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GeoAI for Urban Understanding: Fusing Multi-Source Geospatial Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 138

Special Issue Editors


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Guest Editor
School of Artificial Intelligence, China University of Geosciences, Beijing, China
Interests: geospatial big data modeling; spatiotemporal dynamics; GeoAI and built environment

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Guest Editor
School of Geographical & Earth Sciences, University of Glasgow, Glasgow G12 8QQ, UK
Interests: advancing GIScience and big data analytics methods, including GeoAI, explainable artificial intelligence, and econometrics, to better understand urban systems
Special Issues, Collections and Topics in MDPI journals
School of Earth Sciences, Zhejiang University, Hangzhou, China
Interests: GeoAI; GNNWR; RS and spatiotemporal modeling and geo-big data analysis
Special Issues, Collections and Topics in MDPI journals
School of Earth Sciences, Zhejiang University, Hangzhou, China
Interests: GeoAI; geo-big data analysis; complex system modeling

Special Issue Information

Dear Colleagues,

The urban environment, as a complex system, is at the forefront of global sustainability challenges. Achieving a deep, dynamic and comprehensive understanding of urban forms, functions and flows is critical for informed decision-making and sustainable development. The concurrent explosion of geospatial big data—from high-resolution remote sensing and IoT sensors to ubiquitous social media and mobile phone data—presents an unprecedented opportunity to decode urban complexity. Fueled by advanced Artificial Intelligence, particularly deep learning and foundational models, Geographic Artificial Intelligence has emerged as a transformative force, enabling us to extract meaningful patterns from these vast digital footprints.

However, the primary scientific challenge has shifted from analyzing single data sources to the **effective fusion of multi-source, heterogeneous geospatial data**. These data vary greatly in spatiotemporal scale, resolution and semantics. The true potential for a holistic "urban understanding" lies in developing novel GeoAI theories and computational paradigms that can integrate these disparate data sources, creating synergistic value that is greater than the sum of its parts. This Special Issue aims to address this pivotal challenge, showcasing cutting-edge research that leverages integrated data and advanced AI to advance urban science.

This Special Issue aims to collate high-quality original research and review articles that demonstrate significant methodological breakthroughs and novel applications in fusing multi-source geospatial data for enhanced urban understanding. We seek contributions that move beyond mere pattern detection towards causal explanation, predictive modeling and actionable insights for smart and sustainable urban development.

This topic aligns perfectly with the journal's scope of publishing impactful research at the intersection of remote sensing, urban informatics and artificial intelligence. It emphasizes the development of novel computational methods and their application to pressing real-world problems.

Welcome submissions on topics including, but not limited to:

  • Novel theories and computational paradigms for multi-source geospatial data fusion.
  • Spatiotemporal foundation models for urban application.
  • Fusion of remote sensing imagery (optical, SAR, etc.) and/or social sensing data (e.g., mobile phone, social media).
  • Explainable AI and knowledge-guided deep learning for urban process modeling.
  • Fine-scale, near-real-time monitoring of urban dynamics (e.g., population, traffic, energy, water, urban infrastructure, land ).
  • GeoAI for urban sustainability assessment (e.g., SDGs, carbon emissions, green equity, urban planning).

We invite original research articles, comprehensive review articles and short communications that present significant conceptual or technical advancements.

Prof. Dr. Changfeng Jing
Dr. Mingshu Wang
Dr. Sensen Wu
Dr. Chao Wang
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

  • GeoAI
  • urban understanding
  • data fusion
  • multi-source data
  • geospatial big data
  • remote sensing
  • social sensing
  • sustainable cities

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Published Papers

This special issue is now open for submission.
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