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	ecaade2021_109
id	ecaade2021_109
authors	Doumpioti, Christina and Huang, Jeffrey
year	2021
title	Intensive Differences in Spatial Design - Reversing form-finding
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 9-16
doi	https://doi.org/10.52842/conf.ecaade.2021.1.009
summary	Drawing from the philosophy of science, 'intensive' qualities define differences in degree instead of 'extensive' ones that define additive quantities. More relevant to architecture, intensive differences can define transient boundaries such as warmness and coolness, dryness and moisture, light and shadow, or visual accessibility, to name a few.The question that serves as a starting point of this study is whether the attributes mentioned above can become form-giving agents during the design process and, therefore, whether they become fundamental parameters for the conceptualization and configuration of extensive spatial qualities. This question is explored using Generative Adversarial Networks and image-to-image translation. The dataset consists of two types of images; one consists of spatial configurations representing extensive attributes. The second set depicts intensive characteristics of visual accessibility. The study proposes a conceptual model and workflow that reverses form-finding and enables the design of environments through the specification of desired intensive attributes. Furthermore, it discusses the advantage of working with this method in search of architectural environments with embedded spatial experiences.
keywords	Intensive Differences; Form-Finding; Isovist Simulation; conditional Generative Adversarial Networks (cGAN)
series	eCAADe
email
last changed	2022/06/07 07:55

_id	cdrf2021_3
id	cdrf2021_3
authors	Jean Jaminet, Gabriel Esquivel, and Shane Bugni
year	2021
title	Serlio and Artificial Intelligence: Problematizing the Image-to-Object Workflow
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_1
summary	Virtual design production demands that information be increasingly encoded and decoded with image compression technologies. Since the Renaissance, the discourses of language and drawing and their actuation by the classical disciplinary treatise have been fundamental to the production of knowledge within the building arts. These early forms of data compression provoke reflection on theory and technology as critical counterparts to perception and imagination unique to the discipline of architecture. This research examines the illustrated expositions of Sebastiano Serlio through the lens of artificial intelligence (AI). The mimetic powers of technological data storage and retrieval and Serlio’s coded operations of orthographic projection drawing disclose other aesthetic and formal logics for architecture and its image that exist outside human perception. Examination of aesthetic communication theory provides a conceptual dimension of how architecture and artificial intelligent systems integrate both analog and digital modes of information processing. Tools and methods are reconsidered to propose alternative AI workflows that complicate normative and predictable linear design processes. The operative model presented demonstrates how augmenting and interpreting layered generative adversarial networks drive an integrated parametric process of three-dimensionalization. Concluding remarks contemplate the role of human design agency within these emerging modes of creative digital production.
series	cdrf
email
last changed	2022/09/29 07:53

_id	sigradi2021_200
id	sigradi2021_200
authors	Karabagli, Kaan, Koc, Mustafa, Basu, Prithwish and As, Imdat
year	2021
title	A Machine Learning Approach to Translate Graph Representations into Conceptual Massing Models
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 191–202
summary	Machine learning (ML) has popular applications in domains involving image, video, text and voice. However, in architecture, image-based ML systems face challenges capturing the complexity of three-dimensional space. In this paper, we leverage a graph-based ML system that can capture the inherent topology of architectural conceptual designs and identify high-performing latent patterns within such designs. In particular, our goal is to translate architectural graph data into three-dimensional massing models. We are building on our prior ML work, where we, a. discovered latent topological features, b. composed building blocks into new designs, c. evaluated their feasibility, and d. explored Generative Adversarial (Neural) Networks (GAN)-generated design variations. We trained the ML system with architectural design data that we gathered from an online architectural design competition platform, translated them into machine-readable graph representations, and identified their essential subgraphs to develop novel compositions. In this paper, we explore how these novel designs (outputted in graph form), can be translated into three-dimensional architectural form. We present an ML approach to turn graph representations into functional volumetric massing models. The ultimate goal of the study is to develop an end-to-end pipeline to generate architectural design - from a graph representation to a fully developed conceptual proxy of a designed product. The research question is promising in automating conceptual design, and we believe the outcome can be relevant to other design disciplines as well.
keywords	Architectural design, machine learning, conceptual design, deep learning, artificial intelligence
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	sigradi2021_134
id	sigradi2021_134
authors	Uzun, Can
year	2021
title	What can Colors and Shapes Tell about Generative Adversarial Networks?
