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CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
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_id	ascaad2022_033
id	ascaad2022_033
authors	Rohani, Nima; Kim, Ikhwan
year	2022
title	Urban Design Analysis of New York City's Virtual Model: The Case of Tom Clancy's The Division
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. 188-201
summary	People have started spending time with digital tools and virtual worlds to escape reality's horrors. However, designed spaces are more than the players' needs, especially those digital games that their stories involve urban environments. This inefficiency causes spending futile efforts both in time and cost for the digital games' productions; The urban environments in these digital games are replicas of real-world cities. Some companies use some techniques for downgrading replicas. Therefore, this study aims to uncover the used techniques for designing Tom Clancy's The Division (2016). By using reverse engineering methodology and qualitative comparative analysis, the in-game map compared with the real-world map. Based on the results, the used techniques allowed the designers to scale down the game environment to be 2.5 times smaller than the actual city. Rather, verisimilitude is achieved by combining sufficiently accurate elements to give the impression of complete accuracy. By implementing the results of this research, designers can develop smaller replicas to be perceived as more extensive.
series	ASCAAD
email
last changed	2024/02/16 13:24

_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

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