Steven J Quan
Biographic Data
| ID | 6278465 |
|---|---|
| NAME | Steven J Quan |
| GIVEN NAMES | Steven J |
| FAMILY NAME | Quan |
| SIGNATURE | QUAN S J |
| AFFILIATIONS | Seoul National University |
| ORCID | 0000-0001-9841-4823 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 4 |
| AUTHOR COUNT | 15 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Analyzing seasonal and nonlinear built environment-energy dynamics using street view imagery in New York City
A machine learning-based mediation analysis of urban form influences on building energy use: Evidence from Seoul, South Korea
Augmenting urban planning with computer vision: A review of the state-of-the-art
Computer vision (CV) uncovers hidden patterns, advancing evidence-based urban planning decisions • CV aids urban planning visualisation and automated urban planning and design processes • Machine learning algorithms complement CV, improving classification and modelling tasks • Convolutional neural networks enable CV to excel in classifying diverse urban environment data • CV facilitates more informed decision-making, and advancing urban planning …
Artificial Intelligence-Aided and Data-Driven Design (Aidd) for Participatory Urban Design Computation
The burgeoning resurgence of interest in artificial intelligence (AI) is transforming urban planning and design. While studies have leveraged AI for generative planning and design, the focus has been on augmenting the capabilities of planners and designers, often overlooking public participation. Addressing this gap, this study proposes a new AI-aided and data-driven (AIDD) framework that integrates design, science, and participation to support p…
Deciphering urban land use: A typological analysis of built form across 105 urban areas in the U.S. using representation learning
From pixels to policy: Multi-scale flood susceptibility mapping using interpretable machine learning for urban resilience
Thermal performance-based urban climate zone mapping through multivariate time-series clustering: A case study of Seoul
Discovering urban block typologies in Seoul: Combining planning knowledge and unsupervised machine learning
Urban blocks are fundamental elements of urban form and play an important role in urban development. Identification of their typologies has been a long-standing challenge in urban planning and design. Traditionally, expert-based classification has been used, but recent studies have shifted towards data-driven clustering to achieve better results. However, most studies tend to use only one of these approaches and few integrate both. To address thi…
Identifying urban form typologies in Seoul using a new Gaussian mixture model-based clustering framework
Seoul, the capital city of South Korea, has diverse urban forms developed through its complex history. Previous studies show limitations of strong subjectivity and difficulty in scalability in identifying typical Seoul urban forms with expert knowledge. Data-driven approach offers an opportunity to address those challenges, but previous studies often focused on direct applications of clustering algorithms to a given area with diverse methods and …
Planning decentralized urban renewable energy systems using algal cultivation for closed-loop and resilient communities
To tackle climate challenges, communities need to harvest renewable energy and resources on site locally to close the loops for enhancing the resilience of communities facing unpredictable and uncertain future changes. A decentralization planning of urban renewable energy systems is proposed by treating urban waste streams and producing biomass through applying algal biotechnology. When applying algal technology as a renewable and decentralized e…
Urban-GAN: An artificial intelligence-aided computation system for plural urban design
The current urban design computation is mostly centered on the professional designer while ignoring the plural dimension of urban design. In addition, available public participation computational tools focus mainly on information and idea sharing, leaving the public excluded in design generation because of their lack of design expertise. To address such an issue, this study develops Urban-GAN, a plural urban design computation system, to provide …
A robust metamodel-based optimization design method for improving pedestrian wind comfort in an infill development project
An exploration of the relationship between density and building energy performance
Artificial intelligence-aided design: Smart Design for sustainable city development
Current planning and design decision support systems show limitations in the integration of design, science, and computation. Planning support systems with manual design and post-design evaluations impose major challenges in exploring huge design spaces. Generative design systems largely neglect the wicked nature of design problems and lack appropriate representation methods and simulation tools at the urban scale. To tackle those challenges, thi…
Energy performance simulation for planning a low carbon neighborhood urban district: A case study in the city of Macau
Energy performance simulation for planning a low carbon neighborhood urban district: A case study in the city of Macau
Discovering urban block typologies in Seoul: Combining planning knowledge and unsupervised machine learning
