Agentic and Generative AI for Spatial Data Science: Toward Intelligent, Interactive, and Predictive Urban Systems

Dear Colleagues,

This Topic aims to bring together cutting-edge research on the integration of agentic artificial intelligence and generative AI to enable next-generation urban systems that are intelligent, interactive, and predictive.

With the rapid advancement of large language models (LLMs), Foundation models (FMs), multimodal generative models, and autonomous AI agents are evolving from static data representations into dynamic, decision-support systems capable of simulation, reasoning, and real-time adaptation.

This Topic seeks to explore how these emerging AI paradigms can enhance the automation, scalability, and usability of spatial data science across complex urban environments, including infrastructure systems, transportation networks, environmental monitoring, and disaster management. We invite original research articles, reviews, and case studies that address, but are not limited to, the following topics:

1. Agentic AI for Digital Twins

• Autonomous AI agents for simulation orchestration and workflow automation;
• LLM-based planning, reasoning, and decision-making in digital twin systems;
• Multi-agent systems for urban operations and infrastructure management;
• Human–AI collaboration and interactive decision support systems.

2. Generative AI in Urban Modelling and Simulation

• Generative models for synthetic urban data, scenarios, and environments;
• AI-driven simulation of complex urban processes (e.g., mobility, energy, fire spread, climate impacts);
• Surrogate modelling and hybrid physics–AI approaches for scalable simulations;
• Multimodal generation (text, image, 3D, spatiotemporal data) for urban systems.

3. AI-Enhanced 3D modelling and geovisualization

• Integration of LLMs, retrieval-augmented generation (RAG), and knowledge graphs in cartographic applications;
• Scalable and distributed architectures for urban digital twins;
• Automated and interoperable data pipelines for heterogeneous urban data sources;
• Geospatial AI frameworks and advanced 3D city modelling.

4. Interaction and Immersive Analytics

• Interactive digital twins using web-based platforms, VR/AR, and mixed reality;
• Conversational interfaces for digital twin systems;
• Visual analytics and decision-making support tools;
• Community engagement and participatory GIS.

5. Reliability, Validation, and Decision Support

• Integration of optimization and operations research for decision grounding;
• Model validation, uncertainty quantification, and robustness;
• Ethical considerations, transparency, and trust in AI-driven digital twins;
• Benchmarking and evaluation frameworks.

6. Applications in Smart Cities and Urban Systems

• Intelligent transportation systems and mobility digital twins;
• Environmental monitoring, climate resilience, and sustainability;
• Disaster management (e.g., wildfire, flood, and emergency response simulations);
• Infrastructure resilience and urban planning.

This Topic aims to foster interdisciplinary collaboration across AI, geospatial science, urban informatics, and engineering, and to establish a foundation for next-generation digital twin systems that move beyond data representation toward autonomous, intelligent, and actionable urban decision support.

Dr. Haowen Xu
Dr. Zhe (Sarina) Zhang
Dr. Di Zhu
Topic Editors