Digital Twins and Urban Heat Island Modeling: A Systematic Review of Conceptual, Technical, and Geospatial Gaps in Next-Generation Urban Climate Systems
DOI:
https://doi.org/10.24114/jg.v18i1.71340Keywords:
Digital Twin, Urban Heat Island, Remote Sensing, Machine Learning, Urban SustainabilityAbstract
This systematic review examines the emerging integration of Digital Twin technologies with Urban Heat Island modeling to advance next-generation urban climate systems. Increasing urbanization and rising thermal stress have intensified the need for dynamic, data-driven tools capable of representing and predicting microclimate conditions in real time. Through a structured PRISMA-based screening of major scientific databases (Scopus, IEEE Xplore, ScienceDirect), 268 initial records were identified, from which 19 studies were ultimately included after systematic deduplication (25 duplicates removed) and eligibility screening (16 studies excluded: 4 lacking technical information, 10 non-urban settings, 2 non-English). These 19 studies collectively illustrate three major knowledge domains: conceptual frameworks, technical architectures, and geospatial modeling characteristics. The findings indicate that Digital Twin is progressively regarded as a real-time, adaptive digital representation of the urban environment; however, it lacks standardized definitions for climate applications (identified in 68% of reviewed studies). Technically, the integration of heterogeneous data—ranging from Internet of Things (IoT) sensors, UAV thermal imagery, and satellite-derived land surface temperatures—remains limited by challenges in latency, model calibration, data interoperability, and computational scalability (reported in 74% of studies). Geospatial analysis further highlights inconsistencies in spatial-temporal resolution and inadequate representation of suburban areas (noted in 63% of studies), constraining robust Urban Heat Island simulations across scales. Overall, this review identifies critical gaps and emerging opportunities for developing intelligent, multi-scale, and hybrid modeling approaches that combine physics-based simulations with machine learning. The findings call for harmonized Digital Twin frameworks, improved geospatial data infrastructures, and stronger interdisciplinary collaboration to support climate-resilient urban planning and adaptive heat mitigation strategies.
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