Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	cdrf2022_293
id	cdrf2022_293
authors	Amal Algamdey, Aleksander Mastalski, Angelos Chronis, Amar Gurung, Felipe Romero Vargas, German Bodenbender, and Lea Khairallah
year	2022
title	AI Urban Voids: A Data-Driven Approach to Urban Activation
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_26
summary	With the development of digital technologies, big urban data is now readily available online. This opens the opportunity to utilize new data and create new relationships within multiple urban features for cities. Moreover, new computational design techniques open a new portal for architects and designers to reinterpret this urban data and provide much better-informed design decisions. The “AI Urban Voids'' project is defined as a data-driven approach to analyze and predict the strategic location for urban uses in the addition of amenities within the city. The location of these urban amenities is evaluated based on predictions and scores followed by a series of urban analyses and simulations using K-Means clustering. Furthermore, these results are then visualized in a web-based platform; likewise, the aim is to create a tool that will work on a feedback loop system that constantly updates the information. This paper explains the use of different datasets from Five cities including Melbourne, Sydney, Berlin, Warsaw, and Sao Paulo. Python, Osmx libraries and K-means clustering open the way to manipulate large data sets by introducing a collection of computational processes that can override traditional urban analysis.
series	cdrf
email
last changed	2024/05/29 14:02

_id	ecaade2022_175
id	ecaade2022_175
authors	Di Carlo, Raffaele, Mittal, Divyae and Vesely, Ondrej
year	2022
title	Generating 3D Building Volumes for a Given Urban Context using Pix2Pix GAN
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 287–295
doi	https://doi.org/10.52842/conf.ecaade.2022.2.287
summary	Our ability to delegate the most intellectually demanding tasks to machines improves with each passing day. Even in the fields of architecture and design, which were previously thought to be exclusive domain of human creativity and flare, we are moving the first steps towards developing models that can capture the patterns, invisible to the naked eye, embedded in the creative process. These patterns reflect ideas and traditions, imprinted in the collective mind over the course of history, that can be improved upon or serve as a cautionary tale for the new generation of designers in their work of designing an equitable, more inclusive future. Generative Adversarial Networks (GANs) give us the opportunity to turn style and design into learnable features that can be used to automatically generate blueprints and layouts. In this study, we attempt to apply this technology to urban design and to the task of generating a building footprint and volume that fits within the surrounding built environment. We do so by developing a Pix2Pix model composed of a ResNet-6 generator and a Patch discriminator, applying it to satellite views of neighborhoods from across the Netherlands, and then turning the resulting 2D generated building footprint into a reusable 3D model. The model is trained using the national cadastral data and TU Delft 3D BAG dataset. The results show that it is possible to predict a building shape compatible in style and height with the surroundings. Although the model can be used for different applications, we use it as an evaluation tool to compare the design alternatives fitting the desired contextual patterns.
keywords	Generative Adversarial Networks, Urban Design, Pix2Pix, Raster Vectorization, 3D Rendering
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_114
id	caadria2022_114
authors	Dong, Zhiyong, Lin, Jinru, Wang, Siqi, Xu, Yijia, Xu, Jiaqi and Liu, Xiao
year	2022
title	Where Will Romance Occur, A New Prediction Method of Urban Love Map through Deep Learning
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 213-222
doi	https://doi.org/10.52842/conf.caadria.2022.1.213
summary	Romance awakens fond memories of the city. Finding out the relationship between romantic scene and urban morphology, and providing a prediction, can potentially facilitate the better urban design and urban life. Taking the Yangtze River Delta region of China as an example, this study aims to predict the distribution of romantic locations using deep learning based on multi-source data. Specifically, we use web crawlers to extract romance-related messages and geographic locations from social media platforms, and visualize them as romance heatmap. The urban environment and building features associated with romantic information are identified by Pearson correlation analysis and annotated in the city map. Then, both city labelled maps and romance heatmaps are fed into a Generative Adversarial Networks (GAN) as the training dataset to achieve final romance distribution predictions across regions for other cities. The ideal prediction results highlight the ability of deep learning techniques to quantify experience-based decision-making strategies that can be used in further research on urban design.
