GISCafe Industry Predictions for 2025 – GeoSapient

By John L Kelley,  CEO and CoFounder, GeoSapient, Inc.

John L. Kelley

GIS and AI integration.

GeoSapient explores the intricacies of geocomputing data, processes, and applications, going beyond the rise of space and airborne resources.

GIS Applications Transformed by Disruptive Generative AI Agents

GIS and Artificial Intelligence (AI) together revolutionize spatial analysis and decision-making. Let’s begin with a brief overview of AI Agents.

Autonomous AI software, AI agents, execute tasks, make decisions, and supply insights. Data (at rest and streaming) allows these agents to learn, reason, and adapt to changing environments. There are two categories of AI Agents. Processing vast amounts of text and structured data is the providence of Large Language Models (LLMs) which can power sophisticated analysis and predictions. Lightweight and optimized for specific tasks, Small Language Models (SLMs) are perfect for localized geospatial queries or particular feature extractions.

Agents will enable autonomous feature extraction from satellite imagery, accurately identifying roads, buildings, and water bodies. SLMs complement this by supporting targeted tasks like specific urban trend predictions or localized disaster risk modeling. Their combined effort allows for (near) real-time data-driven actions through real-time spatial decision-making.

LLMs are adept at producing sophisticated geospatial data, such as highly realistic 3D city models and interactive maps that respond to user requests. When combined with SLMs, these models generate highly localized visualizations like heatmaps or trend analyses, improving stakeholder understanding and action on complex spatial data. Broad-scale intelligence and fine-grained precision enhance GIS visualization, particularly useful in infrastructure planning and environmental monitoring.

LLMs and SLMs make GIS tools user-friendly for non-technical users through natural language interfaces. LLMs can address intricate questions such as “What regions within the AOI’s 800-year floodplain are most vulnerable to flooding and create a layer accordingly?” SLMs also enable streamlined processing of lightweight tasks, including automated localized map annotation or straightforward commands, leveraging specialized domain vocabulary. Working together empowers more stakeholders, thus democratizing access to advanced GIS features.

We must tackle ethical issues like data privacy and bias reduction to futureproof GIS using AI agents. LLMs excel at identifying dataset biases; SLMs excel at localized data security. By enhancing transparency, Explainable AI fosters trust among stakeholders in these tools. LLM and SLM agents make GIS an adaptive, accessible, and transformative platform for complex spatial problem-solving.

About Author:

John L. Kelley, President and Co-founder, GeoSapient, Inc.

Mr. Kelley is the President and Co-founder of GeoSapient, Inc. He has extensive experience in remote sensing systems engineering and associated geospatial applications. Before starting GeoSapient, Mr. Kelley spent most of his career in the aerospace industry developing remote sensing systems. Mr. Kelley’s passion is the subject of remote sensing of the Earth for environmental and geospatial applications. Driven by this passion, he co-founded GeoSapient to create a unique ‘Geospatial Knowledge’ platform that is not currently in existence. He also taught Remote Sensing as an adjunct lecturer at Villanova University and has guest lectured on the subject.  John.Kelley@GeoSapient.com

Tags: AI agents, autonomous feature extraction, generative AI, geospatial analysis, GIS, Large Language Models, Small Language Models

Category: Industry Predictions

This entry was posted on Thursday, January 30th, 2025 at 7:22 pm. You can follow any responses to this entry through the RSS 2.0 feed. You can skip to the end and leave a response. Pinging is currently not allowed.