CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
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_id sigradi2022_253
id sigradi2022_253
authors Sanatani, Rohit Priyadarshi; Nagakura, Takehiko; Tsai, Daniel
year 2022
title The Tourist’s Image of the City: A comparative analysis of visual features and textual themes of interest across three metropolises
source Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 89–100
summary Tourist attractions play a major role in shaping ‘mental images’ of cities. The growing availability of urban big-data in recent years has opened up novel lines of inquiry into the nuances of urban imageability and sentiment. Drawing upon crowdsourced hybrid data in the form of both textual descriptions as well as photographs for 750 tourist attractions across Boston, Singapore and Sydney, this work compares the predominant themes of discussion and visual features of interest that shape tourist sentiment towards these cities. The study collects over 3500 user reviews and uses Latent Dirichlet Allocation (LDA) for the extraction of high-level topics of discussion. Object detection is also run on over 6000 photographs, and unsupervised clustering is carried out on extracted features to identify clusters of visual elements which capture tourist attention. The findings reinforce the popular identity of Boston as a city steeped in history, while strong perceptions of nature and greenery emerge from Singapore. Tourist interest in Sydney is dominated by specific anchors such as the Sydney Harbor Bridge.
keywords Data Analytics, Urban Tourism, Topic Modeling, Sentiment Analysis, Unsupervised Clustering, Big Data
series SIGraDi
email
last changed 2023/05/16 16:55

_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
doi https://doi.org/10.52842/conf.ecaade.2022.1.511
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
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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