Urban blocks are fundamental elements of urban form and play an important role in urban development. Identification of their typologies has been a long-standing challenge in urban planning and design. Traditionally, expert-based classification has been used, but recent studies have shifted towards data-driven clustering to achieve better results. However, most studies tend to use only one of these approaches and few integrate both. To address thi…
Energy performance simulation for planning a low carbon neighborhood urban district: A case study in the city of Macau
Artificial intelligence-aided design: Smart Design for sustainable city development
Current planning and design decision support systems show limitations in the integration of design, science, and computation. Planning support systems with manual design and post-design evaluations impose major challenges in exploring huge design spaces. Generative design systems largely neglect the wicked nature of design problems and lack appropriate representation methods and simulation tools at the urban scale. To tackle those challenges, thi…
An exploration of the relationship between density and building energy performance
A robust metamodel-based optimization design method for improving pedestrian wind comfort in an infill development project
Planning decentralized urban renewable energy systems using algal cultivation for closed-loop and resilient communities
To tackle climate challenges, communities need to harvest renewable energy and resources on site locally to close the loops for enhancing the resilience of communities facing unpredictable and uncertain future changes. A decentralization planning of urban renewable energy systems is proposed by treating urban waste streams and producing biomass through applying algal biotechnology. When applying algal technology as a renewable and decentralized e…
Urban-GAN: An artificial intelligence-aided computation system for plural urban design
The current urban design computation is mostly centered on the professional designer while ignoring the plural dimension of urban design. In addition, available public participation computational tools focus mainly on information and idea sharing, leaving the public excluded in design generation because of their lack of design expertise. To address such an issue, this study develops Urban-GAN, a plural urban design computation system, to provide …
Identifying urban form typologies in Seoul using a new Gaussian mixture model-based clustering framework
Seoul, the capital city of South Korea, has diverse urban forms developed through its complex history. Previous studies show limitations of strong subjectivity and difficulty in scalability in identifying typical Seoul urban forms with expert knowledge. Data-driven approach offers an opportunity to address those challenges, but previous studies often focused on direct applications of clustering algorithms to a given area with diverse methods and …
Discovering urban block typologies in Seoul: Combining planning knowledge and unsupervised machine learning
Urban blocks are fundamental elements of urban form and play an important role in urban development. Identification of their typologies has been a long-standing challenge in urban planning and design. Traditionally, expert-based classification has been used, but recent studies have shifted towards data-driven clustering to achieve better results. However, most studies tend to use only one of these approaches and few integrate both. To address thi…
Thermal performance-based urban climate zone mapping through multivariate time-series clustering: A case study of Seoul
Analyzing seasonal and nonlinear built environment-energy dynamics using street view imagery in New York City
A machine learning-based mediation analysis of urban form influences on building energy use: Evidence from Seoul, South Korea
Augmenting urban planning with computer vision: A review of the state-of-the-art
Computer vision (CV) uncovers hidden patterns, advancing evidence-based urban planning decisions • CV aids urban planning visualisation and automated urban planning and design processes • Machine learning algorithms complement CV, improving classification and modelling tasks • Convolutional neural networks enable CV to excel in classifying diverse urban environment data • CV facilitates more informed decision-making, and advancing urban planning …
Artificial Intelligence-Aided and Data-Driven Design (Aidd) for Participatory Urban Design Computation
The burgeoning resurgence of interest in artificial intelligence (AI) is transforming urban planning and design. While studies have leveraged AI for generative planning and design, the focus has been on augmenting the capabilities of planners and designers, often overlooking public participation. Addressing this gap, this study proposes a new AI-aided and data-driven (AIDD) framework that integrates design, science, and participation to support p…
Deciphering urban land use: A typological analysis of built form across 105 urban areas in the U.S. using representation learning
From pixels to policy: Multi-scale flood susceptibility mapping using interpretable machine learning for urban resilience
Urban planning (11 works) · Engineering (8 works) · Civil engineering (7 works) · Computer Science (7 works) · Land Use and Ecosystem Services (6 works) · Urban Heat Island Mitigation (6 works) · Building Energy and Comfort Optimization (5 works) · Urban design (5 works) · Artificial Intelligence (4 works) · Geography (4 works)