Nearmap partnership to accelerate 5G with digital twins

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Nearmap and Digital Twin Sims team up to create accurate digital twins of US cities to help telcos roll out 5G and new IoT services. Nearmap’s automation pipeline transforms high-resolution images and spatial data into 3D models for urban, construction, and urban planning. Digital Twin Sims is a telecommunications and network modeling consultancy.

The co-founder of Digital Twin Sim, Sameer Lalwani, started the company after a 25-year career in telecom planning. The industry has long used manual approaches to create models that started with a resolution of 100 meters and gradually improved to 10 meters. In recent years, he saw an opportunity to take advantage of high-resolution data from companies like Nearmap and cloud computing from companies like AWS and Microsoft Azure to reduce this city-scale resolution to 6 inches (15 cm).

Planning for complexity

5G deployments create a new level of complexity and require more precision than traditional mobile networks. For starters, 5G needs many more base stations to achieve the same levels of coverage as previous wireless services. In addition, many spectrum bands are more susceptible to interference or reflection from buildings or even tree branches. As a result, telcos must provide line-of-site paths between cell towers and coverage zones – something that has historically been an expensive and time-consuming process

The Nearmap platform automatically feeds data into the Digital Twin Sims engine to generate models that reflect changes, including new construction and even the growth of trees and other vegetation that can affect coverage. The platform can continuously update these models in response to changes detected in aerial and satellite surveys. This new approach allows Digital Twin Sims to simulate thousands of nodes in one afternoon, while a manual measurement would traditionally take several days to plot a single node in a 5G network.

The digital twin combines physical models of buildings and infrastructure – right down to lampposts – with demographics and business models that represent existing customers and insights into new opportunities. Better simulations help telco managers evaluate business opportunities and enable technical teams to plan and execute optimized infrastructure.

The process of creating a digital twin starts with telco planning teams setting a specific goal. A strategic overview for a nationwide network starts with low-resolution data for high-level planning. A more tactical analysis for estimating the cost and time to deliver a millimeter wave or 5G network would start with the highest resolution possible.

Currently, operators, marketing, sales, network planning, deployments and device buyers operate in different business process and data silos. Each group works on different stats that sometimes conflict with other departments.

“There has to be one entity where the datasets from all these sources are combined and looked at holistically, but that rarely happens,” Lalwani told VentureBeat.

Different sizes required

Digital Twin Sims creates a consolidated view of data from many sources and different formats, presented within a single user interface. For raster data, such as satellite and aerial imagery, they use GeoTIFF and are now moving to Cloud Optimized GeoTIFF (COG). All vector data representing line routes, building properties, and demographics is stored in GeoPackage (GPKG) files. Point cloud data derived from lidar scans is also captured and stored in .laz files.

The digital twin downloads data from the Nearmap API and loads it into a fixed-resolution H3 server. H3 is an open source geospatial indexing system developed by Uber. Lalwani said they usually modify their existing code for a specific scenario and run it in Docker containers based on the computer size needed.

Nearmap’s general manager of North America, Tony Agresta, said the company is prioritizing robust distribution of file formats that integrate easily with as many third-party applications as possible. They have developed tools to convert data feeds into standard ortho-images (as seen from directly above), 3D data created through photogrammetry, and vector AI data. 3D formats include OBJ, SLPK, 3MX, Cesium, and FBX files with mesh texture and point cloud data in a LAS file. 3D digital elevation models, digital terrain models (bare earth), and true ortho are all saved as GeoTIFF. Vector AI is stored in GPKG and Shapefiles.

Since it is still relatively early in the mass adoption and adoption of digital twins, best practices for developing this technology are still in progress. Engineers, data scientists and executives need to keep their options open to leverage the best data formats needed to simplify data pipelines, create useful simulations, and provide the best insight for every use case. Later, emerging efforts such as universal scene description (USD) could make it easier to transform data across different usage scenarios.

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