keywords	Romance Heatmap, Generative Adversarial Networks, Deep Learning, Big Data Analysis, Correlation Analysis, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_272
id	caadria2022_272
authors	Dong, Zhiyong
year	2022
title	Perceiving Fabric Immersed in Time, an Exploration of Urban Cognitive Capabilities of Neural Networks
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 263-272
doi	https://doi.org/10.52842/conf.caadria.2022.1.263
summary	City develops gradually with the lapse of time. Cities, as a ‚container‚, are injected new urban elements along the trajectory of the times and the progress of human civilization, constructing the historical structures involved past, present and future. Thus, the cultural information of each era is preserved in the urban fabric together and urban fabric features are complex and rich, which are difficult to capture in traditional design methods. In this paper, we try to use Generative Adversarial Networks (GAN), one of the neural network algorithms, to explore the inner rules of complex urban morphological features and realize the perception of the urban fabric. Neural networks are innovatively applied to the larger and more complex city generation in this experiment. First, we collect European urban fabric as the dataset, then label data to facilitate machine training, use GAN to learn the feature of the dataset by adjusting parameters, and analyze the effect of the generated results. The automatic feature learning capability of the neural networks is used to summarize the inherent patterns and rules in urban development which is difficult for human to discover.
keywords	Deep Learning, Generative Adversarial Networks, Generative Design, Morphology Cognition, Urban Fabric, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_154
id	ecaade2022_154
authors	Ferretti, Maddalena, Di Leo, Benedetta, Quattrini, Ramona and Vasic, Iva
year	2022
title	Creativity and Digital Transition in Central Apennine - Innovative design methods and digital technologies as interactive tools to enable heritage regeneration and community engagement
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 187–196
doi	https://doi.org/10.52842/conf.ecaade.2022.2.187
summary	This contribution proposes strategies of reactivation of the central Apennine of Marche Region in Italy through creative design methods and virtual technologies. The research activities are connected to two related PhD projects: one focusing on architectural and urban design, the other one on heritage digitalization and new technologies and to other research activities of our interdisciplinary team. Cagli, a small town of 8.000 inhabitants, is currently undergoing socio-economic transformations that need to be addressed strategically with a cultural and spatial perspective. The research explores regenerative solutions and local development strategies to enhance the city and its cultural landscape. Participatory processes aided by digital tools and innovative design methods are tested in Cagli’s living lab. The final output of the overall research is a “Reactive Map” combining a trans-scalar and multidisciplinary territorial analysis with visions to identify “potential spaces”. The map is a design tool to define a shared strategy of enhancement of the city and its heritage. With this paper we present one of the methodological steps of the research, a WEB-APP built upon a point clouds database and assessed through a preliminary user test. The highly descriptive 3D environment is able to collect analysis and to be enriched in a participatory way during planned activities of co-thinking. The 3D environment, improved with interviews, plans, historical pictures and other media contents, is also paired with a virtual tour to offer a different representation of the “potential spaces”. The fully boosting 3D digital technology thus represents a viable and effective solution to involve citizens and an innovative and interdisciplinary tool for knowledge advancement in the fields of architectural and urban design and heritage regeneration.
keywords	Tangible and Intangible Heritage, Co-Thinking, Trans-Scalar Approach, Narrative, Point Clouds Exploitation, Interactive Annotation, Virtual Reality
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_140
id	caadria2022_140
authors	Huang, Shuyi and Zheng, Hao
year	2022
title	Morphological Regeneration of the Industrial Waterfront Based on Machine Learning
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 475-484
doi	https://doi.org/10.52842/conf.caadria.2022.1.475
summary	The regeneration of the industrial waterfront is a global issue, and its significance lies in transforming the waterfront brownfield into an eco-friendly, hospitable, and vibrant urban space. However, the industrial waterfront naturally has comparatively unmanageable morphological features, including linear shape, irregular waterfront boundary, and separation with urban networks. Therefore, how to subdivide the vacant land and determine the land-use type for each subdivision becomes a challenging problem. Accordingly, this study proposes an application of machine learning models. It allows the generation of morphological elements of the vacant industrial waterfront by comparing the before-and-after scenarios of successful regeneration projects. The data collected from New York City is used as a showcase of this method.
