Event: Geo Week 2026
Location: Denver, Colorado
Host: Carla Lauter, Senior Content Manager, Geo Week
On the opening day of Geo Week 2026 in Denver, Colorado, five industry leaders took the stage for one of the conference’s most anticipated conversations: where is geospatial headed over the next ten years, and what does the industry need to do now to be ready? Moderated by Carla Lauter, Senior Content Manager for Geo Week, the panel brought together voices spanning global trade advocacy, federal ocean services, artificial intelligence leadership, LiDAR production, and solutions engineering. The result was a frank, forward-looking exchange that touched on AI realism, foundational data infrastructure, workforce development, and the persistent challenge of communicating geospatial’s value to the people who need it most.
AI Is Codification, Not a Magic Button
Dr. Aaron Morris, Global Director of AI, Woolpert, Inc.
Dr. Aaron Morris opened with a reframe that set the tone for the entire discussion. The AI transformation underway in the geospatial industry is real — but it is not the transformation most people reach for first when they hear the term.
What AI Actually Means for Geospatial Workflows
“Classification is AI — I might disagree with that,” Morris said, drawing a distinction that runs through much of the industry’s current confusion. What is genuinely new, in his view, is codification: the ability to train a system to replicate the complex, expert-driven workflows that have historically required years of human observation and slow, painful manual translation into software. “It’s the first time I think genuinely we can take a computer program and train it to start to do our things.”
At Woolpert, Morris frames the practical implementation as an information production line: the AI is the conveyor belt — it automates movement and processing — but the workers who understand how pieces fit together remain essential. The architects of those workflows are people who know how data works, how AI platforms function, and what model performance metrics actually mean. That expertise is not being replaced; it is being elevated.
The Sensor Amplification Opportunity
Morris drew on his robotics background — including PhD work at Carnegie Mellon during the DARPA Grand Challenge era — to illuminate a coming opportunity in the industry. LiDAR provides dense geometry data close to a sensor but grows sparse at distance. Cameras can see further but lack depth precision. The technique his team deployed on autonomous vehicles: train the camera to recognize traversable terrain patterns from where the LiDAR is confident, then project those patterns forward where the LiDAR is sparse. The result is sensor amplification — two modalities combining to produce capability that neither has alone. Applied to the proliferating sensor ecosystem in geospatial today, Morris sees this principle as foundational to the next generation of intelligent data products.
Reality Check from the Production Floor
Dan Bellissimo, Director of LiDAR and Remote Sensing, GIS Surveyors, Inc.
If Morris represented the AI optimist’s view, Dan Bellissimo brought the production manager’s reality check — and the two perspectives were more complementary than contradictory.
What AI Can and Cannot Do Today
Bellissimo runs a 500-person data extraction operation and is in regular contact with clients who have recalibrated their price and timeline expectations based on AI hype. The pressure is real: “They want that same data — maybe better — but they want it cheaper, because they’re seeing and reading and hearing all this about AI and machine learning.” The current reality: AI and machine learning tools are in the sandbox, they have improved significantly in the past five years, but they cannot yet take a raw point cloud from input to deliverable without human correction in established software. The tools are getting better. The trajectory is clear. But the industry needs an accurate picture of where capability actually is today.
Power Utilities, ADAS, and the BIM Opportunity
Bellissimo pointed to two sectors where geospatial’s role is growing fastest. In power utilities, LiDAR is primarily used for vegetation management — ensuring nothing encroaches on transmission corridors — but the same data could be solving far more problems than it currently is. As AI and data center demand grows and grid pressure increases, the industry’s stake in utility infrastructure is expanding.
In autonomous vehicles — more precisely, Advanced Driver-Assistance Systems (ADAS) — high-definition maps paired with in-vehicle LiDAR represent a maturing market. But Bellissimo sees the greater near-term opportunity in interior scanning for industrial automation: factories, distribution centers, and manufacturing facilities scanning their interiors to deploy autonomous systems and improve worker safety. The BIM market, he predicted, will grow substantially as a result.
Foundational Data as National Infrastructure
Rachel Dempsey, Deputy Assistant Administrator, National Ocean Service, NOAA
Rachel Dempsey brought a perspective that grounded the panel’s technology discussions in something more foundational: all of it depends on a consistent geodetic reference framework to function at all.
Why the Geodetic Framework Is the Backbone of Everything
Without a shared, precise coordinate system, LiDAR data and satellite imagery cannot be reliably integrated. Digital twins cannot accurately represent physical space. Autonomous navigation cannot achieve the centimeter-level precision that safety-critical applications require. “Without a consistent geodetic frame, those modern technologies don’t fit together,” Dempsey said. She described geospatial infrastructure as both a national asset and a national defense asset — pointing to maritime navigation safety and the kind of disasters that result from navigational failures as concrete stakes.
The NSRS Modernization: A 2027 Milestone
The centerpiece of Dempsey’s contribution was an update on the National Spatial Reference System (NSRS) modernization — a project that NOAA, NGA, NASA, and USGS are collaborating on to replace WGS 84. Beta versions were released in 2025, active feedback collection is ongoing, and full delivery is targeted for 2027. “It’s not only going to get us all on the same chart,” Dempsey said, “but it will allow us the flexibility to update it constantly so that we can actually represent our geoid as the living, breathing data sphere that it is.” Attendees were encouraged to seek out the additional NSRS modernization sessions and roundtable elsewhere in the Geo Week program.
