Research on urban landscape design using the interactive genetic algorithm and 3D images

Abstract

Abstract Background Generally, there are different optimal solutions with regard to urban landscape planning depending on the area and the opinions and characteristics of community residents. Furthermore, when considering urban landscape and/or city-planning regulations, it is important to include residents’ opinions based on voluntary activities like participation in town development on a regional scale and its management. However, residents’ opinions are difficult to quantify, as many do not have specialized knowledge. Therefore, when an administrative body plans a city, a system to include residents’ opinions on urban landscape options is required. Methods In this study, an optimization system for urban landscape design was proposed using an interactive genetic algorithm (IGA). In this system, three properties of an urban landscape, that is, wall surface positions, heights, and building textures, were varied and the resulting urban landscape images, developed using OpenGL, were subjectively evaluated by users. Weighted scores were then calculated using the paired comparison method. In this system, a site of 200 m × 70 m was assumed and 20 buildings were located on 20 m × 20 m lots. The building widths were fixed at 20 m, and wall positions from the sidewalk varied from 10 m to 20 m at 2 m intervals. The building heights varied from 20 m to 40 m at 4 m intervals, and eight building textures were considered. Two simulations were performed: Case 1, in which the three parameters were evaluated simultaneously; and Case 2, in which the three parameters were evaluated individually. The same 10 users participated in both cases. Following completion of each case, questionnaires were administered to users in which they were asked to confirm that the results obtained matched their expectations. Results The results demonstrated that individual users were satisfied with the results generated based on their evaluations. In both cases, the results were obtained from the optimal results of the system as the result of questionnaires. Conclusions It is necessary to re-examine the evaluation order and evaluation method used as evaluation order may affect optimal results. Furthermore, since users generated different optimal results, it is necessary to develop an optimization system for urban landscapes that allows for collaboration between users.