keywords	machine learning, urban morphology, industrial waterfront regeneration, sustainable cities, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_231
id	caadria2022_231
authors	Kim, Frederick Chando and Huang, Jeffrey
year	2022
title	Deep Architectural Archiving (DAA), Towards a Machine Understanding of Architectural Form
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 727-736
doi	https://doi.org/10.52842/conf.caadria.2022.1.727
summary	With the ‚digital turn‚, machines now have the intrinsic capacity to learn from big data in order to understand the intricacies of architectural form. This paper explores the research question: how can architectural form become machine computable? The research objective is to develop "Deep Architectural Archiving‚ (DAA), a new method devised to address this question. DAA consists of the combination of four distinct steps: (1) Data mining, (2) 3D Point cloud extraction, (3) Deep form learning, as well as (4) Form mapping and clustering. The paper discusses the DAA method using an extensive dataset of architecture competitions in Switzerland (with over 360+ architectural projects) as a case study resource. Machines learn the particularities of forms using 'architectural' point clouds as an opportune machine-learnable format. The result of this procedure is a multidimensional, spatialized, and machine-enabled clustering of forms that allows for the visualization of comparative relationships among form-correlated datasets that exceeds what the human eye can generally perceive. Such work is necessary to create a dedicated digital archive for enhancing the formal knowledge of architecture and enabling a better understanding of innovation, both of which provide architects a basis for developing effective architectural form in a post-carbon world.
keywords	artificial intelligence, deep learning, architectural form, architectural competitions, architectural archive, 3D dataset, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_464
id	caadria2022_464
authors	Liu, Xinyu and van Ameijde, Jeroen
year	2022
title	Data-driven Research on Street Environmental Qualities and Vitality Using GIS Mapping and Machine Learning, a Case Study of Ma On Shan, Hong Kong
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 485-494
doi	https://doi.org/10.52842/conf.caadria.2022.1.485
summary	In a post-carbon framework, data-driven methods can be used to assess the environmental quality and sustainability of urban streetscape. Streets are an important part of people's daily lives and provide places for social interaction. Therefore, in this study, the relationship between street quality and street vibrancy is measured using the new town of Ma On Shan, Hong Kong as a study area. Firstly, machine learning was used to identify the physical features of streets through geographic information collection and streetscape image acquisition. Secondly, previous measurement algorithms are combined to calculate the greenness, walkability, safety, imageability, enclosure, and complexity of streets. Thirdly, secondary calculations and visualisations were carried out on a Geographic Information System (GIS) platform to observe the current distribution of street qualities. Finally, the relationship between street quality and vibrancy was analysed using SPSS statistical analysis software. The results show that walkability has a positive effect on street vitality, whereas safety and complexity have a negative effect on street vitality. This study demonstrates how the quantitative assessment of urban street environments can be used as a reference for building a green, low-carbon, healthy, and walkable city.