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 161–171
summary	The study aims to understand the how’s and what’s of creating an architectural dataset for generative adversarial nets through the evaluation of the effects of colors and shapes in image datasets on generative adversarial nets. Throughout the paper, six generative adversarial network training sessions are conducted on DCGAN and context-encoder algorithms with three different datasets having different complexities for colors and shapes. Firstly the color and shape complexities are analyzed for datasets. For color complexity, heuristic analyze is applied and for shape complexity, gray level occurrence matrix entropy which gives the textural complexity is utilized. In the end, the complexities and the training results are evaluated. Results show that color complexity has an important role for generative adversarial networks to generate colors correctly. Regularity in shape complexity /gray level co-occurrence matrix entropy distribution facilitates the algorithm training and shape generating processes.
keywords	Context-Encoder, GAN, Colors, Shapes
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	cdrf2021_69
id	cdrf2021_69
authors	Virginia Ellyn Melnyk
year	2021
title	Punch Card Patterns Designed with GAN
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_7
summary	Knitting punch cards codify different stitch patterns into binary patterns, telling the machine when to change color or to generate different stitch types. This research utilizes Neural Networks (NN) and image-based Generative Adversarial Networks (GAN), with an image database of knitting punch cards, to generate new punch card designs. The hypothesis is that artificial intelligence will learn the basic underlying structures of the punch cards and the pattern makeup that is inherent across patterns of different styles and cultures. Different neural networks were utilized throughout the research, such as Neural Style Transfer (NST), AdaIN Style Transfers, and StyleGAN2. The results from these explorations offer different insights into pattern design and various outcomes of the different neural networks. Ultimately physically testing these punch card designs, these patterns were knit on a domestic knitting machine, resulting in novel fabrication and design techniques that are both digital and craft-based.
series	cdrf
email
last changed	2022/09/29 07:53

_id	cdrf2021_26
id	cdrf2021_26
authors	Yuqian Li and Weiguo Xu
year	2021
title	Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_3
summary	Architects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process.By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology.
series	cdrf
email
last changed	2022/09/29 07:53

_id	ijac202119106
id	ijac202119106
authors	Del Campo, Matias; Alexandra Carlson, and Sandra Manninger
year	2021
title	Towards Hallucinating Machines - Designing with Computational Vision
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 88–103
summary	There are particular similarities in how machines learn about the nature of their environment, and how humans learn to process visual stimuli. Machine Learning (ML), more specifically Deep Neural network algorithms rely on expansive image databases and various training methods (supervised, unsupervised) to “make sense” out of the content of an image. Take for example how students of architecture learn to differentiate various architectural styles. Whether this be to differentiate between Gothic, Baroque or Modern Architecture, students are exposed to hundreds, or even thousands of images of the respective styles, while being trained by faculty to be able to differentiate between those styles. A reversal of the process, striving to produce imagery, instead of reading it and understanding its content, allows machine vision techniques to be utilized as a design methodology that profoundly interrogates aspects of agency and authorship in the presence of Artificial Intelligence in architecture design. This notion forms part of a larger conversation on the nature of human ingenuity operating within a posthuman design ecology. The inherent ability of Neural Networks to process large databases opens up the opportunity to sift through the enormous repositories of imagery generated by the architecture discipline through the ages in order to find novel and bespoke solutions to architectural problems. This article strives to demystify the romantic idea of individual artistic design choices in architecture by providing a glimpse under the hood of the inner workings of Neural Network processes, and thus the extent of their ability to inform architectural design.The approach takes cues from the language and methods employed by experts in Deep Learning such as Hallucinations, Dreaming, Style Transfer and Vision. The presented approach is the base for an in-depth exploration of its meaning as a cultural technique within the discipline. Culture in the extent of this article pertains to ideas such as the differentiation between symbolic and material cultures, in which symbols are defined as the common denominator of a specific group of people.1 The understanding and exchange of symbolic values is inherently connected to language and code, which ultimately form the ingrained texture of any form of coded environment, including the coded structure of Neural Networks.A first proof of concept project was devised by the authors in the form of the Robot Garden. What makes the Robot Garden a distinctively novel project is the motion from a purely two dimensional approach to designing with the aid of Neural Networks, to the exploration of 2D to 3D Neural Style Transfer methods in the design process.
