A rendering pipeline optimized with features from game engines to enhance rendering speed by reducing GPU draw calls. As our project does not use a game engine, we must develop this pipeline from scratch.

Proprietary 3D model compression method, specifically designed to handle a large number of buildings, crucial for efficient streaming of model assets over the internet.

The first step involves importing geospatial data from the OSM database. This data is then processed by automated scripts for error detection and correction, and enhanced with custom data from government-maintained public sources. Finally, the processed data is saved into our database and converted into vector data.

3D models of water, land areas, roads, and some buildings are created on-demand on the client's side using three.js. More visually detailed buildings, currently limited to HDBs, are generated offline through procedural modeling techniques from game development.

1. Integrate real-time data into our system:
Integrate real-time data feeds, like traffic information, into our system using APIs from traffic data providers to display current conditions. Additionally, incorporating live event data might require partnerships with event organizers or social media platforms for timely updates in specific locations.

2. Build an automated process to update geospatial data:
Set up automated workflows to regularly fetch and integrate updated geospatial data into our database. This can involve using scheduled tasks to pull data from various sources like government databases, satellite imagery, and urban development records.

3. Use AI to generate more accurate 3D models:
Leveraging AI by using machine learning techniques to process images and data to create or refine 3D models of environments.