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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	sigradi2023_387
id	sigradi2023_387
authors	Dong, Jiahua, Lin, Shuiyang and van Ameijde, Jeroen
year	2023
title	Predicting Network Integration Based on Satellite Imagery Around High-Density Public Housing Estates Through Machine Learning
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 795–806
summary	In studies focusing on environmental and health aspects of urban planning, the integration of road networks within the built environment emerges as an important metric for assessing the livability and healthiness of neighborhoods. The complexity and diversity of the road networks are significant for shaping vibrant streets. In Hong Kong’s ongoing construction program of large-scale public housing estates, the design prioritizes the connectivity of pedestrian circulation to foster social interaction among residents and encourage the utilization of recreational facilities. In this study, an analytical framework is developed to interpret public housing estate spatial layout based on satellite imagery. It extracts road networks using neural networks and vectorizes results to analyze network integration around estates to predict social interactions. The aim of this process is to employ a machine learning workflow to analyze options for newly planned estates, where the design configuration can be further optimized based on its potential to stimulate social engagement and community interaction. Due to the scalability and universality of the method, the research can contribute to improved road networks and sociable housing complexes in Hong Kong, or in other international cities of similar density and vibrancy.
keywords	Network Integration, Spatial Structure, Satellite Imagery, Machine Learning, Hong Kong Public Housing
series	SIGraDi
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last changed	2024/03/08 14:07

_id	sigradi2023_156
id	sigradi2023_156
authors	Luo, Jianing, Yu, Boyuan, Peng, Haohan, Shi, Yi, Li, Yangzhi and Fingrut, Adam
year	2023
title	Deep generative modeling tasks: Automatic generation of building facades with Pix2Pix GAN for Hong Kong city expansion and renovation
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 137–148
summary	Hong Kong's rapid urbanization and expansion have resulted in a high incidence of new urban construction projects as well as building renovation and restoration activities. The above architectural activities in the city are characterized by inconsistency in design styles and inconsistency with the neighborhood environment. The proposal of a self-weakening design style of different designers in the same neighborhood should be taken as a practical consideration at the early stage of design. In this research, a dataset of old building facades in Hong Kong is provided, and the method of training a deep convolutional neural network is used to realize the coupling of Pix2Pix GAN algorithm to the whole or local design generation of building facade in Hong Kong. Moreover, a trained network based on the architectural styles of Hong Kong is provided with 160 sets of collected original image datasets of Hong Kong building facades for organizing pre-calibration. It classifies the elemental information of complex building facades for later training of the network and automatic generation to give new construction and renovation schemes a replicable technical route.
keywords	Pix2Pix GAN, Facade, Deep learning, Hong Kong, Generative design
series	SIGraDi
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last changed	2024/03/08 14:06

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