Three Layers for the Analysts under the sky,
Imagery, Points, and Polygons, data easy on the eye.
Seven for the Scientists in their labs below,
SAR, Hyperspectral, Thermal, revealing secrets only they know.
Nine for the Planners doomed to vie,
Zoning, Parcels, Infrastructure, and Everything Nearby.
One for the People, deep and bold,
In the realm of Resolution where truth unfolds.
One Layer to rule them all, One Layer to align them,
One Layer to build upon and with terrain define them
In the real world where decisions lie.
As the one layer to rule them all Elevation is not only one of the most essential but one of the most difficult to work with.
I’ve been blessed to spend the better part of my career managing and advising geospatial data acquisition across a wide range of platforms and sectors, shaping the end to end geospatial value chain. From deploying vehicle-mounted cameras and lidar, processing high-resolution aerial imagery, and QCing satellite imagery to helping Microsoft compete with Google’s 3D maps, managing the production of Uber’s proprietary basemap, coordinating USGS and State 3DEP activity, and integrating the resulting data into critical business processes for real estate and construction companies it’s been awesome to see how data is collected, shaped, and utilized to influence the real world.
While each data type offers a different perspective on the world, none have captured my attention and passion like elevation. Not just because of my current work with 3DEP and prior work with Microsoft Bing, although these are certainly big influences. It’s something deeper. The more I work with elevation data, the more I realize how powerful and overlooked it really is.
Elevation underpins everything.
It governs how water flows, how and where infrastructure gets built, how we model risk and plan for resilience. It helps us determine where roads should go, how to optimally place cell towers, and understand whether a site is viable for development or vulnerable to flood.
Elevation is not just another mapping layer. It’s the foundation upon which all other layers must sit if you want to truly understand our physical world and the systems we’ve built to interact with it. It’s the one layer that rules them all.
And yet, despite its importance, elevation remains one of the most underutilized geospatial data types. I believe this is due to a unique combination of challenges: it’s harder to acquire, more complex to process, more difficult to host and serve, less intuitive to visualize, and perhaps most importantly, it requires users to make strategic choices about how to interpret and apply it.
The complexity starts with how it’s captured. Unlike imagery, which passively collects reflected light, the highest resolution elevation data requires active sensing. That means lidar systems mounted on drones, aircraft, and soon satellites, emitting pulses and measuring returns with centimeter precision. These systems need precise GPS, stable platforms, and ideal flying conditions. Moreover, the resulting datasets are not only massive, they require ground control points and extensive post-processing just to become usable. It’s a technically demanding, expensive, and often weather-dependent process.
Even after it’s collected, elevation data isn’t straightforward. A single raw lidar point cloud can be transformed into a dozen different derivative products: bare-earth DEMs, first-return DSMs, detailed DTMs, slope maps, contours, hillshades, canopy models, 3D meshes. Each is useful in its own way, but they’re not all interchangeable. Hydrologists, engineers, planners, foresters, advertisers, and game developers might all work from the same source data, but they each need radically different outputs depending on what they’re trying to do. This makes elevation more than just a dataset, it’s a foundation and at the same time a toolkit. Using that toolkit to build upon the foundation requires understanding what each derivative represents and how to store, manage, and apply it appropriately.
This level of complexity and interpretation creates a real challenge for integration. Most layers in a mapping system are simple: a road is a road, an image is an image. But elevation forces you to make choices about what to include, what to smooth or filter, what format and resolution to use. And since elevation isn’t immediately visible in flat maps, it often goes unnoticed by end users unless it’s rendered as a hillshade, contour map, or 3D scene which can make a map look cluttered and busy. This visualization challenge contributes to its underuse, especially in commercial mapping applications that prioritize visual polish and simplicity over spatial depth.
Meanwhile, most global mapping platforms rely on coarse terrain models like SRTM, ALOS, or photogrammetrically derived datasets that are decades old and nowhere near accurate enough for modern infrastructure, land use, or risk modeling. Even as agencies like USGS and partners in the 3DEP program make high-resolution elevation data publicly available, much of it remains siloed or underutilized.
The issue isn’t always lack of data for a specific AOI, it’s a lack of usable pipelines, global coverage, and most of all awareness.
This lack of awareness is producing a gap between what’s possible and what’s actually being implemented.
And in many ways the gap is growing. But as we enter an era defined by real-time digital twins, automated infrastructure, spatial computing, and AI-assisted planning, this gap will become harder to ignore across industries and sectors. As high resolution elevation data becomes more widely available and easily accessible thanks to programs like 3DEP and the USGS National Map, elevation data will no longer be just for specialists, but essential to how anyone, even the most novice user, models, interprets, and interacts with the physical world at scale.
To close this gap, we don’t just need more intuitive tools and a deeper understanding of how verticality shapes everything we do. We need to build systems that treat high resolution elevation data as a foundational layer. Most of all, I think we need to help people understand elevation data and how it underpins the functional geospatial ecosystem.I’ve come to believe that elevation data might be the most valuable geospatial layer we have. It’s complex, yes. It’s expensive, yes. But it’s also the key to building smarter systems, more resilient communities, and maps that actually reflect how the world works, not just how it looks.
So, if you’re curious about elevation data, how to access it, use it, or interpret it, hit me up. Let’s jam.
