Call for Papers: Deep Learning-based Geo-visual Analytics for Human Mobility Pattern Analysis

Geo-visual analytics is a scientific discipline concerned with applying geographic information to problem-solving, facilitated by interactive visual interfaces and typically anchored in real-world problem-solving situations. New computational approaches to identify or predict human mobility patterns, novel interfaces for geographically visual data, or new approaches toward the perceptual and cognitive processes that users employ to solve complex analytical problems are all possible topics of investigation in visual analytics. Geo-visual analytics systems often include a high range of interactive, user-driven mechanisms and a variety of visual representation formats for spatial data. According to the forecast, new spatial data sources and display formats are projected to stimulate many applications and research applications for future perspectives.

Remote sensing technologies like satellites, drones, aircraft, and autonomous vehicles collect visual data. The data produced by these devices is primarily visual in nature, including RGB imaging, thermal imagery, and cloud processing with 3D points, among other things. It contains real-time metadata that identifies the precise geographic place where the data was recorded. It is geotagged as well as additional information. Geo-visual Intelligence is a business intelligence strategy derived from this type of information. Using this type of intelligence, you can discover the dynamic reality of vital assets and infrastructure. When combined with AI-enabled ML/DL techniques and automated analytics, visual data can potentially change formerly manual and inefficient operations into ones that are automated and operate on a large scale and with high frequency. The rapid breakthroughs in artificial intelligence have produced new computer vision and visual data processing capabilities. Transforming visual data into visual intelligence is the core aspect. Particularly well-suited for evaluating enormous volumes of visual data, deep learning algorithms have swiftly advanced to the point where they can be used in commercial applications.

Identifying human mobility patterns is critical because it impacts various areas of our society, including disease transmission, planning urban structures, healthy living, environmental pollution, and other factors. Combined with the predictive capacity of artificial intelligence, the growth of digital mobility data like phone databases, traces of GPS signals, and posts on social media prompted the application of AI-enabled deep learning to identify human mobility patterns. In order to address the open challenges of human mobility pattern discovery, deep learning methodologies such as deep neural networks could address prediction of next-location, prediction of crowd flow, generation of trajectory, and generation of flow, users' destination location prediction, and capturing individual activity-based behavior. Hence, this special issue focuses on Deep Learning based Geo-visual Analytics for Human Mobility Pattern Analysis. The Special Issue is open for contributions related but not limited to the following topics:

  • Futuristic AI-based multi-scale visualization architectures for Human Mobility Pattern Analysis
  • Recent approaches in transforming visual data into visual intelligence with big data analytics
  • Advanced mobility modeling techniques and dynamic analysis for discovering human mobility patterns
  • Deep learning techniques for Network visualization of spatial-temporal objects in examining human interactions in physical spaces
  • Novel visual data analytic methods for recognizing socio–graphical human mobility patterns
  • Modern techniques to explore perceptual and cognitive psychology of human mobility pattern analysis
  • Trends, opportunities, and challenges of visualization standards for human mobility pattern discovery
  • Advanced business intelligence for Geo-visual Intelligence on human mobility patterns
  • Enhanced cutting-edge pattern recognition techniques of geo-visualization in deploying activity-based human mobility patterns
  • Innovative data-driven and prediction approaches for precise human mobility pattern deployment

Content published in this journal is peer reviewed (Double Blind).

Manuscripts can be submitted under the following link:

The topical collection (special issue) will be closed on 10 November 2022.

You can find the Instructions for Authors here. Please direct any questions regarding the Special Issue to one of the guest editors:

Dr. Malik Bader Alazzam, Faculty of Computer Science and Informatics, Amman Arab University, Jordan


Dr. Abdulsattar Abdullah Hamad, Associate Professor, Department of Mathematics and Computer Sciences, College of Science, Tikrit University – Imam University College, Tikrit, Iraq


Dr. Azam Abdelhakeem Khalid Ahmed, Faculty of Management and Economics, University Pendidikan Sultan Idris (UPSI), Perak, Malaysia


Working on a manuscript?

Avoid the most common mistakes and prepare your manuscript for journal editors.

Learn more