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

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closed (20 September 2025)

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20 September 2026

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Topic Information

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

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Geomatics geomatics	2.8	5.1	2021	22.6 Days	CHF 1200	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	7.2	2012	33.1 Days	CHF 1900	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000	Submit

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Published Papers (4 papers)

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All Applied Sciences Geomatics IJGI Remote Sensing Smart Cities

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24 pages, 30238 KB  

Open AccessArticle

Efficient Four-Level LOD Simplification for Single- and Multi-Mesh 3D Scenes Towards Scalable BIM/GIS/Digital Twin Integration

by Siyuan Sun, Lin Su, Xukun Yang, Chunyu Qi, Xinyu Liu, Licheng Pan and Qilin Zhang

ISPRS Int. J. Geo-Inf. 2026, 15(2), 61; https://doi.org/10.3390/ijgi15020061 - 30 Jan 2026

Viewed by 316

Abstract

Efficient level-of-detail (LOD) management is crucial for handling large-scale 3D meshes in BIM, GIS, and digital twin applications. In practice, both individual models and complex multi-mesh scenes require multi-resolution representations. Yet two practical issues persist: (i) simplification rates are often fixed a priori, [...] Read more.

Efficient level-of-detail (LOD) management is crucial for handling large-scale 3D meshes in BIM, GIS, and digital twin applications. In practice, both individual models and complex multi-mesh scenes require multi-resolution representations. Yet two practical issues persist: (i) simplification rates are often fixed a priori, lacking principled guidance and yielding suboptimal fidelity–cost trade-offs; and (ii) after a scene-level target is set, workflows commonly impose a uniform rate on all models, which is ill-suited to heterogeneous geometry and produces uneven visual quality. This paper presents an automatic approach that constructs a cumulative edge collapse loss curve using a QEM (Quadric Error Metrics)-based process. Shape analysis of this curve defines four representative LOD targets, and an automated procedure then determines their corresponding simplification rates. The method is first developed for individual meshes and then extended to multi-mesh scenes, assigning model-specific rates that satisfy a prescribed scene-level reduction while maintaining visual consistency. Experiments on complex engineering datasets show higher fidelity than uniform-rate baselines, especially at high reductions. The approach provides a practical, automated framework for object- and scene-level LOD generation. Full article

(This article belongs to the Topic The Geography of Digital Twin: Concepts, Architectures, Modeling, AI and Applications)

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23 pages, 2493 KB  

Open AccessArticle

Rule-Based Scenario Classification Using Vehicle Trajectories

by Sungmo Ku and Jinho Lee

ISPRS Int. J. Geo-Inf. 2026, 15(1), 37; https://doi.org/10.3390/ijgi15010037 - 11 Jan 2026

Viewed by 366

Abstract

Ensuring the safety of autonomous driving systems (ADS) requires scenario-based testing that reflects the complexity and variability of real-world driving conditions. However, the nondeterministic nature of actual traffic environments makes physical testing costly and limited in scope, particularly for rare and safety-critical scenarios. [...] Read more.

Ensuring the safety of autonomous driving systems (ADS) requires scenario-based testing that reflects the complexity and variability of real-world driving conditions. However, the nondeterministic nature of actual traffic environments makes physical testing costly and limited in scope, particularly for rare and safety-critical scenarios. To address this, simulation has become a core component in validation by providing scalable, controllable, and repeatable testing environments. This study proposes a trajectory-based scenario classification framework that emphasizes both generality and interpretability. Specifically, we define a set of rule-based maneuver classification criteria using lateral acceleration patterns and apply them to simulated urban driving scenarios modeled with OpenSCENARIO. To address overlapping maneuver characteristics, a priority ordering of classification rules is introduced to resolve ambiguities. The proposed method was evaluated on a dataset comprising 7 types of maneuvers, including straight driving, lane changes, turns, roundabouts, and U-turns. Experimental results demonstrate the effectiveness of rule-driven classification based on vehicle trajectory dynamics and highlight the potential of this approach for structured scenario definition and validation in ADS simulation environments. Full article

(This article belongs to the Topic The Geography of Digital Twin: Concepts, Architectures, Modeling, AI and Applications)

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26 pages, 4176 KB  

Open AccessFeature PaperArticle

An Effective Approach to Geometric and Semantic BIM/GIS Data Integration for Urban Digital Twin

by Peyman Azari, Songnian Li and Ahmed Shaker

ISPRS Int. J. Geo-Inf. 2025, 14(12), 478; https://doi.org/10.3390/ijgi14120478 - 2 Dec 2025

Viewed by 1259

Abstract

Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper [...] Read more.

Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper proposes a novel, scalable methodology for comprehensive BIM/GIS integration, addressing both geometric and semantic challenges. The approach introduces a geometry conversion workflow that transforms solid BIMs into valid, simplified CityGML representations through a level-by-level detection of building elements and outer surface extraction. To preserve semantic richness, all entities, attributes, and relationships—including implicit connections—are automatically extracted and stored in a Labeled Property Graph (LPG) database. The method is further extended with a new CityGML Application Domain Extension (ADE) that supports Multi-LoD4 representations, enabling selective interior visualization and efficient rendering. A web-based urban digital twin platform demonstrates the integration, allowing dynamic semantic querying and scalable 3D visualization. Results show a significant reduction in geometric complexity, full semantic retention, and robust performance in visualization and querying, offering a practical pathway for advanced UDT development. Full article

(This article belongs to the Topic The Geography of Digital Twin: Concepts, Architectures, Modeling, AI and Applications)

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23 pages, 8342 KB  

Open AccessArticle

Digital Twin-Ready Earth Observation: Operationalizing GeoML for Agricultural CO₂ Flux Monitoring at Field Scale

by Asima Khan, Muhammad Ali, Akshatha Mandadi, Ashiq Anjum and Heiko Balzter

Remote Sens. 2025, 17(21), 3615; https://doi.org/10.3390/rs17213615 - 31 Oct 2025

Cited by 1 | Viewed by 1093

Abstract

Operationalizing Earth Observation (EO)-based Machine Learning (ML) algorithms (or GeoML) for ingestion in environmental Digital Twins remains a challenging task due to the complexities associated with balancing real-time inference with cost, data, and infrastructure requirements. In the field of GHG monitoring, most GeoML [...] Read more.

Operationalizing Earth Observation (EO)-based Machine Learning (ML) algorithms (or GeoML) for ingestion in environmental Digital Twins remains a challenging task due to the complexities associated with balancing real-time inference with cost, data, and infrastructure requirements. In the field of GHG monitoring, most GeoML models of land use CO₂ fluxes remain at the proof-of-concept stage, limiting their use in policy and land management for net-zero goals. In this study, we develop and demonstrate a Digital Twin-ready framework to operationalize a pre-trained Random Forest model that estimates the Net Ecosystem Exchange of CO₂ (NEE) from drained peatlands into a biweekly, field-scale CO₂ flux monitoring system using EO and weather data. The system achieves an average response time of 6.12 s, retains 98% accuracy of the underlying model, and predicts the NEE of CO₂ with an R² of 0.76 and NRMSE of 8%. It is characterized by hybrid data ingestion (combining non-time-critical and real-time retrieval), automated biweekly data updates, efficient storage, and a user-friendly front-end. The underlying framework, which is part of an operational Digital Twin under the UK Research & Innovation AI for Net Zero project consortium, is built using open source tools for data access and processing (including the Copernicus Data Space Ecosystem OpenEO API and Open-Meteo API), automation (Jenkins), and GUI development (Leaflet, NiceGIU, etc.). The applicability of the system is demonstrated through running real-world use-cases relevant to farmers and policymakers concerned with the management of arable peatlands in England. Overall, the lightweight, modular framework presented here integrates seamlessly into Digital Twins and is easily adaptable to other GeoMLs, providing a practical foundation for operational use in environmental monitoring and decision-making. Full article

(This article belongs to the Topic The Geography of Digital Twin: Concepts, Architectures, Modeling, AI and Applications)

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