3D Geospatial Data: The Quiet Backbone of Modern Infrastructure and Insurance

What 3D geospatial data is

The foundational product in the category is the digital elevation model, or DEM, which represents the height of the earth’s surface across a defined geographic area. DEMs come in different resolutions — measured in meters per pixel — and different vertical accuracies. A coarse DEM at 30-meter resolution is useful for high-level analysis; a high-resolution DEM at 1-meter resolution or better can support detailed infrastructure design.

Related products include digital surface models, which include the height of objects on the surface (buildings, vegetation, infrastructure), and digital terrain models, which represent the bare earth without surface objects.

Higher-order products built on top of these foundational data sets include hydrological models, viewshed analyses, slope stability assessments, line-of-sight calculations for telecommunications, solar exposure analyses, and many others. Each of these higher-order products takes the underlying elevation data as an input and applies analytical processing tailored to a specific use case.

How the data is produced

Modern geospatial data is produced through several different sensing modalities, each with its own strengths.

Light detection and ranging (LiDAR) uses laser pulses from aircraft or drones to measure distances to the ground and to surface objects. LiDAR produces high-resolution, high-accuracy data and is the gold standard for many applications. Acquiring LiDAR data over large areas requires substantial flight operations.

Interferometric synthetic aperture radar (IfSAR or InSAR) uses radar signals from aircraft to measure terrain elevation. Radar penetrates cloud cover, which makes it useful in regions where optical sensing is limited by weather. IfSAR can also be acquired more efficiently over large areas than LiDAR.

Satellite-based stereo imaging produces elevation data from overlapping satellite images. The accuracy is generally lower than LiDAR or IfSAR but the geographic coverage is broader.

Other sensing approaches — including drone-based photogrammetry and ground-based surveying — fill specific gaps in the data ecosystem.

Companies in the geospatial data category typically combine multiple sensing modalities, fuse the resulting data through specialized processing pipelines, and deliver curated data products to end customers.

The customer landscape

The market for high-quality geospatial data spans several industries.

Defense and national security have been long-standing customers. Terrain data is used for mission planning, weapons system targeting, communications and signal intelligence, and a wide range of other applications. Defense customers typically have stringent accuracy and currency requirements and prefer commercial sources that can supplement government-produced data.

Energy and utilities depend on terrain data for transmission line routing, pipeline planning, substation siting, renewable resource assessment, and infrastructure maintenance. The cost of getting infrastructure routing wrong is substantial, and the value of accurate terrain data in this context is high.

Telecommunications and broadcast use terrain data for cell tower siting, microwave path planning, and broadcast coverage analysis.

Insurance is one of the higher-growth customer segments. Flood, wildfire, and other catastrophe insurance lines depend heavily on terrain data to assess property-level risk. The shift toward more granular risk pricing has expanded the demand for high-resolution data and for analytical products built on it.

Transportation infrastructure planning — highways, rail, transit, ports — uses terrain data at every stage from corridor planning through detailed design.

Real estate and construction use terrain data for site evaluation, development feasibility, and design.

Each customer category has its own technical requirements, procurement processes, and pricing patterns. Companies in the category that have built deep relationships in multiple customer segments have more durable revenue than those concentrated in a single segment.

The insurance opportunity in detail

The insurance use case deserves particular attention because the underwriting environment is changing in ways that materially expand the demand for high-resolution geospatial data.

Property and casualty insurers have historically priced policies based on relatively coarse geographic information — postal code, broad flood zones, and basic property characteristics. The increasing frequency and severity of weather-related losses has put pressure on this approach. Insurers are moving toward more granular risk assessment, evaluating individual property exposures based on detailed terrain, structure, and environmental data.

That shift requires data inputs that were previously not part of mainstream insurance workflows. High-resolution elevation data, flood propagation models, wildfire vulnerability assessments, and similar geospatial products are increasingly procured as standard inputs to underwriting and pricing.

Reinsurance and catastrophe modeling firms have been longer-standing users of detailed geospatial data, and their workflows often set the standard that primary insurers eventually adopt.

What investors should think about

For investors evaluating companies in the geospatial data category, several considerations are central.

Data asset value is the foundation. The cumulative investment in acquiring and processing high-quality geospatial data over years creates a real asset that is difficult and expensive to replicate. The geographic coverage, resolution, and currency of a company’s data library are key features to understand.

Recurring revenue from licensing and subscription customers provides revenue visibility that pure project-based work does not. The mix of recurring and project revenue is important to evaluate.

Customer concentration risk is real. Defense and large utility customers can be substantial, and shifts in their procurement cycles can affect quarterly and annual results.

Technology investment matters. The sensing technology, processing software, and analytical capabilities that turn raw data into customer-relevant products continue to evolve. Companies that invest in technology development have a structural advantage.

International expansion is a meaningful opportunity. Many of the customer applications for geospatial data exist in international markets that are less well served than the U.S. and Canadian markets.

The structural backdrop

The longer-term picture for high-quality 3D geospatial data is shaped by several persistent forces.

Infrastructure investment globally, in both developed and emerging markets, continues to require high-quality terrain data as an input to planning and design.

Climate adaptation activities — flood mitigation, wildfire management, sea level rise planning — are driving demand for detailed environmental data at the local level.

Defense modernization across major economies continues to require terrain data for an expanding set of applications.

The insurance industry’s transition toward more granular property-level risk pricing is an early-stage development that will likely continue for years.

For companies positioned in this category, the demand backdrop is structurally favorable across multiple customer segments. The competitive question is not whether the demand will be there but who will supply it most effectively, at what price, and with what level of integration into customer workflows.