GAN Loci

Steinfeld, Kyle

doi:10.52842/conf.acadia.2019.392

Cited by 12 publications

(6 citation statements)

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“…The application of machine learning at the micro level of urban planning mainly focuses on analysis of the spatial environment. These applications include measuring physical settings, predicting human perceptions of a place (Zhang et al 2018), generating street images (Steinfeld 2019), and developing intelligent tools for designers. (Naderi and Raman 2005).…”

Section: Machine Learning Methods In Urban Planningmentioning

confidence: 99%

A Machine Learning Method of Predicting Behavior Vitality Using Open Source Data

Sun,

Scelsa,

Jiang

et al. 2020

Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA)

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The growing popularity of machine learning has provided new opportunities to predict certain behaviors precisely by utilizing big data. In this research, we use an image-based neural network to explore the relationship between the built environment and the activity of bicyclists in that environment. The generative model can produce heat maps that can be used to predict quantitatively the cycling and running activity in a given area, and then use urban design to enhance urban vitality in that area. In the machine learning model, the input image is a plan view of the built environment, and the output image is a heat map showing certain activities in the corresponding area. After it is trained, the model yields output (the predicted heat map) at an acceptable level of accuracy. The heat map shows the levels and conditions of the subject activity in different sections of the built environment. Thus, the predicted results can help identify where regional vitality can be improved. Using this method, designers can not only predict the behavioral heat distribution but also examine the different interactions between behaviors and aspects of the environment. The extent to which factors might influence behaviors is also studied by generating a heat map of the modified plan. In addition to the potential applications of this approach, its limitations and areas for improvement are also proposed.

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Section: Machine Learning Methods In Urban Planningmentioning

confidence: 99%

A Machine Learning Method of Predicting Behavior Vitality Using Open Source Data

Sun,

Scelsa,

Jiang

et al. 2020

Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA)

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“…The design of facades has also been revolutionized, with systems offering facade suggestions highlighted in work of Kelly et al (2018). Moreover, these algorithms enhance the creation of views and perspectives, a concept explored in the GAN model of Kyle Steinfeld (2019) They extend to the engineering domain as well, aiding in the design of structures as noted in R. Danhaive and C.T. Mueller's paper (2021), and have notable applications in simulation analysis for performance optimization, as discussed by Quintana et al(2020).…”

Section: Evaluation Of Generative Ai Toolsmentioning

confidence: 99%

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

Zeytin,

Öztürk Kösenciğ,

Öner

2024

Journal of Computational Design

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This study explores the integration of Generative Design Assistants (GDAs), specifically machine learning based tools, in the architectural design process. It investigates how these tools, once confined to experimental realms, are now influencing mainstream architectural practice, particularly among novice architects. The research focuses on third and fourth-year architecture students, examining how they adapt to and integrate these advanced AI tools into their design workflows. Through an empirical online workshop, the study collected data of design process recordings, design output success scores of students by an independent jury, and post-experiment surveys. This approach provided insights into the timing, frequency, and sequence of GDA usage, as well as the influence of specific GDA features on design success. The research reveals that three primary strategies emerged in students' GDA usage: continuous use throughout the design process, selective problem-solving use, and initial ideation use followed by traditional methods. However, an over-reliance on GDAs was noted to potentially limit the designer’s interpretive and developmental input. The survey shows that different GDAs have distinct strengths and impacts on the design process. In terms of selected GDAs for the experiment, ArchiGAN aids in discovery and ideation, while HouseGAN excels in reframing design problems. In conclusion, the study underscores the transformative potential and challenges of GDAs in architectural design and highlights the need for balanced GDA integration. The research outputs show that future research should focus on the long-term implications of GDAs in architectural education. This research aims to guide the effective integration of AI in architecture, enhancing the human designer's role rather than overshadowing it.

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“…Non solo, nel panorama della grafica generativa si sperimentano complesse rappresentazioni con l'intelligenza artificiale -immagini sintetiche, dipinti, scrittura automatica -partendo da un programma che immagazzina ed impara informazioni che stabilisce il progettista. In questo caso, più strettamente legato all'ambito città, risulta particolarmente interessante la sperimentazione di K. Steinfeld con il progetto GAN Loci, nel tentativo di documentare il Genius Loci di una città prendendo in considerazione forme, spazi e luce che definiscono un luogo e lo contraddistinguono e combinando questi elementi con l'uso di una rete neurale [4].…”

Section: Alterità Della Città E Flessibilità D'immagineunclassified

Comunicazione della città contemporanea: la grafica generativa per le identità visive dinamiche

Irene De Natale

2023

Transizioni / Transitions

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Facendo riferimento alla rappresentazione della città contemporanea, il presente saggio intende proporre una riflessione critica sulle tecniche e le metodologie che possono contribuire alla formazione della sua immagine identitaria. Naturalmente, tenendo in considerazione la complessità e le contraddizioni che sempre più alimentano il contesto urbano, i processi di rappresentazione identitaria presi in esame sono quelli relativi ai software di grafica generativa che, come noto, permettono di sperimentare linguaggi alternativi che si differenzino rispetto all'attuale sistema di identità visiva della città, il quale ha bisogno di aggiornarsi, assecondando le esigenze di un'identità dinamica e mutevole. Il saggio vuole contribuire ad evidenziare potenzialità e limiti che questi sistemi possono offrire alla cultura visiva grazie al progresso tecnologico, soprattutto in relazione all'equilibrio tra progetto e automatismo previsti da questa modalità di rappresentazione. Parole chiaveCittà, grafica generativa, alterità, identità visiva Collage realizzato tramite l'AI Dall.e con le parole 'rappresentare la complessità della città di Genova tra mare, terra, cielo, storico e moderno'. Elaborazione dell'autrice.

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GAN Loci

Cited by 12 publications

References 0 publications

A Machine Learning Method of Predicting Behavior Vitality Using Open Source Data

A Machine Learning Method of Predicting Behavior Vitality Using Open Source Data

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

Comunicazione della città contemporanea: la grafica generativa per le identità visive dinamiche

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