keywords	Artificial intelligence, design agency, neural networks, machine learning, machine vision
series	journal
email
last changed	2021/06/03 23:29

_id	caadria2021_117
id	caadria2021_117
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2021
title	Can a Generative Adversarial Network Remove Thin Clouds in Aerial Photographs? - Toward Improving the Accuracy of Generating Horizontal Building Mask Images for Deep Learning in Urban Planning and Design
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 377-386
doi	https://doi.org/10.52842/conf.caadria.2021.2.377
summary	Information extracted from aerial photographs is widely used in the fields of urban planning and architecture. An effective method for detecting buildings in aerial photographs is to use deep learning to understand the current state of a target region. However, the building mask images used to train the deep learning model must be manually generated in many cases. To overcome this challenge, a method has been proposed for automatically generating mask images by using textured 3D virtual models with aerial photographs. Some aerial photographs include thin clouds, which degrade image quality. In this research, the thin clouds in these aerial photographs are removed by using a generative adversarial network, which leads to improvements in training accuracy. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D virtual models with textured aerial photographs to enable the removable of thin clouds so that the image can be used for deep learning. A model trained on datasets generated by the proposed method was able to detect buildings in aerial photographs with an accuracy of IoU = 0.651.
keywords	Urban planning and design; Deep learning; Generative Adversarial Network (GAN); Semantic segmentation; Mask image
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	cdrf2021_55
id	cdrf2021_55
authors	Shengyu Meng
year	2021
title	Exploring in the Latent Space of Design: A Method of Plausible Building Facades Images Generation, Properties Control and Model Explanation Base on StyleGAN2
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_6
summary	GAN has been widely applied in the research of architectural image generation. However, the quality and controllability of generated images, and the interpretability of model are still potential to be improved. In this paper, by implementing StyleGAN2 model, plausible building façade images could be generated without conditional input. In addition, by applying GANSpace to analysis the latent space, high-level properties could be controlled for both generated images and novel images outside of training set. At last, the generating and controlling process could be visualized with image embedding and PCA projection method, which could achieve unsupervised classification of generated images, and help to understand the correlation between the images and their latent vectors.
series	cdrf
email
last changed	2022/09/29 07:53

_id	caadria2021_191
id	caadria2021_191
authors	Shou, Xinyue, Chen, Pinyang and Zheng, Hao
year	2021
title	Predicting the Heat Map of Street Vendors from Pedestrian Flow through Machine Learning
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 569-578
doi	https://doi.org/10.52842/conf.caadria.2021.2.569
summary	Street vending is a recent policy advocated by city governments to support small and intermediate businesses in the post-pandemic period in China. Street vendors select their locations primarily based on their intuitions about the surrounding environment; they temporarily occupy popular locations that benefit their business. Taking the city of Chengdu as an example, this study aims to formulate the rules governing vendors location selection using machine learning and big data analysis techniques, thus identifying streets likely to become vital street markets. We propose a semantic segmentation method to construct heat maps that visualize and quantify the distribution of street vendors and pedestrians on public urban streets. The image-based generative adversarial network (GAN) is then trained to predict the vendors heat maps from the pedestrians heat map, finding the relationship between the locations of the vendors and the pedestrians. Our successful prediction of the vendors locations highlights machine learning techniques ability to quantify experience-based decision strategies. Moreover, suggesting potential marketing locations to vendors could help increase cities vitality.
