The Geography of Digital Twin: Concepts, Architectures, Modeling, AI and Applications

Dear Colleagues,

Digital twins (DTs) are a new paradigm of digital transformation that impact and provide feedback for real-world problems by presenting optional solutions. Geospatial research can be advanced in this paradigm by integrating real-time data, simulation models, and artificial intelligence to create dynamic, high-fidelity representations of physical and human systems and test potential solutions. With applications spanning climate change, urban planning, infrastructure management, public health, and environmental monitoring, DTs enable data-driven decision-making and predictive analytics.

This Special Issue explores the advancements in concepts, architectures, modeling, geospatial AI, interoperability, ethics, and applications of digital twins. Key challenges such as data interoperability, scalability, privacy, and governance will also be addressed. By bridging remote sensing, GIS, and AI, this issue aims to advance both the theoretical foundations and practical implementations of digital twins in geographic sciences. We invite researchers to contribute innovative methodologies, interdisciplinary perspectives, and real-world case studies. Potential Topics:

• Digital twin concepts, frameworks, and architectures;
• Spatiotemporal modeling and simulation in digital twins;
• Remote sensing and GIS integration for digital twins;
• Geospatial AI and machine learning for digital twins;
• Interoperability and data integration challenges;
• Ethics, governance, and data privacy in digital twins;
• Applications in climate change, smart cities, infrastructure, and public health;
• Big Earth data and digital twins for environmental monitoring;
• Case studies and best practices in geographic digital twins.

Prof. Dr. Chaowei Yang
Dr. Daniel Q Duffy
Dr. Xiao Huang
Dr. Lingbo Liu
Topic Editors