<![CDATA[The rapid evolution of digital technologies is fundamentally reshaping the landscape of civil engineering, shifting the industry toward data-driven paradigms. This study provides a critical-thinking synthesis of how remote sensing, Geographic Information System (GIS), and Building Information Modeling (BIM) converge to facilitate enhanced decision-making throughout the infrastructure lifecycle. While traditional methods often suffer from data fragmentation and localized perspectives, this review identifies a significant research gap in the seamless integration of multi-scale spatial data into automated engineering workflows. Through an application-driven framework, this paper evaluates the comparative performance of various sensing platforms, ranging from InSAR-based satellite monitoring for regional deformation to UAV-borne LiDAR for high-precision as-built modeling. Furthermore, it examines the pivotal role of Artificial Intelligence (AI) in bridging the gap between raw data acquisition and actionable intelligence. By critically discussing technical limitations, such as data uncertainty and the “black-box” nature of automated processing, the study highlights the need for a hybrid approach that combines the efficiency of remote sensing with rigorous ground-truthing. The findings underscore that the future of civil engineering lies not in standalone technologies, but in a unified digital ecosystem where GIS serves as the spatial backbone and BIM as the micro-scale digital twin, together enabling more resilient and proactive infrastructure management.]]>