keywords	Machine Learning; Big Data Analysis; Semantic Segmentation; Generative Adversarial Networks
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	sigradi2021_114
id	sigradi2021_114
authors	Cesar Rodrigues, Ricardo, Kenzo Imagawa, Marcelo, Rubio Koga, Renan and Bertola Duarte, Rovenir
year	2021
title	Big Data vs Smart Data on the Generation of Floor Plans with Deep Learning
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 217–228
summary	Due to the progressive growth of data dimensionality, addressing how much data and time is required to train deep learning models has become an important research topic. Thus, in this paper, we present a benchmark for generating floor plans with Conditional Generative Adversarial Networks in which we compare 10 trained models on a dataset of 80.000 samples, the models use different data dimensions and hyper-parameters on the training phase, beyond this objective, we also tested the capability of Convolutional Neural Networks (CNN) to reduce the dataset noise. The models' assessment was made on more than 6 million with the Frétche Inception Distance (FID). The results show that such models can rapidly achieve similar or even better FID results if trained with 800 images of 512x512 pixels, in comparison to high dimensional datasets of 256x256 pixels, however, using CNNs to enhance data consistency reproduced optimal results using around 27.000 images.
keywords	Floor plans, Generative design, Generative adversarial networks, Smart Data, Dataset reduction.
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	sigradi2021_234
id	sigradi2021_234
authors	Al Nouri, Mhd Ziwar, Baghdadi, Bilal and Khateeb, Nairooz
year	2021
title	Re-coding Post-War Syria: The Role of Data Collection & Objective Investigations in PostWar Smart City
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 127–145
summary	Re-coding post-war Syria is an ongoing research and data platform, focused on innovation and collecting comprehensive, infrastructural and socioeconomic analytics, synchronization data, by using AI driven to give a more transparent image of innovating a new methodology to regenerate the future of post-war smart cities into advanced and sustainable urban environments in a smarter way (Fig. 1). The pressure to achieve a rapid Post-war smart city without clear strategy and comprehensive analysis of all aspects will cause a particularly catastrophic collapse in the interconnected social structure, services, education and health care system, leaving a long-term impact on the society. This paper presents the current status of the Research & Documentation methodology in the Data Collection phase by the objective investigations conducted through a series of local and international workshops species developed in this research called “Re-Coding“, offering consequent direct ground surveys, statistics and documentation study of the targeted areas, merging professionalism and youth power with local community to detect an open source data used as a tool to re-generate a precarious area towards a new methodology.
keywords	Post-War Smart cities, Collecting Data, Local community, Objective Investigations, Artificial intelligence
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	ecaade2021_225
id	ecaade2021_225
authors	Anishchenko, Maria and Paoletti, Ingrid
year	2021
title	Yarn-Level Modeling of Non-Uniform Knitted Fabric for Digital Analysis of Textile Characteristics - From a bitmap to the yarn-level model
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 253-262
doi	https://doi.org/10.52842/conf.ecaade.2021.1.253
summary	Modern CNC weft knitting machines are capable to produce textiles with complex non-uniform structures and shapes in a single operation with minimum human intervention. The type of knit structure and the settings of the knitting machine significantly influence the fabric characteristics and its role in architectural comfort. However, there is still no open-access tool for fast and efficient analysis of textiles with consideration of their knit structure, especially if they are knitted non-uniformly. Moreover, the existing methodologies of digital modeling of the knit structure are not linked to the actual production of textiles on flat-bed knitting machines. This paper presents a tool that "reads" a bitmap image that can be as well imported into a knitting machine software and generates a yarn-level geometry of the knitted textiles, that can be further integrated into the behavior analysis software within the rhino-grasshopper environment. This methodology helps to preview and analyze knitted textiles before production and can help to optimize the programming of bespoke knitted textiles for large-scale architectural applications.
keywords	knitting; computational knitting; digital simulation; textile characteristics; textiles for architecture
series	eCAADe
email
last changed	2022/06/07 07:54

_id	caadria2021_039
id	caadria2021_039
authors	Chen, Jielin, Stouffs, Rudi and Biljecki, Filip
year	2021
title	Hierarchical (multi-label) architectural image recognition and classification
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 161-170
doi	https://doi.org/10.52842/conf.caadria.2021.1.161
summary	The task of architectural image recognition for both architectural functionality and style remains an open challenge. In addition, the paucity of well-organized, large-scale architectural image datasets with specific consideration for the domain of architectural design research has hindered the exploration of these challenging tasks. Drawing upon images from the professional architectural website ArchdailyÂ®, and leveraging state-of-the-art deep-learning-based classification models, we explore a hierarchical multi-label classification model as a potential baseline for the task of architectural image classification. The resulting model showcases the potential for innovative architectural discipline-related analyses and demonstrates some heuristic insights for visual feature extraction pertaining to both architectural functionality and architectural style.
