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Geospatial AI: Systems, Model, Methods, and Applications
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Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA
Department of Biomedical and Electrical Engineering, Marshall University, Huntington, WV 25755, USA
Department of Epidemiology and Biostatistics, College of Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA
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Geospatial AI: Systems, Model, Methods, and Applications

Abstract submission deadline
31 October 2026
Manuscript submission deadline
31 December 2026
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Topic Information

Dear Colleagues,

Geospatial artificial intelligence (GeoAI) is an emerging interdisciplinary field that integrates geospatial data analysis with artificial intelligence (AI) to derive meaningful insights from diverse sources such as satellite imagery, geographic information systems (GIS), and location-based data. By leveraging advanced AI algorithms, GeoAI can uncover patterns, predict trends, and support data-driven decision-making across a wide range of domains. This integration enhances our understanding of spatial relationships and dynamics, enabling more effective responses to complex global challenges.

In today’s world, pressing issues such as climate change, environmental degradation, resource depletion, and rapid urbanization demand innovative solutions. These challenges are deeply interconnected and require advanced, data-intensive approaches. AI’s capacity to process and analyze massive volumes of geospatial data both efficiently and accurately makes it a powerful enabler for addressing these concerns. Through GeoAI, researchers and practitioners can inform policy, improve strategies, and contribute to a more sustainable and resilient future.

This Topics seeks to bring together original research and comprehensive reviews on the latest advances, applications, and challenges in GeoAI. We invite submissions of both review articles and original research papers that explore innovative methodologies, cutting-edge technologies, and novel applications in themes including (but not limited to) the following:

  • GeoAI for Smart Cities and Urban Development: Research on how GeoAI can optimize urban planning and enhance smart city initiatives.
  • GeoAI applications for Environmental Monitoring: Research on using GeoAI to monitor and mitigate environmental changes, such as climate change, land cover change, water, and wetland management.
  • GeoAI for Disaster Management: Research on the role of GeoAI in disaster risk reduction, early warning systems, and post-disaster recovery efforts.
  • Intelligent Systems for Transportation and Mobility: Research on using GeoAI to monitor, analyse, and optimize transportation networks, improve traffic management, and enhance mobility solutions.
  • GeoAI for Public Health: Research on the use of GeoAI in disease surveillance, health resource allocation, and understanding the spatial dynamics of public health issues.
  • Technological Advances in GeoAI: Research on new algorithms, data fusion techniques, and computational methods that enhance the capabilities of GeoAI.

We look forward to receiving your contributions and creating a comprehensive collection of articles that show the potential of GeoAI to drive sustainable development and improve our understanding of the world around us.

Dr. Lirong Yin
Dr. Shan Liu
Dr. Kenan Li
Topic Editors

Keywords

  • GeoAI (geospatial AI)
  • spatiotemporal AI
  • large language models
  • remote sensing
  • semantic segmentation
  • object detection
  • human mobility
  • land use monitoring
  • disaster forecasting
  • smart cities
  • environmental sustainability
  • public health
  • explainable AI
  • generative AI
  • spatial bias

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.5 5.5 2011 19.8 Days CHF 2400 Submit
Atmosphere
atmosphere 		2.3 4.9 2010 16.9 Days CHF 2400 Submit
ISPRS International Journal of Geo-Information
ijgi 		2.8 7.2 2012 34.2 Days CHF 1900 Submit
Land
land 		3.2 5.9 2012 16 Days CHF 2600 Submit
Systems
systems 		3.1 4.1 2013 18.8 Days CHF 2400 Submit
Urban Science
urbansci 		2.9 3.7 2017 25.5 Days CHF 1600 Submit
Water
water 		3.0 6.0 2009 19.1 Days CHF 2600 Submit

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  3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
  4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
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Published Papers (1 paper)

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Article
Revealing the Impact of the Built Environment on the Temporal Heterogeneity of Urban Vitality Using Ensemble Machine Learning
by Xuyang Chen, Junyan Yang, Jingjing Mai, Ao Cui and Xinyue Gu
Land 2025, 14(11), 2182; https://doi.org/10.3390/land14112182 - 3 Nov 2025
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Abstract
The multidimensional urban built environment (BE) in high-density cities has been shown to be closely related to the urban vitality (UV) of residents’ travelling. However, existing research lacks consideration of the differences in this relationship over a week, so this paper proposes an [...] Read more.
The multidimensional urban built environment (BE) in high-density cities has been shown to be closely related to the urban vitality (UV) of residents’ travelling. However, existing research lacks consideration of the differences in this relationship over a week, so this paper proposes an ensemble machine learning approach that simultaneously considers different time periods of the week. This study reveals the impacts of four dimensions of BE variables on UV at different time periods at the scale of the community life circle. The four well-performing base models are integrated to reveal the mechanism of differential effects of BE variables on UV under different time periods in the old city of Nanjing through Shapley addition explanation. The findings reveal that (1) the seven most important built environment variables existed in different time periods of the week: floor area ratio, service POI density, remote sensing ecological index, POI mixability, average building height, fractional vegetation cover, and maximum building area; (2) The nonlinear and threshold effects of the built environment factors differed across time periods of the week; (3) There is a dominant interaction between built environment variables at different time periods of the week. This study can provide guidance for the refined management of complex urban systems. Full article
(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)
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