The 19th IEEE International Conference on Machine Learning and Applications
Special Session: The 2nd AI with Geographic Information Systems for Social Good
December 14-17, 2020, Miami, Florida, USA
Artificial intelligence (AI) has the potential to help tackle challenging urban and regional problems, ranging from diagnosing cancer to assisting blind people to navigate their surroundings, detecting drug abuse and trafficking, identifying victims of online sexual exploitation, facilitating disaster response, and promoting social justice and sustainable development. In addition to AI, Geographic Information Systems (GIS) technology can be a powerful tool capable of exploring the social issues in the spatial context, through the domains such as criminology, resource management, and transportation planning. For example, a GIS platform might allow emergency planners to swiftly map resources towards emergency response in the event of a natural disaster.
If AI and GIS can be appropriately managed and integrated, the rich and diverse geographic information via GIS will significantly boost the impact of AI on many aspects of our lives. An AI-GIS framework can be used to predict and locate vulnerable communities under natural disasters, identify virus and disease transmission patterns, or to optimize the food distribution in the areas facing shortages and famine.
IEEE ICMLA 2020 will feature a special track on AI with GIS for Social Good. The goal of this track is to provide a venue to disseminate the research focusing on social problems for which the synthesis of AI and GIS has the potential to offer the innovative solutions. This track will bring together researchers and practitioners across AI, GIS and a range of application domains on the frontiers of frameworks, theories, and methods.
Sample topics include, but not limited to:
- Spatial and Social Network Analysis
- Public Health
- Human and Population Dynamics
- Information Diffusion and Community Detection
- Crime Prediction
- Disaster Response and Recovery
- Environmental Sustainability
- Ethics and Security Issues
- Fairness and Biases
- Mobility and Traffic Prediction
- Urban Computing
- Physical sensing (e.g., remote sensing, unmanned aerial vehicle, cameras, smart cards) and Social Sensing
Submission Guidelines and Instructions
Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from IEEE website. The maximum length of papers is 8 pages. All the papers will go through double-blind peer review process. Authors’ names and affiliations should not appear in the submitted paper. Authors’ prior work should be cited in the third person. Authors should also avoid revealing their identities and/or institutions in the text, figures, links, etc.
Papers must be submitted via the CTM System by selecting the track “Special Session on AI with Geographic Information Systems for Social Good”. All accepted papers must be presented by one of the authors, who must register. Detailed instructions for submitting papers can be found at How to Submit .
Accepted papers will be published in the ICMLA 2020 conference proceedings (to be published by IEEE).
Selected high-quality papers will be invited to special issues of the Computational Social Networks – Springer.
Submission Deadline: August 24, 2020
Notification of Acceptance: September 24, 2020
Camera-ready papers & Pre-Registration: October 01, 2020
Special Session Organizers
Prof. Hai N. Phan, New Jersey Institute of Technology, USA (email@example.com)
Prof. Xinyue Ye, New Jersey Institute of Technology, USA (firstname.lastname@example.org)
Prof. Ruoming Jin, Kent State University, USA (email@example.com)
Prof. Qunying Huang, University of Wisconsin-Madison, USA (firstname.lastname@example.org)
Prof. Liping Yang, University of New Mexico ( (email@example.com)
- Xi Liu – Google
- Biplab Banerjee – IIT Bombay
- Subarna Tripathi – Intel AI Lab
- Lingfei Wu – IBM research AI
- Guido Cervone – Penn State University
- Zhenlong Li – University of South Carolina
- Bandana Kar – Oak Ridge National Laboratory
- Mingshu Wang – University of Twente
- Qiusheng Wu – University of Tennessee