keywords	Street Quality, Geographic Information Systems, Machine Learning, Image Segmentation, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_264
id	ecaade2022_264
authors	Sanatani, Rohit Priyadarshi
year	2022
title	Democratizing Urban Data - A smartphone-based framework for rapid cataloging of geolocated street-level imagery and visual content analysis
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 511–516
doi	https://doi.org/10.52842/conf.ecaade.2022.1.511
summary	The commercial availability of high-resolution street view imagery, most notably Google Street View, has led to its widespread use in urban analytics research over the past couple of years. Recent developments in computer vision, most notably semantic segmentation and object detection, have made it possible to extract and map the visual features of streetscapes (such as buildings, automobiles, green cover, pedestrians etc.) using geo-located street level photographs. However, the absence of such detailed imagery in many parts of the world stands as a significant deterrent to these research methodologies. A majority of countries in Africa, the Middle East, as well as some parts of Asia currently have limited coverage by street view image providers. The cost component and equipment involved in manual data collection stands as a barrier to accessible urban visual data. This paper demonstrates a quick and inexpensive smartphone-based framework for rapid and inexpensive collection and cataloging of geolocated street-level imagery. The user walks/drives down the streets to be mapped with a smartphone, as a first-person egocentric hyper-lapse video is recorded with a fixed frame interval, along with location information for the path taken. The video frames are then automatically extracted, geo-referenced and stored in a readily retrievable format. This data can then easily be used for urban feature extraction through computer vision workflows. For demonstration, imagery has been cataloged for a ~1.5 sq.km urban area in New Delhi, and then processed through a semantic segmentation workflow for visual feature mapping. It is hoped that this framework plays a role in democratizing access to street level data for students and researchers regardless of national boundaries.
keywords	Street View Imagery, Democratizing Data, Hyperlapse Photography, Smartphone, Urban Analytics
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_215
id	caadria2022_215
authors	Settimi, Andrea, Vestartas, Petras, Gamerro, Julien and Weinand, Yves
year	2022
title	Cockroach: an Open-source Tool for Point Cloud Processing in CAD
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 325-334
doi	https://doi.org/10.52842/conf.caadria.2022.2.325
summary	In the architecture, engineering and construction (AEC) sector, the use of point cloud data is not a novelty. Usually employed to retrieve data for inspecting construction sites or retrofitting pre-existing buildings, sensors like LiDAR cameras have been known to practitioners such as architects and engineers for a while now. In recent years, the growing interest in 3D data acquisition for autonomous vehicles, robotic and extended reality (XR) applications has brought to the market new compact, performant, and more accessible hardware leveraging different technologies able to provide low-cost sensing systems. Nevertheless, point clouds obtained from such sensors must be processed to extract valuable data for any design or fabrication application. Unfortunately, most advanced point cloud processing tools are written in low-level languages and are hardly accessible to the average designer or maker. Therefore, we present Cockroach: a link between computer-aided design (CAD) modeling software and low-level point cloud processing libraries. The main objective is an adaptation to C# .NET via Grasshopper visual scripting interface and C++ single-line commands in native Rhinoceros workspaces. Cockroach has proved to be a handy design tool in integrating building components with unpredictable geometries such as raw wood or mineral scraps into new design and industrial fabrication processes.
keywords	Computer-vision, Point-clouds, Data-processing, 3D modeling, CAD interface, Open-source tools, Quality education, Industry innovation and infrastructure, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_68
id	caadria2022_68
authors	Carta, Silvio, Turchi, Tommaso and Pintacuda, Luigi
year	2022
title	Measuring Resilient Communities: an Analytical and Predictive Tool
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 615-624
doi	https://doi.org/10.52842/conf.caadria.2022.1.615
summary	This work presents the initial results of an analytical tool designed to quantitatively assess the level of resilience of urban areas. We use Deep Neural Networks to extract features of resilience from a trained model that classifies urban areas using a pre-assigned value range of resilience. The model returns the resilience value for any urban area, indicating the distance between the centre of the selected area and relevant typologies, including green areas, buildings, natural elements and infrastructures. Our tool also indicates the urban morphological characteristics that have a larger impact on the resilience score. In this way we can learn why a neighbourhood is successful (or not) and how to improve its level of resilience. The model employs Convolutional Neural Networks (CNNs) with Keras on Tensorflow for the computation. The outputs are loaded onto a Node.JS environment and bootstrapped with React.js to generate the online demo.
