Generative Adversarial Networks for Urban Block Design Stanislava Fedorova∗ [email protected] Politecnico di Milano Milan, MI, Italy ABSTRACT a set of parameters determining an optimal solution for a given Development and diffusion of machine learning and big data tools area. Some of them are dictated by the local regulations, some by provide a new tool for architects and urban planners that could be common knowledge, some by the sense of aesthetics; sometimes it used as analytical or design instruments. The topic investigated in is difficult to formulate them using words while algorithms would this paper is the application of Generative Adversarial Networks require formulae or numeric representation. to the design of an urban block. The research presents a flexible One of the problems involving a manifold of parameters is a model able to adapt to the morphological characteristics of a city. design of an urban block. This problem takes into consideration the This method does not define explicitly any of the parameters ofan shapes of the buildings, their proportions, relationships with the urban block typical for a city, the algorithm learns them from the adjacent buildings, the composition of the open space, relationships existing urban context. This approach has been applied to the cities of buildings with the open space, with the urban block itself, rela- with different morphology: Milan, Amsterdam, Tallinn, Turin, and tionships with the buildings of the adjacent blocks. Block design Bengaluru in order to see the performance of the model and the involves the knowledge of the typologies of the blocks in a city, possibility of style translation between different cities. The data surroundings (streets, squares, railways nearby), etc. Moreover, it are gathered from Openstreetmap and Open Data portals of the should smoothly integrate into the existing urban context, which cities. This research presents the results of the experiments and means taking into consideration the disposition and typology of their quantitative and qualitative evaluation. the adjacent blocks. All of the parameters mentioned above would be difficult to define for each block a design should be produced to. CCS CONCEPTS With the recent introduction of Generative Adversarial Net- works (GANs)[15], it became possible to use the neural networks’ • Applied computing ! Computer-aided design; • Comput- ability to autonomously learn visual features in order to generate ing methodologies ! Neural networks. new content. The main advantage of this technique applied to gen- KEYWORDS erative design is the elimination of the necessity to determine any parameters of the desired design solution and learning the visual generative design; generative adversarial networks; urban morphol- features determining the existing city structure and consequently ogy; artificial intelligence; machine learning. generating blocks imitating this structure. ACM Reference Format: Stanislava Fedorova. 2021. Generative Adversarial Networks for Urban 2 RELATED WORK Block Design. In SimAUD 2021: ASymposium on Simulation for Architecture As the concept of GANs has been introduced quite recently[15] and Urban Design, April 15–17, 2021, Online. ACM, New York, NY, USA, there has not been much research on the topic concerning archi- 9 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn tecture and urban design, although it is well-known in the field of computer science. 1 INTRODUCTION Prior work in the generative urban design field is mostly based Urban design solutions determine many important aspects of urban on a set of defined rules and constraints according to which several life: economical, ecological, social aspects, transportation, pedes- design options are generated[5]. Nagy, Villaggi, and Benjamin[18] trian flows, consequently, multiple parameters are involved. The arXiv:2105.01727v1 [cs.CV] 4 May 2021 explore optimization of the problem complexity with the set of two necessity to consider many different factors interacting with each principal goals and the use of genetic algorithms. However, their other made this problem a challenge in the field of generative de- approach, too, needs a set of constraints and requirements; their sign. The result of an urban project has a long-lasting effect on model outputs a design space out of which, after optimization, only various spheres of life, which makes finding an optimal solution a few designs are chosen as final. even more important. The issue arises when it is necessary to define The use of GANs has been explored in interior design as well as in visualization[22] and simulation. Stanislas Chaillou[8] explored Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed the functionality of the method applied to the design of the apart- for profit or commercial advantage and that copies bear this notice and the full citation ments generating their shape, internal structure, functional zoning, on the first page. Copyrights for components of this work owned by others than ACM and furniture placement. The same approach at the same architec- must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a tural scale[14] was used for the simulation of natural illumination fee. Request permissions from [email protected]. in an apartment provided its plan. Another work published in 2020 SimAUD 2021, April 15–17, 2021, Online used a GAN with a different architecture with respect to ours for © 2021 Association for Computing Machinery. ACM ISBN 978-1-4503-XXXX-X/21/04...$15.00 landuse planning[21] with the use of predefined parameters that a https://doi.org/10.1145/nnnnnnn.nnnnnnn planner considers important for his solution and the zone design. SimAUD 2021, April 15–17, 2021, Online Fedorova, et al. However, this approach produces only a large-scale distribution of an empty polygon in the center of the image, destined to be filled the functional zoning in the city and is not applicable to the urban with the buildings as in Figure1. Code for dataset generation is design. available in the repository[12]. The present work tackles a different scale problem: urban block The data was acquired from open sources: Open Data Portal of level design. Instead of using a set of rules exploited by the gener- Comune di Milano [10] available under CC License [1] and Open- ative urban design models previously, the GAN learns the set of streetmap.org [19] available under the Open Database Licence. The parameters present in the city block design from the visual features absent parameters have been considered as a mean value for the of the images. A work that uses GANs in architecture on a scale city. No modifications have been made to the datasets provided. that is similar to ours is the project This Map Does Not Exist[7]. However, this work uses a different architecture that does not allow the production of the designs based on the existing input: the maps are not attached to any existing point in the real world and the existing conditions of an urban environment are not taken into account when producing an image. One more work[9] uses gener- ative networks for the generation of an urban street layout. This approach is the closest to our work both in terms of urban scale and the possibility of conditional generation. The problem is explored from a different point of view: the authors use graph generation to produce a road design while our research tackles the generation of the urban blocks. Importantly, our research combines both the challenges faced in Figure 1: Sample from Milan Dataset of urban blocks: on the the previously mentioned works using GANs: production of a de- left an image from set A, on the right a corresponding image sign solution based on the chosen parameters of the surrounding from set B. The white block in the center of the left image is environment and the integrity of the solution within its urban the block to be designed. block. 3 DATA SAMPLING AND ACQUISITION 4 ARCHITECTURE Firstly, the data of the city of Milan was used to make a dataset. The architecture used for this research is a GAN for Image-to-Image This city has been chosen due to its characteristic morphology and translation [17]. The algorithm consists of two models: Generator relatively consistent urban fabric inside the external ring road and Network based on Unet architecture and Discriminator Network within a 1 km distance from it. based on PatchGAN Convolutional Network. The principle is in The dataset consists of images that are diagrammatic represen- mutual learning: the generator generates an output image based tations of the city zones. It has been decided to start with the basic on the condition (input image). The discriminator model receives parameters determining the composition of a building block: build- generated images and target images and learns to distinguish be- ing footprints, building heights’ categories, road structure, and tween them, classifying each of them into a ’real’ or a ’fake’ class. railways. The heights of the buildings have been divided into 3 In the case of PatchGAN, the model learns to classify patches of a ranges: low-rise structures, medium-rise structures, and high-rise given image as real or fake instead of giving a result for the whole structures depending on the distribution of the heights in the city. image sample, so it outputs the result over which areas of an image Heights in the diagrammatic representation are defined as three should be improved by the generator. gray values. In case of height not having been mentioned for a Three different implementations of the algorithm were used, building, a mean value for the city was used to denote its height.