Precision to prediction:

Harnessing geospatial intelligence for smarter, sustainable growth

As AI helps make data capture faster and more comprehensive and expands the use cases for digital twins, it becomes increasingly critical to ensure measurements and models are precise.

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Burkhard Böckem

Chief Technology Officer at Hexagon

Surveying has long been the backbone of geodata-driven industries, providing the high-accuracy measurements that enable impactful insights. Many sectors increasingly apply geodata, for example, through AI-enabled digital twins.

Surveyors are needed now more than ever to ensure mass digital data is also precise data. Quality control at the point of capture is crucial so that scaled insights will not have a flawed basis. With accuracy, geospatial data turns into intelligence for sustainable growth and resilient operations.

Digital twins, with AI

Digital twins – machine-readable models that replicate physical entities – enable industries to anticipate challenges, optimise processes, and innovate solutions. Applying artificial intelligence to digital twins unlocks a range of new creative capabilities for solving problems and extracting insights.

For instance, AI algorithms paired with edge computing process vast amount of sensor data close to the source. That means digital twins can monitor environments in real-time, predict potential failures, and even simulate different scenarios to identify risks and find performance-enhancing solutions.

AI is also shaping the way we capture the data that forms the foundation for digital twins. For example, the Leica iCS50 is a first-of-its-kind fusion of computer vision and laser precision in a very compact, wireless system that closes the gap between design and execution. By using spatial AI at the edge for tracking, it takes the complexity out of demanding layout and measuring tasks so teams can work faster, smarter, and with full confidence that every detail is right the first time.

The impact of digital twins across industries

Several industries rely heavily on geospatial data for digital twins, including construction, urban planning, and environmental management.

In construction projects, precise geodata is vital for planning, monitoring progress, and resolving conflicts. In construction, accurate geodata is essential for planning, tracking progress, detecting changes, and ensuring compliance. For example, AI-enhanced digital twins can help flag potential issues early and enable proactive solutions by helping answer questions such as: do the as-built conditions match the original plans? Does the building meet requirements for firestopping; heating, ventilation, and air conditioning (HVAC) systems; or other safety standards?

Zoom out and you will find urban planners using geodata to design cities that are more efficient and sustainable. Here, digital twins showcase their transformative potential by simulating air pollution, wind, and temperature using computational fluid dynamics.

And of course, environmental managers rely on geospatial insights to assess the overall impact of urban construction on the surrounding ecology. For example, projects like R-evolution’s Green Cubes use digital twins to measure biodiversity and carbon sequestration, supporting sustainability efforts.

A prime example of the impact of AI-enabled digital twins is seen in Klagenfurt’s urban planning. Here, an AI-enabled digital twin was created using

aerial images

and a wealth of ground data for a complete 3D picture. They used this digital twin to analyse solar potential and urban planning scenarios, enabling the city to double solar panel installations and increase green spaces – contributing significantly to its goal of climate neutrality by 2030.

The importance of surveying

However, as AI helps make data capture faster and more comprehensive and expands the use cases for digital twins, it becomes increasingly critical to ensure measurements and models are precise. This makes the expertise of surveyors crucial for establishing control, checking for georeferenced alignment of points clouds, and knowing which technologies to employ across environments to capture interiors and exterior infrastructure fully and accurately.

By providing accurate data, surveying now fuels dynamic, AI-driven digital twins that forecast problems and optimise decisions long before concrete is poured or roads are built. As machine learning, edge computing, and advanced sensors continue to evolve, the value of measurements will increasingly be judged by the tangible social, economic, and environmental outcomes they enable, including shorter build cycles, affordable housing, greener cities, and healthier ecosystems. By integrating these technologies thoughtfully, guarding data quality, and nurturing the skill sets that turn raw geodata into actionable foresight, the surveying profession will remain the catalyst of a smarter, more resilient world.