Geospatial digital twins can make us all smarter

Author: Adina R. Gillespie is VP Strategic Initiatives for Geospatial Content Solutions at Hexagon's Geosystems division.

From the evening news to real estate marketing, 3D virtual environments seem to be everywhere. We are now accustomed to virtually flying through every part of the world or walking from room to room in an architect’s concept of a yet-to-be-built home. These 3D virtual scenes, often called digital twins, provide us with insights into the reality of present conditions and the impacts of future scenarios.

In nearly all cases, these digital twins are made possible by a remarkable integration of sensor and artificial intelligence (AI) technologies. Airborne sensors can now quickly and cost-effectively collect highly detailed and accurate imagery and elevation data that are core components of generating 3D environments. AI and machine learning algorithms can extract a variety of feature information to transform these geospatial data sets into immersive digital twins ready for visualisation, modelling, and simulation.

Digital twins are often linked to smart city applications using the virtual world to analyse the real world and determine how changes can make urban areas more livable, navigable, and sustainable. The affordability and accessibility of sensors collecting 3D geospatial data, however, will ensure the democratisation of digital twin technology from the outset. Not only can cities afford them, but the data sets as well as the modelling and simulation packages that leverage the 3D data can be obtained and utilised productively by the private sector for commercial purposes.

Digital twins can help everyone make more well-informed decisions. The advantages of using digital twins are many, but the most beneficial are undoubtedly those that involve modelling and simulating the future to discover the impacts of proposed changes to current conditions. Below are four scenarios that can assist in planning for the future by examining the risks of various options. 

Clearance analysis

As our cities become more densely developed and populated, traffic disruptions can have domino-like impacts on commerce – people are late for work, appointments are missed, and product deliveries are delayed. In this traffic model (Figure 1), a proposed truck route is simulated and reveals a turn is too sharp for a large vehicle to handle, potentially blocking traffic as the truck gets stuck.

City planners and traffic engineers can use this real-life information to designate alternate routes for large commercial vehicles to reduce disruption. Additionally, trucking companies can leverage the same digital twin simulation to train their drivers in advance how to navigate in the crowded downtowns of specific cities on their delivery manifests, saving time, and even minimising vehicle emissions. 

High-rise building construction planning

As city centres become more populated, high-rise apartment and condo buildings are becoming more prevalent in areas where new construction sites are limited in availability. In this real-life example (Figure 2), the developer of a proposed building showed potential investors how the new structure would appear in the cityscape. The developer simulated various building heights so the investors could see how the number of floors might impact the living experience, potentially adding dozens of units with premium views of the skyline that could be rented or sold at higher prices.

The same digital twin containing realistic 3D renderings of surrounding buildings near the proposed construction could be used by the local zoning authority to determine how the new structure would impede views from existing apartments nearby, contribute to existing parking woes, or magnify the heat sink effects of solar reflection from the façade. An accurate digital twin makes it possible like never before to simulate the reality of new construction and its negative and positive consequences for people and the environment. As this technology becomes commonplace, we can expect better and more efficient planning decisions. 

Downtown noise mapping

A realistic noise map may be the ultimate example of how digital twins have the potential for high-impact decision-making. In this simulation (Figure 3), the noise from an existing city tram has been modelled and represented as a volume bubble. Each layer of the bubble portrays loudness in decibels. As the tram moves through the city, so does the noise bubble, showing precisely where and how much each volume level impacts residential buildings, offices, and restaurants.

A potential renter or purchaser of a new domicile will be able to use this information in advance to determine if their new home will be uncomfortably loud each time a tram passes through. For a tramline in the planning stages, city transport planners could use the same simulation to identify the best routes to reduce noise impact – or perhaps plan for noise abatement walls that will shield homes, hospitals, and schools from ongoing noise pollution.

Event planning

Accurate digital twins for event venues enable planners to determine how many people the site can accommodate to plan ticket sales and apply for necessary permits. Organisers can model the layouts of these events in advance, creating a variety of configurations for where the stage, food stalls, first aid stations, and restrooms should best be located to allow for the most efficient and safest movement of attendees.

Most importantly, public safety officials can review the plans, simulate emergencies at various locations in or near the venue, and designate the safest ingress and egress routes in case of an incident. Precise mapping of streets, alleyways, and walkways makes it possible to determine how to get emergency vehicles in and attendees out as quickly as possible.

The HxGN Content Program is supporting initiatives like these with its Metro HD city data offering, including 3D mesh models of major cities. Captured with the Leica Citymapper-2, a true hybrid airborne system that integrates optical imaging and LiDAR sensors for simultaneous co-registered data capture, the 3D mesh models generate highly accurate and detailed digital twins that make simulations like those described above a reality. 

For more information on the HxGN Content Program , visit our website or follow us on LinkedIn.

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