Artificial intelligence continues to play a significant role in the geospatial, AEC, and 3D industries, influencing areas from data analysis to project management. The conversation surrounding AI is evolving as businesses increasingly adopt these new tools. To clarify some frequently used terms, let's look at five AI "buzzwords" circling in tech announcements, industry talks, and everyday conversations, along with real-world examples of their application.
Machine Learning
Machine learning (ML) is the foundation for many AI features found in modern mapping and AEC tools. ML models can detect patterns, classify large datasets, and improve predictions with exposure to new information. In geospatial workflows, ML drives the automation feature extraction from satellite or aerial imagery, structure analysis in digital twins, and predictive models for transportation networks. Many agencies and engineering companies are utilizing ML to streamline asset inspections, reduce manual review time, and analyze historical data to forecast problems and solutions.
Machine Learning in Geo Week News:
In an interview with Dr. Aaron Morris, Innovation Principal at Woolpert, Morris says that “We now have the ability to quickly spin up instances that already include machine learning platforms like TensorFlow, which was not as easily doable even a few years ago.”
Senior Content Manager Carla Lauter explains NPUs, writing that a neural processing unit (NPU) is a specialized microprocessor, also known as an AI accelerator or deep learning processor, designed to execute artificial neural networks and other machine learning workloads.
Computer Vision
This tool gives AI the ability to interpret data, making it extremely useful for drone workflows, mobile mapping, and construction verification. Companies are now using Computer vision (CV) models to detect equipment, quantify materials, track progress in BIM models, or identify hazards from site photos or scans. In lidar and reality capture workflows, CV techniques are helping segment dense point clouds and automate classification tasks that had previously required extensive user effort.
Computer Vision in Geo Week News:
Geo Week News describes OpenSpace as a visual intelligence platform for builders which uses computer vision and AI to help commercial builders reduce risk and increase efficiency.”
In an article introducing AlphaEarth Foundations, Geo Week News explains how earth observation has been made ripe for impact from the boom around artificial intelligence, with machine learning and computer vision techniques being developed to more efficiently parse through satellite imagery.
Large Language Model
Large language models, also referred to as LLMs, are AI systems trained on extensive text datasets to interpret and generate natural language. In geospatial, LLMs are being explored to help users phrase complex queries in a more conversational way that feels intuitive. For example, large language models allow users to ask direct questions to gather a response, instead of manually applying various filters to a search engine or program. Some platforms are also experimenting with LLMs to assist with metadata creation, summarize site assessments, or draft code for custom analysis. As these AI capabilities mature, LLMs may continue to become a common interface layer for interacting with spatial data in a way that is more accessible for the people who need to use it.
Large Language Model in Geo Week News:
In an article about new technologies unveiled at this year's ESRIs conference, Geo Week News gives context about large language models like ChatGPT.
Matt Collins writes about the symbiotic future of AI and digital twins, and notes that it’s even worth honing in specifically on generative AI and large language models, which is what is really driving this broader AI explosion across industries.
Agentic AI
This form of AI describes technology that can complete tasks or make decisions without requiring step-by-step instructions from users. Rather than “thinking,” this tech follows predefined logic and goals to independently carry out a multi-step workflow. This AI is becoming more popular as companies explore tools that can automatically validate sensor data, generate change-detection reports, or flag anomalies in construction imagery. Companies like Trimble and Bentley have begun referencing “agentic workflows” and “agentic AI” as they progress their technology to orchestrate tasks rather than respond to prompts from users.
Agentic AI in Geo Week News:
In their recent conference, Trimble unveiled its agentic AI strategy that embeds AI into solutions and enables improved data flow to take the next steps towards unlocking the power of connected data.
Autodesk also unveiled an AI assistant at their recent conference in September that utilizes agentic AI, and is built on eight years of research.
Foundation Model
A foundation model is a large, pre-trained AI system that can be adapted to specific domains with minimal additional data. Software companies are beginning to use and explore foundation models trained on their archives of lidar and imagery data. These models can support tasks like identifying vegetation encroachment along power lines, recognizing road signs, or converting unstructured project files into easily accessible information. Building on a foundation model can help vendors develop new features more efficiently and improve baseline performance.
Foundation Model in Geo Week News:
A geospatial foundational model, a first of its kind, was created from a collaboration with NASA announced in February from IBM that could speed up geospatial analysis by as much as four times.
In an article that explores what Google and NVIDIA means for geospatial AI, Geo Week News explains that Google's recent announcement of its GeoAI initiative combines generative AI with multiple foundation models to tackle complex geospatial reasoning tasks.
As AI tools and structures evolve, having clarity about the terminology surrounding them can help professionals better access and assess emerging capabilities and understand what new technologies can realistically offer. These terms represent more than their technical concepts but an ongoing shift in how spatial data is processed and applied. Whether you’re evaluating new software, shaping digital strategies, or staying engaged in innovations, a working understanding of these concepts can help distinguish meaningful innovation and support more informed decisions about future workflows.
