Information on spatial distribution of buildings must be explored as part of the process of map generalization. A new approach is proposed in this article, which combines building classification and clustering to enable the detection of class differences within a pattern, as well as patterns within a class. To do this, an analysis of existing parameters describing building characteristics is performed via principal component analysis (PCA), and four major parameters (i.e. convex hull area, IPQ compactness, number of edges, and smallest minimum bounding rectangle orientation) are selected for further classification based on similarities between building characteristics. A building clustering method based on minimum spanning tree (MST) considering rivers and roads is then applied. Theory and experiments show that use of a relative neighbor graph (RNG) is more effective in detecting linear building patterns than either a nearest neighbor graph (NNG), an MST, or a Gabriel graph (GssG). Building classification and clustering are therefore conducted separately using experimental data extracted from OpenStreetMap (OSM), and linear patterns are then recognized within resultant clusters. Experimental results show that the approach proposed in this article is both reasonable and efficient for mining information on the spatial distribution of buildings for map generalization.

In map generalization, scale reduction and feature symbolization inevitably generate problems of overlapping objects or map congestion. To solve the legibility problem with respect to the generalization of dispersed rural buildings, selection of buildings is necessary and can be transformed into an optimization problem. In this paper, an improved genetic algorithm for building selection is designed to be able to incorporate cartographic constraints related to the building selection problem. Part of the local constraints for building selection is used to constrain the encoding and genetic operation. To satisfy other local constraints, a preparation phase is necessary before building selection, which includes building enlargement, local displacement, conflict detection, and attribute enrichment. The contextual constraints are used to ascertain a fitness function. The experimental results indicate that the algorithm proposed in this article can obtain good results for building selection whilst preserving the spatial distribution characteristics of buildings.

Spatial conflicts can remain unresolved during map generalization if only displacement is used, especially for urban building maps. To solve all possible spatial conflicts during urban building map generalization, we propose a collaborative displacement method, combining aggregation, elimination, and constrained reshape. After applying an improved vector field-based displacement for solution of initial conflicts, two types of sequential conflicts are detected and evaluated to identify additional displacement solutions based on a trial and error strategy. If no displacement solution is available, aggregation, elimination, or constrained reshape are selected and applied for the unsolved sequential conflicts based on cartographic rules. For a more reasonable generalization result, an improved constrained reshape approach is also introduced for buildings in conflicts along roads. Two data sets with scales of 1:5000 and 1:25 000 for urban building map were selected to validate this approach. The results indicate that it is reasonable and feasible to solve all defined spatial conflicts in urban building map generalization using our proposed method.

In automatic map compilation, both the scale reduction and the geometric transformation of map features may give rise to spatial conflicts. Several generalization approaches have been established to resolve this problem, mainly for the displacement operator. This paper proposes one such approach that is based on the snake model and multiple agents. It focuses on the resolution of various spatial conflicts that emerge when generalizing the rural areas of medium density. In this approach, the map features are converted into map agent, object agent, group agent, and conflicting agent, and the spatial relationships between these features are managed by an auxiliary relationship agent. Each agent is assigned with tasks accordingly, and all agents collaborate with each other to complete the generalization. The snake-based algorithm is applied in the conflicting agent when it identifies a certain configuration. The experimental results indicate that the approach proposed in this paper can obtain good results.

The resolution of road graphic conflicts is a key aspect of map generalization, which involves both scale reduction and the symbolization of map features. This study proposes collaborative methods of road graphic conflict resolution considering different road characteristics. These methods consider both geometric and semantic characteristics, and they incorporate the bend characteristics of roads, the road symbol size, and road semantics. Constrained Delaunay triangulation skeleton lines are used to categorize road graphic conflicts, which are made up of four independent conflict types and four group conflict types. Based on their characteristics, three collaborative methods are designed to deal with the different types of road graphic conflicts: collaboration between deletion and the snake displacement model, collaboration between the snake displacement model and collinearity, and collaboration among simplification, smoothing, and the beam displacement model. Two types of independent conflicts can be processed using only one simple operation. This study summarizes the cartographic rules for resolving road graphic conflicts, and these are used along with geometric features to drive the collaborative methods or one simple operation presented here. The experimental results indicate that the method proposed in this study can effectively resolve road graphic conflicts.