How GIS Benefits from Machine Learning and Deep Learning

Big Data is a big deal. Vast, complex data sets gleaned from networks of connectivity points contain valuable information that is rapidly changing how business is done. Individuals are also impacted by Big Data. Without it, many technological conveniences of everyday life would not exist.

In the interconnected world, data drives decisions. Increasingly, those decisions are made by “smart” computers and machines that can seamlessly apply human logic to the process. Geospatial artificial intelligence (GeoAI), which combines spatial analysis with machine learning and deep learning, is now a core part of modern GIS. It powers everything from pretrained models that pull features out of imagery to AI assistants built directly into GIS software.

Machine Learning: The Logic Behind Spatial Analysis

A subset of AI, machine learning uses algorithms that learn patterns from historical data, such as location and historical records, and then use those patterns to make predictions or decisions.¹

Machine learning has long been an integral tool within GIS spatial analysis, specifically as it relates to problem-solving in three key areas:

• Classification: Support vector machine (SVM) algorithms, along with methods such as random trees, help create land-cover classification layers, which discerns land types and can provide a comparative analysis of changes over time²
• Clustering: Input data points are analyzed for patterns that point to meaningful data versus random groupings of “noise”²
• Prediction algorithms: The ability to measure variables at different locations and apply geographically weighted regression analysis which allows local relationship models to be built and mapped instead of solely global versions. Newer multiscale geographically weighted regression (MGWR) goes further by letting each variable vary across its own geographic scale.²

All of these categories contribute to securing data-based predictions, but even the advanced logic of machine learning requires a human component to interpret the data and draw conclusions. That push toward greater automation led to deep learning, which has since become the engine behind many of the biggest advances in GIS.

Deep Learning: Applying Brain Power

Deep learning is a subset of machine learning built on neural networks with many layers, loosely modeled on how neurons in the brain signal one another. Classic machine learning relies on people to decide which features in the data matter. Deep learning learns those features directly from raw data such as images, which reduces human involvement but doesn't eliminate it.¹

Popularly dubbed an “artificial neural network” because of its autonomous logic and layered response to stimuli, deep learning has transformed how Big Data is parsed and used in geospatial AI. It “thinks” about what input it’s “looking” at and can consistently group images, detailed roadways, buildings, and other location-specific data without human intervention. As a result, tremendous amounts of data can be aggregated and applied in near real-time.

Given the immediacy of need and reliance on GIS, the appeal of deep learning among digital mapping experts is obvious.

The newest shift is foundation models: large models pretrained on massive amounts of satellite imagery that can be adapted to new tasks with far less labeled data. NASA and IBM released Prithvi, the first open-source geospatial foundation model, in 2023, and GIS platforms now offer foundation models alongside task-specific ones. At the same time, AI assistants that respond to plain-language requests are being built into everyday GIS tools.

The Lessons from AI Learning

Being able to gather and use location-based data that is continually fed into algorithms through literally a world full of sensors is an amazing accomplishment of the Internet of Things (IoT). 

The greatest value of machine learning and deep learning is the practicality they provide to spatial analysis in digital mapping. End users are assured of seamless data accuracy, quality, and freshness that they can leverage in a number of ways:

• Understanding traffic patterns and improving predictions
• Enabling more accurate maps in real-time to provide timely information such as traffic accidents, congestion, or re-routing
• Empowering decision-makers to locate, select, and expand into target-rich sites
• Improving last-mile tracking
• Advancing autonomous vehicle functionality
• Pushing out location-based marketing to encourage foot traffic and sales to people in the immediate area of a business

The “humanness” of AI helps GIS professionals properly manage the quantity, speed, and varying types of Big Data. In turn, the insights extracted from location-based data strengthen decisions and, ultimately, the value of digital mapping.

Learn more about spatial analysis in The Fundamentals of Geospatial AI: How Geographic Data is Changing Industries. Click the button below to download your copy now.

SOURCES
¹IBM, AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What's the Difference? updated 2024
²Esri, What's New in AI Tools and Models in ArcGIS (Q2 2026), 2026
³Esri ArcGIS Pro documentation, How Multiscale Geographically Weighted Regression (MGWR) Works
⁴NASA Earthdata, NASA and IBM Research Apply AI to Weather and Climate, May 2025