Advanced AI-Enhanced Remote Sensing for Urban Heat Monitoring and Analysis
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".
Deadline for manuscript submissions: 15 August 2026 | Viewed by 372
Special Issue Editors
2. QUT Urban AI Hub, School of Architecture and Built Environment, Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: urban heat; urban AI; heat vulnerability; remote sensing; GIS
Interests: urban AI; quantum AI; urban planning; urban sustainability; urban innovation; urban technology
Special Issues, Collections and Topics in MDPI journals
Interests: urban study; nighttime light remote sening; GIS; urban population; spatial analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Urban heat has become one of the most critical environmental challenges associated with rapid urbanization and climate change, posing increasing risks to urban sustainability, public health, and social equity. Remote sensing provides an indispensable means for monitoring urban thermal environments across multiple spatial and temporal scales, while recent advances in artificial intelligence (AI) have significantly enhanced the capacity to extract, integrate, and analyze complex thermal information from multi-source observations. The convergence of advanced AI techniques and remote sensing therefore offers new opportunities for more accurate, interpretable, and application-oriented urban heat monitoring and analysis.
This Special Issue, entitled “Advanced AI-Enhanced Remote Sensing for Urban Heat Monitoring and Analysis,” aims to bring together cutting-edge research that leverages advanced AI methodologies to improve the observation, modeling, and understanding of urban heat phenomena. The focus extends beyond conventional land surface temperature retrieval to include multi-source data fusion, spatiotemporal modeling, urban morphology–heat interactions, heat exposure assessment, and mitigation-oriented applications. Contributions that integrate physical knowledge, address model uncertainty, or enhance model interpretability are particularly encouraged.
The Special Issue welcomes original research articles, review papers, and perspective studies addressing, but not limited to, the following topics:
- AI-enhanced retrieval and downscaling of urban thermal indicators
- Multi-source and multi-scale remote sensing data fusion for urban heat analysis
- Advanced AI models (e.g., physics-informed learning, graph-based models, transformers) for urban thermal environments
- Spatiotemporal dynamics and long-term monitoring of urban heat patterns;
- Interactions between urban form, land cover, and urban heat characteristics;
- Uncertainty quantification, explainability, and robustness of AI-based urban heat models;
- Heat exposure, risk assessment, and links to human health and social vulnerability;
- Integration of remote sensing, in situ observations, and urban climate models;
- AI-supported evaluation of urban heat mitigation and adaptation strategies;
- Benchmark datasets, comparative studies, and reproducible frameworks for urban heat research;
By fostering interdisciplinary contributions, this Special Issue aims to advance both methodological innovation and practical understanding of urban heat processes, supporting more resilient and sustainable urban environments.
Dr. Fei Li
Prof. Dr. Tan Yigitcanlar
Prof. Dr. Qingwu Yan
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI-enhanced remote sensing
- Urban heat monitoring
- Urban heat analysis
- Land surface temperature (LST)
- Multi-source thermal data fusion
- Spatiotemporal modeling of urban heat
- Urban morphology––heat interaction
- Heat exposure and risk assessment
- Urban heat mitigation strategies
- AI-based urban heat models
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