keywords	sustainable cities and communities, resilient communities, CNN, urban morphology, SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_427
id	caadria2022_427
authors	Ding, Xinyue, Guo, Xiangmin, Lo, Tian Tian and Wang, Ke
year	2022
title	The Spatial Environment Affects Human Emotion Perception-Using Physiological Signal Modes
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 425-434
doi	https://doi.org/10.52842/conf.caadria.2022.2.425
summary	In the past, spatial design was mainly from the perspective of designers. With the increasing demand for quality spaces, contemporary architecture has gradually shifted from focusing on form creation to human well-being, once again advocating the concept of "human-centered" spatial design. Exploring how the spatial environment affects human emotions and health is conducive to quantifying the emotional perception characteristics of space and promoting the improvement of human quality of life and sustainable survival. At the same time, the development of contemporary technology and neuroscience has promoted the study of the impact of spatial environment on human emotion perception. This paper summarizes the research on the impact of the spatial environment on human emotion perception in recent years. First, 28 relevant studies were screened using the PRISMA framework. Then a set of research processes applicable to this study is proposed. Next, the physiological signals currently used to study the effects of the spatial environment on human emotions are summarized and analyzed, including electroencephalography (EEG), skin response (GSR), pulse (PR), and four other signals. The architectural features studied in the related literature are mainly building structural features, building spatial geometric features, and building spatial functional attributes. The study of urban space is divided into different parts, such as urban environment characteristics and urban wayfinding behavior. Finally, we point out the shortcomings and perspectives of studies related to the influence of spatial environment on human emotion perception.
keywords	Architectural space environment, urban space, human emotional feelings, Physiological signals, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_93
id	caadria2022_93
authors	Feng, Jiajia, Liang, Yuebing, Hao, Qi, Xu, Ke and Qiu, Waishan
year	2022
title	POI Data Versus Land Use Data, Which Are Most Effective in Modelling Theft Crimes?
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 425-434
doi	https://doi.org/10.52842/conf.caadria.2022.1.425
summary	Alleviating crime and improving urban safety is important for sustainable development of society. Prior studies have used either land use data or point-of-interests (POI) data to represent urban functions and investigate their associations with urban crime. However, inconsistent and even contrary results were yielded between land use and POI data. There is no agreement on which is more effective. To fill this gap, we systematically compare land use and POI data regarding their strength as well as the divergence and coherence in profiling urban functions for crime studies. Three categories of urban function features, namely the density, fraction, and diversity, are extracted from POI and land use data, respectively. Their global and local strength are compared using ordinary least square (OLS) regression and geographically weighted regression (GWR), with a case study of Beijing, China. The OLS results indicate that POI data generally outperforms land use data. The GWR models reveal that POI Density is superior to other indicators, especially in areas with concentrated commercial or public service facilities. Additionally, Land Use Fraction performs better for large-scale functional areas like green space and transportation hubs. This study provides important reference for city planners in selecting urban function indicators and modelling crimes.
keywords	POI, Land Use, Urban Functions, Theft crime, Predictive Power, SDG 16
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_204
id	caadria2022_204
authors	Narahara, Taro
year	2022
title	Kurashiki Viewer: Qualitative Evaluations of Architectural Spaces inside Virtual Reality
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 11-18
doi	https://doi.org/10.52842/conf.caadria.2022.1.011
summary	This paper discusses how virtual reality (VR) environments can be employed as a data collection tool beyond visualization and representation tools through a simple experiment in a VR space and speculates about its potential applications. Using a VR model that runs on a web browser based on an existing historic town in Japan called Kurashiki, the experiment asked 30 recruited participants to freely walk around and leave ratings on a 5-point scale on any buildings or objects appealing to them. The proposed system in this paper can display points of interest of multiple participants using heatmaps superimposed on a map that can help users visually understand statistical preferences among them. The project's goal is to provide a quantitative means for qualitative values of architectural and urban spaces, making such data more shareable. We intended to show that such a platform could help multiple stakeholders reach better consensuses and possibly collect training datasets for machine learning models that could extract features related to the attractiveness in architecture and urban spaces.
keywords	Virtual reality, subjective evaluation, crowdsourcing, SDG 10, SDG 11.
