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Other literature type . 2025
License: CC BY
Data sources: ZENODO
https://doi.org/10.5194/icuc12...
Article . 2025 . Peer-reviewed
Data sources: Crossref
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Presentation . 2025
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2025
License: CC BY
Data sources: Datacite
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Street-level sensor network (of networks)

Authors: Zeeman, Matthias; Barlow, Janet; Feigel, Gregor; Grimmond, Sue; Ludwig, Svenja; Matthews, James; Plein, Marvin; +2 Authors

Street-level sensor network (of networks)

Abstract

The integration of street-level sensor networks is crucial for improving urban resilience and sustainability, especially in response to the challenges of climate change and urbanisation. This paper presents the results of the urbisphere/ASSURE projects, which includes co-developing a stakeholder-centred approach to environmental monitoring. The projects have established networks of temperature and relative humidity (T/RH) sensors, with initial deployments in the Freiburg and Bristol areas. These sensor networks aim to capture real-time environmental data and provide actionable insights for meteorological agencies, urban planners, policy makers, local communities and researchers.Our findings demonstrate the effectiveness of this network in providing real-time environmental data through a Creative Commons publication model, ensuring the data are freely available and reusable. The ERC and UKRI required open data approach empowers stakeholders to have informed discussions based on transparent, up-to-date information. We also discuss the technical components that underpin the network, including real-time data delivery protocols and APIs that enable seamless integration with existing systems. These technologies support the continuous flow of data to stakeholders and help the efficient operation of multiple networks of deployed components.We showcase the development of network management and diagnostic tools that incorporate quality control (QC) procedures to ensure data accuracy and reliability. These tools support both the routine maintenance of the network and the production of ready-to-use data products, using machine learning techniques. Using case studies from Freiburg and Bristol, we demonstrate the potential of these street-level sensor networks to provide valuable insights into urban microclimates and contribute to building more sustainable cities.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green