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Smart city article details

Title	Urban Heat Island Detection Utilizing Citizen Science
ID_Doc	60012
Authors	Kisters P.; Ngu V.; Edinger J.
Year	2022
Published	Communications in Computer and Information Science, 1617 CCIS
DOI	http://dx.doi.org/10.1007/978-3-031-23298-5_9
Abstract	With rising temperatures and increasing densification in modern cities the risk for so called urban heat islands increases. Urban heat islands pose health risks for elderly people, children, and persons with existing health conditions. Currently, cities rely on satellite images to detect areas of risk since existing sensor networks are not dense enough. However, these measures have either a low spatial or temporal resolution and are not suitable to continuously collect fine-granular data samples. We propose an alternative approach based on a distributed citizen-owned sensor network consisting of existing Smart Home and Internet of Things devices owned by citizens. We further deploy mobile sensors that can be attached to publicly available infrastructure such as buses, rental bikes, or e-scooters. These mobile measurement help to gain temporary insights into areas which have previously not been covered. However, the collected measurements in these areas remains sparse. Therefore, we continuously interpolate the current temperature by utilizing machine learning algorithms. With the existing measurements from mobile sensor stations we train a regression model to learn their dependencies with neighboring stationary weather stations. The predicted weather conditions of unmonitored results in a fine-granular heatmap that allows for an accurate localization of urban heat islands. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Citizen science; Distributed services; Smart cities

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