contextual Building Selection Based on a Genetic Algorithm in Map Generalization

“…These requirements are usually defined as a set of constraints. The constraint-based approach has been actively researched in generalization [11,25,26]. In these studies, constraints have mainly been used to control the process and evaluate the results.…”

“…Instead of the generalization of urban buildings, which has already been studied [25,26], the focus here is on the generalization of rural buildings. The main objective is to transform groups of buildings into a readable form at the target scales by extracting a new representative set of buildings.…”