Generative Artificial Intelligence for Underground Utility Digital Twins: A Review and Task-Oriented Framework
Julien El Amine, Mahmoud Kalekish, Joumana Stephan, Christelle Tohme, Chamseddine Zaki, Roland Billen
DOI: 10.1109/ACCESS.2026.3717418This review explores how Generative Artificial Intelligence can advance Underground Utility Digital Twins by addressing incomplete data, missing geometry, semantic inconsistencies, and uncertainty. It presents a task-oriented framework connecting Generative AI approaches to data synthesis, geometry and topology reconstruction, semantic enrichment, multi-source data fusion, and uncertainty quantification, alongside a conceptual Generative-Enhanced Underground Utility Digital Twin (GE-UUDT) architecture.