keywords	image recognition; hierarchical classification; multi-label classification; architectural functionality; style
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	acadia23_v3_207
id	acadia23_v3_207
authors	Doyle, Shelby; Bogosian, Biayna; Goldman, Melissa
year	2023
title	ACADIA Cultural. History Fellowship
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	The Association for Computer Aided Design in Architecture (ACADIA) launched the Cultural History Project in 2021 to mark the 40th anniversary of the organization and the 41st anniversary of the conference. This initiative has provided an opportunity to reflect upon the legacy and trends of the organization as a method for considering its future. The Cultural History Project began with an open-access digital archive of the organization’s Proceedings and Quarterlies and evolved into a larger discourse about how the ACADIA community values and promotes forms of computational knowledge. A summary essay included in the 2021 Proceedings (Image 2) reflects on what the archive reveals about ACADIA and its “habits”. Habits are settled tendencies or practices, especially ones that are difficult to relinquish. The term implies repetition, perhaps unconscious, that becomes normalized through its reiteration. The 2023 ACADIA Conference, “Habits of the Anthropocene,” marks the 43rd anniversary of the conference and the 42nd anniversary of ACADIA as an organization. What are the computational habits we need to identify, recall, question, break, and replace with new (or perhaps old) ways of thinking and working?
series	ACADIA
email
last changed	2024/04/17 14:00

_id	caadria2023_446
id	caadria2023_446
authors	Guida, George
year	2023
title	Multimodal Architecture: Applications of Language in a Machine Learning Aided Design Process
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 561–570
doi	https://doi.org/10.52842/conf.caadria.2023.2.561
summary	Recent advances in Natural Language Processing (NLP) and Diffusion Models (DMs) are leading to a significant change in the way architecture is conceived. With capabilities that surpass those of current generative models, it is now possible to produce an unlimited number of high-quality images (Dhariwal and Nichol 2021). This opens up new opportunities for using synthetic images and marks a new phase in the creation of multimodal 3D forms, central to architectural concept design stages. Presented here are three methodologies of generation of meaningful 2D and 3D designs, merging text-to-image diffusion models Stable Diffusion, and DALL-E 2 with computational methods. These allow designers to intuitively navigate through a multimodal feedback loop of information originating from language and aided by artificial intelligence tools. This paper contributes to our understanding of machine-augmented design processes and the importance of intuitive user interfaces (UI) in enabling new dialogues between humans and machines. Through the creation of a prototype of an accessible UI, this exchange of information can empower designers, build trust in these tools, and increase control over the design process.
keywords	Machine Learning, Diffusion Models, Concept Design, Semantics, User Interface, Design Agency
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	caadria2021_130
id	caadria2021_130
authors	Han, Yoojin and Lee, Hyunsoo
year	2021
title	Exploring the Key Attributes of Lifestyle Hotels: A Content Analysis of User-Created Content on Instagram
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 71-80
doi	https://doi.org/10.52842/conf.caadria.2021.1.071
summary	This study aims to investigate the key attributes of lifestyle hotels by analyzing user-created content on Instagram, an image-based social network service. In an era of uncertainty in the tourism and hospitality industry, it is inevitable that hotels must create a competitive identity. However, even with the significant growth of the lifestyle hotel segment, the concept of a lifestyle hotel is still vague. Therefore, to explore how to define, perceive, and interpret lifestyle hotels and to suggest their crucial attributes, this paper examines user-created content on Instagram. The data from 20,886 Instagram posts related to lifestyle hotels, including 2,209 locations, 43,586 hashtags, and 20,866 images, were analyzed using Vision AI, a social network analysis method and computer vision technology. The results of this study demonstrated that lifestyle hotels are perceived as design-focused branded hotels that represent the urban lifestyle and share both vacation and urban activities. Furthermore, the results reflected one of the latest hospitality trends-a holiday in an urban setting in addition to the primary purpose of traveling. Finally, this research suggests broader uses of big data and deep learning for analyzing how a place is consumed in a geospatial context.
