Recording Plan: 

Curated Track: 

Fang Qiu, 

Call For Participation
Relevant presentations can join our session. Please contact the following organizers:
Li An, Professor, College of Forestry, Wildlife, and Environment, Auburn University, anli@auburn.edu lan@mail.sdsu.edu
Tom Evans, School of Geography, Development & Environment, University of Arizona, tevans@nsf.gov (tomevans@arizona.edu)
Fang Qiu, Professor, Department of Geospatial Information Sciences, University of Texas at Dallas, ffqiu@utdallas.edu
Wenwu Tang, Professor, Department of Earth, Environmental, and Geographical Sciences, University of North Carolina at Charlotte, WenwuTang@charlotte.edu

Description:

Artificial intelligence (AI) and human-environmental systems are well interconnected and complementary with one another. On the one hand, AI can provide better tools for data processing, predictive modeling, and decision support, while human-environment systems act as real-world contexts that help refine AI methods and even theories. On the other hand, human-environment systems are often complex systems with social, economic, and ecological components that are entangled, making them difficult to understand and manage under traditional disciplinary approaches. This session will focus on the computational power and analytical tools AI provides to navigate complex human-environment systems, which can be manifested as below: 1) AI, including machine learning and deep learning, helps in data-driven analysis and predictive modeling, which are critically important for sustainable development, climate adaptation, and natural resource management; 2) AI contributes to real-time monitoring and adaptive management in complex systems where changes can happen quickly and unpredictably; 3) AI can help model social-ecological feedback loops, allowing researchers to better understand how these loops create stability or lead to tipping points; 4) AI can improve behavioral and cultural insights that are critical for understanding human-environment interactions. By linking such insights with ecological or geospatial models, AI enables a more holistic approach to managing resources and designing interventions that are culturally and socially appropriate.
The above AI related benefits are central to achieving sustainability targets like the United Nations’ Sustainable Development Goals (SDGs). AI-driven solutions can also help monitor automatically progress toward these goals in real time, ensuring that both human well-being and environmental health are maintained. At the same time, complex human-environment systems give AI researchers challenges that push the boundaries of what AI can achieve in real-world applications. In essence, improved insight into artificial intelligence and complex human-environment systems can lead to developing smarter, more sustainable ways to manage and adapt to the planet’s changing conditions, enhancing decision-making for complex systems, and improving resilience and risk management.

Non-Presenting Participants