Call for papers
AI-Enhanced Geospatial Big Data Analysis
This special issue aims to showcase high-impact research that integrates state-of-the-art AI methodologies, including machine learning, deep learning, transfer learning, explainable AI, generative models, and multi-source data fusion. We invite submissions highlighting novel scientific breakthroughs, validated real-world applications, and scalable solutions with practical implications; papers should go beyond theoretical frameworks to demonstrate how AI, in combination with multi-source data and remote sensing technologies, enables more precise environmental monitoring, optimized urban planning, enhanced disaster risk prediction, and improved land-use management, thereby contributing to the advancement of intelligent geospatial analytics and fostering data-driven decision-making for sustainable development in the future.
Guest editors:
Dr. Nan Xu
Shenzhen University, Shenzhen, China
Dr. Wei Tu
Shenzhen University, Shenzhen, China
Prof. Dr. Le Yu
Tsinghua University, Beijing, China
Dr. Yinxia Cao
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Manuscript submission information:
Authors are invited to submit their manuscripts at any time before the submission deadline 30 September 2026. Please select “VSI: Geospatial Big Data” as your article type. For any inquiries about the appropriateness of contribution topics, please contact Dr. Nan Xu ([email protected])
Keywords:
Geospatial; AI; Big data; Geography; GIS
Why publish in this Special Issue?
- Special Issue articles are published together on ScienceDirect, making it incredibly easy for other researchers to discover your work.
- Special content articles are downloaded on ScienceDirect twice as often within the first 24 months than articles published in regular issues.
- Special content articles attract 20% more citations in the first 24 months than articles published in regular issues.
- All articles in this special issue will be reviewed by no fewer than two independent experts to ensure the quality, originality and novelty of the work published.
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