Digital Twins address the fundamental challenges created by large volumes of geospatial data by enabling automated, near-real time processing and analysis, reducing the gap between data and the insights needed for decision-making. Here we present our open-source digital twin software framework, demonstrated through automated flood risk assessment.

Improved communication of flood risk via advanced visualisation could lead to increased and wider understanding of flooding, and therefore improved planning decisions and reduced impacts. We demonstrate the representation of modelled flood scenarios within virtual reality, and an open-source Python package for ingesting flood model outputs into Unreal Engine.