Showing posts with label GeoAI. Show all posts
Showing posts with label GeoAI. Show all posts

Saturday, October 18, 2025

Call for Papers: Geosimulation and Its Emerging Directions with AI

[AAG 2026] GeoAI and Deep Learning Symposium: Geosimulation and Its Emerging Directions with AI


As part of the GeoAI and Deep Learning Symposium at the 2026 AAG Annual Meeting in San Francisco, California we have a call for papers for sessions entitled "Geosimulation and Its Emerging Directions with AI"

Call for Papers:

Simulating past, present, and future events can empower humans to understand the composition and interactions in complex systems and explain their emergence and evolution from bottom up. In practice, geosimulations constitute a powerful tool in engaging different stakeholders, exploring what-if scenarios, and evaluating alternative policy outcomes.

We invite interdisciplinary works for the exploration and understanding of complex social and environmental processes by means of computer simulation. We focus on all aspects of simulation and agent societies, including multi-agent systems, agent-based modeling, microsimulation, artificial intelligence (AI) agents, and the integration of Generative AI with simulation.

As GenAI is impacting all aspects of our lives, we are wondering how it will impact geospatial simulations. How do multimodal large language models (MLLMs) help with agent-decision making in the form of generating agent-personas or scheduling agent activities? Can MLLMs reduce coding barriers for beginners? Will GenAI lead to a new generation of modeling toolkits? What are the challenges brought by MLLMs in model design, validation, and computing costs?

We welcome a wide range of studies exploring simulation theories, data, methodologies, and frameworks. We are also interested in case studies applying geosimulations to address real-world challenges. Potential topic areas include, but are not limited to:

  • Geosimulation Models and Applications
  • Conceptual Geosimulation Models
  • General-Purpose Geosimulation Framework
  • AI and Geosimulation
  • Agents’ Behaviors, Decision-making and AI Agents
  • Data Generation Framework
  • Validation and Verification for Geosimulation
  • Digital Twins
  • Microsimulation
  • Multi-agent Systems

If you are interested, please email your title and 250-word abstract to Fuzhen Yin (fyin@uccs.edu) and Jeon-Young Kang (geokang@khu.ac.kr) by October 30th.

Chairs:

Organizers:

Sponsor Groups:

Tuesday, December 12, 2023

Book Chapter: GeoAI for Public Health

Our book, Handbook of Geospatial Artificial Intelligence edited by Song Gao, Yingjie Hu, and Wenwen Li, is published. It is my pleasure to contribute to the comprehensive handbook as a co-author of the chapter, GeoAI for Public Health. Special thanks to Andreas Züfle for his lead in writing the chapter.

Chapter

GeoAI for Public Health

By Andreas Züfle, Taylor Anderson, Hamdi Kavak, Dieter Pfoser, Joon-Seok Kim, Amira Roess.
BookHandbook of Geospatial Artificial Intelligence
Edition1st Edition
First Published2023
ImprintCRC Press
Pages25
eBook ISBN9781003308423

ABSTRACT

Infectious disease spread within the human population can be conceptualized as a complex system composed of individuals who interact and transmit viruses through spatio-temporal processes that manifest across and between scales. The complexity of this system ultimately means that the spread of infectious diseases is difficult to understand, predict, and respond to effectively. Research interest in GeoAI for public health has been fueled by the increased availability of rich data sources such as human mobility data, OpenStreetMap data, contact tracing data, symptomatic online surveys, retail and commerce data, genomics data, and more. This data availability has resulted in a wide variety of data-driven solutions for infectious disease spread prediction which show potential in enhancing our forecasting capabilities. This chapter (1) motivates the need for AI-based solutions in public health by showing the heterogeneity of human behavior related to health, (2) provides a brief survey of current state-of-the-art solutions using AI for infectious disease spread prediction, (3) describes a use-case of using large-scale human mobility data to inform AI models for the prediction of infectious disease spread in a city, and (4) provides future research directions and ideas.

20% Discount Available - enter the code AFL04 at checkout. For more information visit: www.routledge.com/9781032311661