The Geodesy Workforce Is in Crisis
Dempsey closed with a call that resonated through the rest of the panel: NOAA is not just struggling to grow its geodesy workforce — it is struggling to maintain it. Grants are in place and have been preserved, but they represent a fraction of what the situation requires. “It benefits us; it benefits you,” she told industry representatives directly. Rebuilding and expanding the pipeline of geodetic expertise is a shared responsibility between government and private industry.
The Education-Industry Speed Gap
Aaron Addison, Executive Director, World Geospatial Industry Council (WGIC)
Aaron Addison brought a global trade and workforce policy lens to the panel and offered its most pointed structural analysis of the talent pipeline problem.
Why the Curriculum Is Always Two Years Behind
The industry iterates, Addison argued conservatively, five to eight times for every single iteration in higher education. The curriculum approval process — moving a syllabus through department and dean-level review — takes eighteen to twenty-four months. By the time a course is approved, developed, and taught to its first student, the content can be two years old and twenty industry iterations behind. Students leaving those programs know it. “The things I learned in school are not what these companies seem to want” is a refrain Addison hears from recent graduates regularly.
Teaching Tolerance for Ambiguity
The deeper gap, Addison argued, is not technical curriculum coverage — it is the absence of professional formation. A GIS lab gives students a sequence of steps that produces a correct answer. Industry gives them problems that do not have clean answers, timelines that require returning to an unsolved problem day after day, and teams where collaboration and resilience matter as much as technical precision. Teaching students to stay energized in the face of ambiguity is something that does not come through coursework. It comes through mentorship. His prescription: industry mentorship programs structured to give young professionals genuine opportunities to succeed. “Give them opportunities to succeed; don’t set them up to fail.” Companies that invest this way, he argued, will see real returns.
Dr. Morris added a sobering data point: a major tech firm he knows has seen a seventy percent reduction in entry-level position hiring as AI tools absorb tasks that previously required junior staff. The paradox is acute — you cannot develop experts without training entry-level workers, but the economic incentives are shifting away from that investment.
Sensor Proliferation and the Integration Challenge
Andrew Brenner, Vice President of Solutions Engineering, NV5
Andrew Brenner framed the sensor landscape in terms that were simultaneously exciting and sobering: the industry’s ability to measure things has now outpaced its ability to make sense of what it has measured.
From Aerial Photography to Everything, Everywhere
Satellites are launching weekly with new thermal, hyperspectral, and multispectral capabilities. Autonomous vehicles, drones, mobile mapping platforms, and terrestrial scanners are generating data in modalities that barely existed a decade ago. “You tell me what you want to measure and I can tell you how you can measure it,” Brenner told the audience. “Now, I can’t tell you you can afford to measure it.” The challenge is no longer collection — it is taking all those individual source datasets and making sense of them together, and then answering the question that Brenner flagged as the real one: who cares?
The Silo Problem Is Still the Central Problem
Brenner was direct about where the industry’s biggest structural failure remains: data collected for one client or one purpose stays locked in that context, even when it could solve problems for someone else at no additional cost. High-density utility LiDAR that could inform local government planning. Forest inventory data that mirrors power corridor management challenges. The same geography mapped repeatedly instead of shared. “Sharing data makes everyone better,” Brenner said. The federal government’s free data-sharing model has created the community the industry benefits from today. Extending that ethos into private industry and local government partnerships is, in his view, one of the clearest paths to the next generation of geospatial impact.
The Bigger Picture: What This Panel Tells Us About the Next Decade
The AI conversation needs a reality calibration — urgently. Across every organization represented on this panel, the same tension is visible: clients have absorbed the AI hype and adjusted their cost and timeline expectations, while actual production systems remain human-dependent for quality assurance. The industry needs to hold two things simultaneously — genuine enthusiasm for where AI is heading, and honest communication about where it is today.
Foundational infrastructure is not a legacy concern; it is a prerequisite for everything else. Every technology discussed — AI-driven analysis, sensor fusion, digital twins, autonomous navigation — depends on an accurate, consistent, updatable geodetic reference frame. NOAA’s NSRS modernization is the substrate on which the next generation of geospatial capability will be built, and it is on track for 2027.
The data silo problem is structural, not technical. The tools for enterprise-wide data sharing and interoperability exist. What does not yet exist, at scale, is the organizational will and cross-sector trust required to break down the walls. The federal free-data model provides a working proof of concept. The industry’s challenge is extending it.
The talent pipeline gap is real and widening — but the solution is within reach. The education-industry speed mismatch cannot be fixed by updating curriculum alone. What is needed is structured mentorship at scale, industry investment in early-career development, and a shared recognition that replacing entry-level work with AI tools will hollow out the expert workforce within a decade if left unaddressed.
The trust conversation is the new competitive frontier. As geospatial data moves into executive decision-making — informing infrastructure investment, financial markets, insurance modeling, defense planning — the question shifts from “is the data accurate?” to “can I trust this output well enough to make a consequential decision based on it?” The industry that figures out how to answer that question, credibly and consistently, will earn a seat at every table that matters.
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