Three-Dimensional Multitemporal Game Engine Visualizations for Watershed Analysis, Lighting Simulation, and Change Detection in Built Environments

This study explores the reuse of high-resolution 3D spatial datasets for multiple urban analyses within a game engine environment, aligning with circular economy principles in sustainable urban planning. The work is situated in two residential test areas in Finland, where watershed analysis, lighting simulation, and change detection were conducted using data acquired through drone photogrammetry and terrestrial laser scanning. These datasets were processed and visualized using Unreal Engine 5.5, enabling the interactive, multitemporal exploration of urban phenomena. The results demonstrate how a single photogrammetric dataset—originally captured for visual or structural purposes—can serve a broad range of analytical functions, such as simulating seasonal lighting conditions, modeling stormwater runoff, and visualizing spatial changes over time. The study highlights the importance of capturing data at a resolution that satisfies the most demanding intended use, while allowing simpler analyses to benefit simultaneously. Reflections on game engine capabilities, data quality thresholds, and user interactivity underline the feasibility of integrating such tools into citizen participation, housing company decision making, and urban governance. The findings advocate for a circular data approach in urban planning, reducing redundant fieldwork and supporting sustainable data practices through multi-purpose digital twins and spatial simulations.