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_405
id	caadria2022_405
authors	Onishi, Ryo, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2022
title	A Remote Sharing Method of 3D Physical Objects Using Instance-Segmented Real-Time 3D Point Cloud for Design Meeting
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 395-404
doi	https://doi.org/10.52842/conf.caadria.2022.2.395
summary	In the field of architecture and urban design, physical models are used in design meetings. Furthermore, teleconferencing via the internet has begun to be widely used in society due to COVID-19 and in preparation for disasters. Although conventional web conferencing can share only 2D information through screens, it is expected that interactive screen sharing of physical objects will enable smoother remote conferencing. A system that can manipulate point clouds in clusters by dividing real-time point clouds captured from 3D real objects by distance has been reported as a way to share physical objects. However, because the point clouds are divided by distance between the two clusters when the point clouds get closer than some threshold, they become treated as a single object. In this study, we aim to develop a system that uses instance segmentation to divide point clouds by region rather than by distance between objects. This system is expected to contribute to the realisation of better architectural and urban design processes without any misunderstandings among the parties involved and to the reduction of unnecessary energy consumption due to travel for face-to-face meetings.
keywords	remote meeting, fast point cloud, instance segmentation, three-dimensional remote sharing, mixed reality, SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_60
id	caadria2022_60
authors	Chowdhury, Shuva and Hanegraaf, Johan
year	2022
title	Co-presence in Remote VR Co-design: Using Remote Virtual Collaborative Tool Arkio in Campus Design
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 465-474
doi	https://doi.org/10.52842/conf.caadria.2022.2.465
summary	A participatory co-design approach is most often counted as a time-consuming method and ends without any concrete solution. Since the new evolution of virtual reality-based communication tools, researchers are trying to integrate citizens in the spatial design making process in-situ situation. However, there has been little research on how remotely co-presence in VR can integrate end-users in a co-design environment in re-envisioning their own using spaces. This study adopts a remote VR collaborative platform Arkio to involve novice designers remotely to design their known urban places. Participants are in three different virtual communication systems. Groups can actively engage in co-creating 3D artefacts relevant to a virtual urban environment and communicate through audio together in a remote setting. The platform was tested with a group of graduate students. The given design task was to re-envision the urban places of their academic institute campus. The sessions have been recorded and transcribed for analysis. The analysis of remote conversations shows that co-presence existed while they were engaged in co-design.
keywords	Affordable Tools, Remote Collaboration, Virtual Reality, Participatory Design, SDG 11, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ascaad2022_085
id	ascaad2022_085
authors	Cicek, Selen; Koc, Mustafa; Korukcu, Berfin
year	2022
title	Urban Map Generation in Artist's Style using Generative Adversarial Networks (GAN)
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 264-282
summary	Artificial Intelligence is a field that is able to learn from existing data to synthesize new ones using deep learning methods. Using Artificial Neural Networks that process big datasets, complex tasks and challenges become easily resolved. As the zeitgeist suggests, it is possible to produce novel outcomes for future projections by applying various machine learning algorithms on the generated data sets. In that context, the focus of this research is exploring the reinterpretation of 21st century urban plans with familiar artist styles using different subtypes of deep-learning-based generative adversarial networks (GAN) algorithms. In order to explore the capabilities of urban map transformation with machine learning approaches, two different GAN algorithms which are cycleGAN and styleGAN have been applied on the two main data sets. First data set, the urban data set, contains 50 cities urban plans in .jpeg format collected according to the diversity of the urban morphologies. Whereas the second data set is composed of four well-known artist’s paintings, that belong to various artistic movements. As a result of training the same data sets with different GAN algorithms and epoch values were compared and evaluated. In this respect, the study not only investigates the reinterpretation of stylistic urban maps and shows the discoverability of new representation techniques, but also offers a comparison of the use of different image to image translation GAN algorithms.
