Digital twins, as digital replicas of physical assets that reflect the real-time status of those assets in the context in which they currently reside, promises to reshape how organizations manage their infrastructure (or physical) assets, integrating rich real-time data, geospatial intelligence, and next generation analytics. This chapter examined the architecture, data exchange standards and operational models of digital twins from the perspective of geospatial science and focus on infrastructure assets including transportation networks, utility grids, and urban facilities. This conversation touched on issues of asset lifecycle management through the use of digital twins, employing Internet of Things (IoT) telemetry, GIS integration, and artificial intelligence to identify when to design, construct, operate, maintain and decommission those assets. It also examined the digital twin enabling technologies, including spatial data infrastructures, systems that utilize cloud-edge architectures, and immersive user interfaces to build scalable, resilient, and responsive twin systems. Case studies were presented across diverse sectors of the economy including logistics, railways, port management, and environmental monitoring to unpack potential future applications of these technologies. The chapter also considered technical challenges such as interoperability, security, operation performance, and sustainability, while describing near term trends associated with digital twins such as autonomous digital twins, AI-powered decision making, and the development and commercialization of Digital Twin-as-a-Service platforms. Overall, the findings describe significant opportunities presented by digital twins to support smarter, more dynamic, and resilient management of infrastructure within geospatial contexts.