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	19.8 Days	CHF 2400	Submit
Geomatics geomatics	2.8	5.1	2021	20 Days	CHF 1000	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	7.2	2012	34.2 Days	CHF 1900	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.9 Days	CHF 2700	Submit
Smart Cities smartcities	5.5	14.7	2018	26.8 Days	CHF 2000	Submit

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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 (registering DOI) - 31 Oct 2025

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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