Barrios 4D: Semi-automated morphology analysis of human settlements using mobile laser scanning

Castaño-López, Jason Alejandro ORCID: https://orcid.org/0000-0001-5874-3614, Perafán-Villota, Juan C ORCID: https://orcid.org/0000-0002-7275-9839, Llanos-Neuta, Nicolas, Mora, Simone ORCID: https://orcid.org/0000-0002-7991-1346 and Romero Cano, Victor ORCID: https://orcid.org/0000-0003-2910-5116 2025. Barrios 4D: Semi-automated morphology analysis of human settlements using mobile laser scanning. Environment and Planning B: Urban Analytics and City Science 10.1177/23998083251315966

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Abstract

Civil or government organizations base human settlement transformation decisions on limited and sparse data. However, broader and denser information is necessary. Camera and LiDAR data processing is a more effective, automatic, and affordable method to fully characterize the morphological structure of human settlements. This work presents a system for estimating metrics about relevant morphological characteristics of human settlements using LiDAR data. We provide a quantitative analysis of these metrics obtained in the city of Cali, Colombia. Additionally, we enable the automatic calculation of urban metrics such as the street canyon ratio, which relates building height to street width, a metric highly correlated with air quality. Moreover, we extrapolate findings from existing literature to compare our results and understand how indirectly measured variables, such as thermal sensation and perceived beauty of the environment, might behave Our system can potentially be used by civil and government organizations to develop informed and precise urban planning and transformation strategies, including land use zoning, infrastructure development, and addressing issues related to housing, transportation, and environmental sustainability.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Sage
ISSN:	2399-8083
Date of First Compliant Deposit:	29 January 2025
Last Modified:	26 Feb 2025 14:53
URI:	https://orca.cardiff.ac.uk/id/eprint/175704

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