Sustaining Urban and Environmental Systems with Geospatial Digital Twins and Intelligent Sensing

Dear Colleagues,

The governance of complex urban and natural environments is entering a cognitive era driven by Internet of Things (IoT), Artificial Intelligence (AI), and High-Performance Computing. In this landscape, the convergence of Intelligent Sensing and Geospatial Digital Twins (GDTs) is critical for achieving sustainability. GDTs are dynamic, virtual representations of physical systems integrating geospatial data with real-time sensing and GeoAI to enable continuous monitoring, simulation, and immersive exploration [1–3]. Beyond static modeling, this integration builds high-fidelity, self-evolving replicas of the physical world for precise sustainability assessment.

In this Special Issue, we explore the integration of Intelligent Sensing and GDTs to support the UN Sustainable Development Goals. We invite the submission of original research on theoretical frameworks, key technologies, and innovative applications, particularly focused on three key areas:

• Advanced Data Modeling and Fusion: Multi-modal sensing fusion, 3D geospatial modeling, and AI-driven data processing.
• Urban Applications and Management: Adaptive urban governance, smart infrastructure monitoring, traffic optimization, and socioeconomic impact assessment.
• Environmental Monitoring and Interaction: Climate adaptation, disaster resilience, real-time pollution monitoring, and immersive visualization for decision-making.

We welcome empirical research demonstrating complete workflows from intelligent data acquisition to sustainable decision support, providing quantifiable evidence of environmental and socioeconomic benefits.
References:

[1] Bacher, U. (2022). Hybrid Aerial Sensor Data as Basis for a Geospatial Digital Twin. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B4-2022, 653–659.

[2] Döllner, J. (2020). Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88, 15–24.

[3] Zhang, J., Zhu, J., Zhou, Y., Zhu, Q., Wu, J., Guo, Y., Dang, P., Li, W., & Zhang, H. (2024). Exploring geospatial digital twins: a novel panorama-based method with enhanced representation of virtual geographic scenes in Virtual Reality (VR). International Journal of Geographical Information Science, 38(11), 2301–2324.

Prof. Dr. Chuli Hu
Dr. Jie Li
Dr. Chao Wang
Guest Editors

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