Research Topic

Urban Climate Informatics

About this Research Topic

Climate change and continued urbanization threaten the livelihoods of urban dwellers worldwide through associated social, economic, and health-related risks. Transitioning into the new decade, it has become more important than ever to understand urban-induced and urban-experienced changes in atmospheric processes that affect all aspects of city life in order to increase urban resilience.

Recent advancements in sensing technologies, coupled with rapid growth in computing power, have produced novel data products that can augment traditional urban climate data and provide unprecedented insight into urban atmospheric dynamics. In this context, Urban Climate Informatics (UCI) is a newly evolving research field that emerges from the synthesis of two established domains: Urban Climate (concerned with interactions between a city and the overlying atmosphere), and Climate Informatics (a combination of climate science with approaches from statistics, machine learning, and data mining). UCI seeks to explore and understand complex urban climate systems using novel sensing approaches, crowdsourcing, big data sources, machine learning, and artificial intelligence.

This Research Topic welcomes contributions on recent advances in UCI from a wide range of applications and disciplines, including but not limited to:

- Machine learning and artificial intelligence in urban remote sensing
- Image processing and feature detection for environmental modeling and health
- LIDAR, Big Data, and Procedural Modeling to quantify urban morphology
- IoT and novel sensing techniques in urban climate
- Wearable methodologies for personalized assessments of urban climate impacts
- Crowdsourcing urban climate data
- Urban climate modeling using machine learning and/or artificial intelligence

Topic Editor Dr. Matthias Demuzere (Ruhr University, Bochum, Germany) is CEO of Kode VOF. All other Topic Editors declare no competing interests with regard to the Research Topic subject.


Keywords: urban climate, big data, IoT, artificial intelligence, crowdsourcing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Climate change and continued urbanization threaten the livelihoods of urban dwellers worldwide through associated social, economic, and health-related risks. Transitioning into the new decade, it has become more important than ever to understand urban-induced and urban-experienced changes in atmospheric processes that affect all aspects of city life in order to increase urban resilience.

Recent advancements in sensing technologies, coupled with rapid growth in computing power, have produced novel data products that can augment traditional urban climate data and provide unprecedented insight into urban atmospheric dynamics. In this context, Urban Climate Informatics (UCI) is a newly evolving research field that emerges from the synthesis of two established domains: Urban Climate (concerned with interactions between a city and the overlying atmosphere), and Climate Informatics (a combination of climate science with approaches from statistics, machine learning, and data mining). UCI seeks to explore and understand complex urban climate systems using novel sensing approaches, crowdsourcing, big data sources, machine learning, and artificial intelligence.

This Research Topic welcomes contributions on recent advances in UCI from a wide range of applications and disciplines, including but not limited to:

- Machine learning and artificial intelligence in urban remote sensing
- Image processing and feature detection for environmental modeling and health
- LIDAR, Big Data, and Procedural Modeling to quantify urban morphology
- IoT and novel sensing techniques in urban climate
- Wearable methodologies for personalized assessments of urban climate impacts
- Crowdsourcing urban climate data
- Urban climate modeling using machine learning and/or artificial intelligence

Topic Editor Dr. Matthias Demuzere (Ruhr University, Bochum, Germany) is CEO of Kode VOF. All other Topic Editors declare no competing interests with regard to the Research Topic subject.


Keywords: urban climate, big data, IoT, artificial intelligence, crowdsourcing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

10 July 2020 Abstract
30 November 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

10 July 2020 Abstract
30 November 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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