series	ASCAAD
email
last changed	2024/02/16 13:29

_id	cdrf2022_3
id	cdrf2022_3
authors	Deli Liu and Keqi Wang
year	2022
title	Spatial Analysis of Villages in Jilin Province Based on Space Syntax and Machine Learning
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_1
summary	The development of machine learning technology gives architects and urban planners a new tool that can be used for research and design. The topic of this paper is to analyze the rural space of Jilin Province with the machine learning algorithms and space syntax theory, and to obtain the inherent formation and development laws of rural spatial forms, which can be used as a reference and evaluation system for subsequent rural development, and also can emphasize the locality and continuity of rural development. First, based on geographic information data, researching the connection between the distribution of villages and geographic data at a macro level and to classify them. Then, from each category, selecting one township and use all villages in its area as samples for the more specific study. Spatial features of individual village are extracted based on space syntax theory, and representative spatial features which can as feature values for cluster analysis are selected through comparative analysis. Then classify villages from high-dimensional data and explore their type characteristics. Finally, we hope the result of this study can help provide useful theoretical references for rural construction and nature conservation in the future.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2022_145
id	caadria2022_145
authors	Duering, Serjoscha, Fink, Theresa, Chronis, Angelos and Konig, Reinhard
year	2022
title	Environmental Performance Assessment - The Optimisation of High-Rises in Vienna
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 545-554
doi	https://doi.org/10.52842/conf.caadria.2022.1.545
summary	Our cities are facing different kinds of challenges - in parallel to the urban transformation and densification, climate targets and objectives of decision-makers are on the daily agenda of planning. Therefore, the planning of new neighbourhoods and buildings in high-density areas is complex in many ways. It requires intelligent processes that automate specific aspects of planning and thus enable impact-oriented planning in the early phases. The impacts on environment, economy and society have to be considered for a sustainable planning result in order to make responsible decisions. The objective of this paper is to explore pathways towards a framework for the environmental performance assessment and the optimisation of high-rise buildings with a particular focus on processing large amounts of data in order to derive actionable insights. A development area in the urban centre of Vienna serves as case study to exemplify the potential of automated model generation and applying ML algorithm to accelerate simulation time and extend the design space of possible solutions. As a result, the generated designs are screened on the basis of their performance using a Design Space Exploration approach. The potential for optimisation is evaluated in terms of their environmental impact on the immediate environment.
keywords	simulation, prediction and evaluation, machine learning, computational modelling, digital design, high-rises, SGD 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_456
id	caadria2022_456
authors	Gong, Pixin, Huang, Xiaoran, Huang, Chenyu and White, Marcus
year	2022
title	Quantifing the Imbalance of Spatial Distribution of Elderly Service with Muti-source Data
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 455-464
doi	https://doi.org/10.52842/conf.caadria.2022.1.455
summary	With the growing challenge of aging populations around the world, the study of the elderly service is an essential initiative to accommodate the particular needs of the disadvantaged communities and promote social equity. Previous research frameworks are very case-specific with limited evaluation indicators that cannot be extended to other scenarios and fields. Based on multi-source data and Geographic Information System (GIS), this paper quantifies and visualises the imbalance in the spatial distribution of elderly services in 218 neighbourhoods in Shijingshan District, Beijing, China. Mortality data were obtained, and the most contributing indicators to mortality were investigated by correlation analysis. Finally, mapping between other facility indicators to mortality rates was constructed using machine learning to further investigate the factors influencing the quality of elderly services at the community level. The conclusion shows that the functional density of transportation facilities, medical facilities, living services facilities, and the accessibility of elderly care facilities are most negatively correlated with mortality. The correlation conclusion is combined with a machine learning prediction model to provide future recommendations for the construction of unbalanced elderly neighbourhoods. This research offers a novel systematic method to study urban access to elderly services as well as a new perspective on improving social fairness.
keywords	elderly service facilities, multi-source data, machine learning, SDG 3, SDG 10, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

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