A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm

Abstract

Owing to map scale reduction and other cartographic generalization operations, spatial conflicts may occur between buildings and other features in automatic cartographic generalization. Displacement is an effective map generalization operation to resolve these spatial conflicts to guarantee map clarity and legibility. In this paper, a novel building displacement method based on multipopulation genetic algorithm (BDMPGA) is proposed to resolve spatial conflicts. This approach introduces multiple populations with different control parameters for simultaneous search optimization and adopts an immigration operation to connect different populations to realize coevolution. The optimal individuals of each population are selected and preserved in the elite population through manual selection operation to prevent the optimal individuals from being destroyed and lost in the evolutionary process. Meanwhile, the least preserving generation of the optimal individuals is used as the termination basis. To validate the proposed method, urban building data with a scale of 1:10,000 from Shenzhen, China are used. The experimental results indicate that the method proposed in this paper can effectively resolve spatial conflicts to obtain better results.

A spatial contextual immune genetic algorithm to building cartographic displacement

“…Another improvement in our algorithm implementation of this article is to use the continuous variable “conflict severity” instead of “conflict number” as the quantified indicator of the remaining conflicts. In most previous studies of combinatorial optimization algorithms, the cost of remaining conflicts in the objective functions was quantified by the number of conflicts (Huang et al, 2017; Li et al, 2020; Mackaness & Purves, 2001; Sun, Guo, Liu, Ma, et al, 2016; Ware et al, 2002, 2003; Ware & Jones, 1998; Wilson et al, 2003), in which the total numbers of conflicts and the total displacement distance constitutes the main components of the objective function. Pilehforooshha et al (2021) added the total conflicting area to the objective function of their IGA.…”

“…Lonergan and Jones (2001) proposed a conflict detection and resolution method based on SGD and geometric reasoning. Similar studies include building displacement based on the Tabu search (TS) algorithm (Ware et al, 2002), the genetic algorithm (GA) (Wilson et al, 2003), IGA (Pilehforooshha et al, 2021; Sun, Guo, Liu, Ma, et al, 2016), particle swarm optimization (Huang et al, 2017), and the multi‐population genetic algorithm (BDMPGA) (Li et al, 2020). The remarkable trait of this kind of algorithms is to evaluate the map state in the iterative search process through an objective function, thus providing a basis for the optimal solution.…”

“…During map generalization, proximity conflicts or symbol overlaps often occur between adjacent buildings or between buildings and other adjacent objects (e.g., roads) because of map scale reduction or symbol exaggeration (Liu et al, 2014). To remove these conflicts, several generalization operators have been employed, such as contextual selection (Wang, Guo, Liu, et al, 2017), aggregation (Ai & Zhang, 2007; Li et al, 2004; Shen et al, 2019), displacement (Ai et al, 2015; Bader, 2001; Bader et al, 2005; Basaraner, 2011; Bobrich, 2001; Burghardt & Meier, 1997; Fei, 2004; Harrie, 1999, 2003; Harrie & Sarjakosk, 2002; Højholt, 2000; Huang et al, 2017; Li et al, 2020; Lichtner, 1979; Liu et al, 2014; Lonergan & Jones, 2001; Mackaness, 1994; Mackaness & Purves, 2001; Peng et al, 1995; Pilehforooshha, et al, 2021; Ruas, 1998; Sahbaz & Basaraner, 2021; Sester, 2005; Sun, Guo, Liu, Lv, et al, 2016; Sun, Guo, Liu, Ma, et al, 2016; Wang, Guo, Wei, et al, 2017; Ware & Jones, 1998; Ware et al, 2002, 2003; Wei, He, et al, 2018; Wilson et al, 2003), and typification (Mao et al, 2012; Regnauld, 2001; Shen et al, 2016; Yan et al, 2021). Among these operators, displacement may be the most frequently used one in a map production environment (Ai et al, 2015; Foerster & Stoter, 2008).…”

Generative Adversarial Networks to Generalise Urban Areas in Topographic Maps

“…The classical generalisation to solve this problem is the displacement of buildings. Several algorithms were proposed, with two main categories: the iterative approaches where buildings are displaced one by one Ruas 1998;Aslan et al 2012;Liu et al 2014, and global approaches where the algorithm searches the optimal position for all the buildings in a block (Gaffuri, 2009;Ai et al, 2015;Li et al, 2020).…”

Building Typification in Map Generalization Using Affinity Propagation Clustering

“…When the scale of the map becomes smaller, the gap between adjacent buildings or between buildings and other related objects (e.g., roads) may be less than the cognition tolerance, potentially resulting in massive spatial overlaps and visual clutter. To reduce these spatial conflicts on a smaller-scale representation, cartographers have designed a series of generalization operators, including aggregation [3,4], displacement [5][6][7], typification [8][9][10], etc. Among them, the typification operator aims to replace a larger number of buildings with a subset, which is necessary when buildings are in conflict that cannot be resolved by displacement or their density is not high enough to aggregate them.…”