Next level GIS using realtime 3d solutions with Unity and Esri

Posted on Tuesday, September 16, 2025 by RICHARD HARRIS, Executive Editor

Unity Technologies and Esri have announced a collaboration to combine GIS data with real-time 3D simulations. This integration allows organizations to visualize geographic and infrastructure data in interactive 3D environments. According to the companies, this approach supports more detailed and dynamic modeling of urban, transportation, and energy systems.

Integrating GIS with realtime 3d simulation

Esri’s ArcGIS Maps SDK can be used within Unity to render geographic data in 3D. This enables users to simulate urban spaces, infrastructure networks, and environmental conditions interactively. Unity’s 3D engine allows for real-time visualization of data such as pedestrian movement and infrastructure usage.

Key applications include:

  • Simulating pedestrian traffic: AI models can estimate crowd flow and bottlenecks in urban areas.
  • Optimizing infrastructure: Simulation of transit hubs, road networks, and evacuation routes can be created before construction or implementation.
  • Dynamic asset management: Infrastructure and energy assets can be visualized in 3D to track their condition over time using updated data.

The companies note that these capabilities help organizations make faster decisions and improve planning efficiency.



San Diego Convention Center digital twin

A demonstration project includes a digital twin of the San Diego Convention Center. This model uses the ArcGIS 3D tiling system and Unity’s Universal Render Pipeline to represent the venue and surrounding areas in 3D.

Applications demonstrated include:

  • Environmental simulations: Modeling of solar panels, wind turbines, and weather effects based on data inputs.
  • Crowd modeling: AI can simulate pedestrian movement based on recorded or hypothetical traffic patterns.
  • Transit optimization: Simulations can calculate how different scenarios affect peak-hour transit.
  • Infrastructure monitoring: Digital twins can reflect real-time updates where data is available.

The demonstration was shown on the Acer SpatialLabs View Pro 27, providing a glasses-free 3D view of the simulations.

Applications across industries

This integration supports practical uses in multiple sectors:

  • Urban planning: Planners can model traffic, pedestrian movement, and infrastructure layouts using GIS and 3D simulation data. Predictive modeling can estimate the impact of new roads, parks, or construction before implementation, improving efficiency and reducing congestion.
  • Infrastructure management: Visualization allows monitoring of complex networks such as water systems, electrical grids, or rail lines. Simulations help identify potential failure points before they occur, supporting proactive maintenance.
  • Emergency response: Disaster simulations can model flooding, seismic activity, and other hazards. Organizations can use these models to evaluate response strategies and improve safety planning.
  • Energy and environment: Renewable energy projects can model environmental impacts on assets such as wind turbines or solar arrays. Simulations enable evaluation of operational risks and resource planning.
  • Transportation: Transit operators can simulate passenger flow and network capacity. Analysis of peak-hour congestion or usage patterns can support infrastructure planning and efficiency improvements.

Next level GIS for real world applications

By combining GIS data with real-time 3D simulation, organizations gain tools for visualizing and analyzing complex systems. Unity and Esri describe this integration as a way to enhance GIS workflows, providing improved planning, predictive modeling, and dynamic visualization of geographic and infrastructure data.

Next level GIS applications can help cities evaluate new developments, support energy infrastructure management, and allow emergency planners to model potential disasters using available geospatial data. These tools provide actionable insights for decision-making, while outcomes remain dependent on input data and model assumptions.

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