Digital Twin for Real-Time Urban Planning
The Digital Twin model of the city was created using Unreal Engine for high-fidelity, interactive 3D visualization and Cesium for geospatial mapping and rendering. These technologies allowed city planners to visualize and interact with the city in real time, gaining insights into traffic, energy usage, and other urban metrics.
We used Unreal Engine to generate high-quality, photorealistic 3D models of the city’s infrastructure, including buildings, roads, public spaces, and utilities. This enabled real-time interaction, with city planners able to navigate and explore the city as if it were a physical model.
For geospatial data integration, we used Cesium to provide a rich, dynamic 3D map of the city, layering various geospatial data, including land surveys, zoning information, topography, and other geographic data.
The Digital Twin used real-time data feeds from various urban systems, visualized through Cesium for mapping and Unreal Engine for interactive elements, creating a dynamic simulation of the city.
Geospatial Data Integration and Land Surveying
Accurate geospatial data and land surveying are essential for urban planning. We integrated advanced tools to manage and visualize land surveys, terrain data, and zoning information, allowing city planners to make data-driven decisions.
We used QGIS (Quantum GIS), an open-source platform, to handle large-scale geospatial data management, including topographic surveys, zoning maps, and land parcel information. This enabled us to import, process, and visualize data from a variety of sources.
Using QGIS and Cesium, we integrated detailed land surveys into the Digital Twin, ensuring that every new development and city expansion was mapped with accuracy. This data was used to simulate potential land-use scenarios, including zoning changes, construction planning, and infrastructure development.
For accurate elevation and spatial data, we utilized GPS and LIDAR (Light Detection and Ranging) technology. This data was processed and integrated into the Digital Twin for highly accurate terrain modeling and infrastructure planning.
API-Based Integration for Seamless Connectivity
The platform was built with API-based integration as a core principle, ensuring seamless data flow from the city’s various urban systems into the Digital Twin. We integrated existing infrastructure and third-party solutions into the platform using standard RESTful APIs to connect and synchronize data.
By connecting traffic management systems, energy grids, waste management tools, and environmental sensors to the platform via APIs, we provided city officials with a unified view of the city's operations, reducing the complexity of managing these systems separately.
The system was deployed on AWS, providing a highly scalable and secure environment to store, process, and manage large volumes of geospatial and urban data. The cloud-based infrastructure ensured that real-time data was accessible and sharable across departments and stakeholders.
Real-Time Analytics and Predictive Insights
To optimize urban services, we integrated predictive analytics into the platform. This allowed the city to monitor and forecast potential issues in real time, such as energy shortages, traffic congestion, or waste collection inefficiencies. By using advanced tools like Machine Learning (ML) and AI-powered analytics, the system could offer actionable insights.
Machine learning models were used to predict demand for resources (e.g., water, electricity), optimize traffic flow, and identify patterns in urban behavior, helping planners make more informed decisions.
The platform also generated real-time reports on key urban metrics (e.g., traffic congestion, energy consumption) that could be accessed by city officials at any time, reducing the need for manual reporting and increasing operational efficiency.
High-Fidelity, Real-Time Visualization with Pixel Streaming
For an immersive, real-time experience of the Digital Twin, we used Unreal Engine combined with Pixel Streaming. This allowed users to access high-quality, interactive 3D visualizations of the city on any device, without requiring high-end hardware.
With Pixel Streaming technology, we delivered photorealistic 3D visualizations and simulations directly to end-users’ devices—whether desktops, tablets, or mobile phones—without the need for specialized hardware. This made the Digital Twin easily accessible for both technical stakeholders and non-technical users.
The platform featured interactive dashboards and live visualizations, where users could toggle between different data layers—e.g., energy usage, traffic congestion, waste levels—enhancing decision-making and strategic planning.
