Submission Deadline: 30 June 2026 View: 313 Submit to Special Issue
Dr. Lirong Yin
Email: lirongy@arizona.edu; yin.lyra@gmail.com
Affiliation: Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, 85721, USA
Research Interests: AI/ML, Artificial Neural Network pattern recognition, GeoSpatial Intelligence (GeoAI), remote sensing and earth observation, GIS/RS for earth surface process (land, water, and climatic cycling), GeoSpatial analysis, social dynamics, natural disaster and human disaster, coupled human and nature dynamic system, multimodal AI, deep learning, computational vision, GIScience, Geostatistics, Geoinformatics, GeoMatics, Geo-data Mining, natural hazards, human-complex dynamics
Prof. Dr. Xuan Liu
Email: liuxuan@uestc.edu.cn
Affiliation: School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, 610054, China
Research Interests: Geoinformatics, urban planning, urban renewal, real estate, GIS/RS, AI/ML, NLP, rural development, urbanization, space value modelling, post-productivism transformation, social sensing, GeoAI, land management, policy transfer
Dr. Kenan Li
Email: kenan.li@slu.edu
Affiliation: Department of Epidemiology and Biostatistics, College of Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA
Research Interests: Geoinformatics, spatial computation and modeling of community resilience/sustainability, data science and statistics in land use, geo-simulation of human and environmental systems, Geo-AI (Geospatial Artificial Intelligence) frameworks, integrated geo-cyber-infrastructures, urban planning, GIS/RS, AI/ML, land development, urbanization, space value modelling, social sensing, land management
The rapid advancement of sensor technology—ranging from satellite-based remote sensing to diverse ground-based sensors—has enabled real-time monitoring of vast areas of the Earth's surface, generating unprecedented volumes of geospatial data. In parallel, the proliferation of social media, mobile devices, and advanced cyberinfrastructure has greatly expanded the accessibility and sharing of spatial information. Together, these developments have created fertile ground for the rise of Geospatial Artificial Intelligence (GeoAI), which integrates cutting-edge AI methodologies with geospatial information science to enhance the efficiency, accuracy, and scalability of geospatial analysis.
GeoAI leverages machine learning, deep learning, and multimodal AI to extract meaningful insights from heterogeneous data across multiple spatial and temporal scales. By automating traditionally complex analytical tasks—such as image interpretation, pattern recognition, and change detection—GeoAI lowers barriers to advanced geospatial applications, democratizing access to powerful analytical tools and accelerating innovation in both research and practice.
Building on this foundation, the recent emergence of foundation models has further expanded AI's cognitive capabilities and opened transformative opportunities for geospatial domains. These large-scale models, with their ability to generalize across tasks and modalities, are redefining approaches to Earth observation, land cover classification, automated mapping, disaster response, precision agriculture, smart cities, and transportation systems. Their capacity for knowledge transfer and contextual reasoning enables more accurate, timely, and scalable geospatial intelligence, strengthening decision-making in environmental monitoring, urban planning, and disaster management.
While these developments have already demonstrated remarkable potential, important research challenges remain. Advancing GeoAI requires exploring adaptation of foundation models to geospatial tasks, integrating multimodal data sources, improving explainability and trustworthiness, and ensuring scalability for real-time decision support. Addressing these challenges will shape the next generation of intelligent geospatial systems and their impact on science, society, and the environment.
This Special Issue invites research articles and comprehensive review papers focusing on the latest advancements in Spatial Intelligence, Geospatial Intelligence, and GeoAI. We welcome submissions, both research and review papers, that address topics such as:
· Geospatial Artificial Intelligence (GeoAI)
· Generative AI for Geographic Problems
· Foundation Models in Geospatial Context
· Spatiotemporal AI Models
· Multimodal AI for Geospatial Science
· AI-Based Spatial Data Analysis
· Machine Learning & Deep Learning in GIS
· Spatial Reasoning and Literacy in Large Language Models
· Conversational Agents for Spatial Tasks
· AI Assistants for Mapping and Cartography
· Human–AI Collaboration in Spatial Decision-Making
· Big Data, Crowdsourced Data, and AI
· Cloud-Native and Streaming Data Architectures
· Serverless, Edge AI, and Emerging Technologies in Geo-computation
· Digital Twin Modeling for Dynamic Geospatial Systems
· AI for Social Sensing and Human Mobility
· GeoAI for Environmental Sustainability and Climate Resilience
· AI for Smart Cities and Transportation Systems
· AI for Epidemiology and Public Health
We look forward to high-quality research papers and review papers that synthesize the latest insights and advancements in the field of GeoAI and its applications in remote sensing.
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