keywords	Lifestyle Hotel; Hospitality Experiences; User-Created Content; Social Network Analysis; Vision AI
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	caadria2021_382
id	caadria2021_382
authors	Heidari, Farahbod, Saleh Tabari, Mohammad Hassan, Mahdavinejad, Mohammadjavad, Werner, Liss C. and Roohabadi, Maryam
year	2021
title	Bio-Energy Management from Micro-Algae Bio-Computational Based Reactor
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 401-410
doi	https://doi.org/10.52842/conf.caadria.2021.1.401
summary	Microalgae are a sustainable source of unique properties with potential for various applications. Biofuel production has led to the use of them as bioreactors on an architectural scale. Most of these efforts cannot manage the output due to the lack of intelligent control and monitoring over environmental micro-scale growth. This research presents the possibility of control and monitoring over the bio-energy retrieved through micro-organisms in bio-reactors, specifically the growth environments computation. To achieve monitoring, three dimensions of the medium culture captured by cameras, and with the advantage of image processing, the picture frames pixel values measured. In this process, we use the Python OpenCV Library as an image processing reference. Finally, a specifically developed algorithm analyses the calculated 3d-matrix. By changing the environmental parameters, control happens by directly recognizing changes in density and outputs. This researchs computational process has proposed a novel approach for controlling particle-based environments to reach the desired functions of microorganisms, This approach can use in a wide range of cases as a method.
keywords	Bio-Computation; Monitoring; Image Processing; Pattern Recognition; Multi-Functional Bio-Materials
series	CAADRIA
email
last changed	2022/06/07 07:49

_id	caadria2021_166
id	caadria2021_166
authors	Hu, Wei
year	2021
title	The experiment of neural network on the cognition of style
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 61-70
doi	https://doi.org/10.52842/conf.caadria.2021.2.061
summary	This paper introduces a method to obtain quantified style description vector which is for computer analysis input by using image style classification task. In the experiment, 3331 architectural photos of three styles obtained by crawling and filtering were used as training data. A deep convolutional neural network was trained to map architectural images to high-dimensional feature space, and then the high-dimensional style description vector was used to output the measurement results of style cognition with fully connected neural network. Tested by test data-set of 371 architectural pictures, the accuracy rate of style cognition reached more than 80%. The neural network using architectural data training was applied to the style cognition of non-architectural objects, high accuracy rate was also achieved, it proved that this quantified style description vector did include the information about style cognition to some extent instead of simply classification. Finally, the similarities and differences between the cognitive characteristics of style of neural network and human beings are investigated.
keywords	deep neural network; style cognition experiment; eye tracker
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia21_152
id	acadia21_152
authors	Kwon, Hyojin; Sherman, Adam
year	2021
title	Crooked Captures
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 152-157.
doi	https://doi.org/10.52842/conf.acadia.2021.152
summary	With flashy renderings dominating news feeds and high-flying drones filming from otherwise inaccessible vantage points, our encounters with the built environment increasingly involve perspectival views, but not necessarily those experienced firsthand. As tools for image production and consumption evolve, so too will methods for studying historical precedents. Crooked Captures treats this proliferation of digital images as fertile ground for photogrammetric explorations into how two-dimensional imaging techniques can influence three-dimensional form. While photogrammetry, the process of determining spatial measurements of physical objects from photographic inputs, has been an area of investigation for almost two centuries, the technique’s potential has blossomed with increased access to high quality cameras. Typical photogrammetric applications couple high-fidelity scanning and computing to produce faithful digital copies of physical artifacts and scenes for measuring and surveying. Leading photogrammetry software packages promise accuracy and precision, touting the exact replication of physical forms in digital space—so-called reality capture—as an indisputable virtue.
series	ACADIA
type	project
email
last changed	2023/10/22 12:06

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