GeoAI with ArcGIS Pro 3.7: Build Intelligent Spatial Workflows with Machine Learning, Deep Learning, Imagery, and Foundation Models

By Michael Bell

GeoAI with ArcGIS Pro 3.7

A Practical Guide to Spatial Machine Learning, Deep Learning, Remote Sensing, and Intelligent Geographic Analysis

Artificial intelligence is changing the way geographic data is analyzed-but using AI with spatial data requires more than simply training a model and putting the results on a map.

GeoAI with ArcGIS Pro 3.7 provides a practical, project-focused introduction to applying machine learning, deep learning, remote sensing, spatial forecasting, foundation models, and 3D analysis to real geographic problems.

Starting with the fundamentals, Michael Bell takes you from organizing your first GeoAI project in ArcGIS Pro through increasingly advanced workflows involving spatial machine learning, imagery, object detection, segmentation, time-series forecasting, geospatial embeddings, LiDAR, and automation.

You will learn how to:

Prepare raster and vector data for machine learningEngineer useful spatial featuresBuild classification, regression, and clustering workflowsUnderstand spatial autocorrelation and spatial leakagePerform more reliable spatial validationTrain deep-learning models with geospatial imageryDetect buildings and other objects in aerial and satellite imageryPerform image segmentation and land-cover classificationAnalyze multispectral imagery and geographic changeBuild spatial time-series and forecasting workflowsExplore foundation models and geospatial embeddingsWork with LiDAR and 3D point cloudsAutomate GeoAI workflows with ModelBuilder and ArcPyEvaluate model accuracy and investigate failure casesUnderstand bias, uncertainty, explainability, and reproducibilityTurn individual analyses into complete GeoAI pipelines

The book also emphasizes something that is often overlooked in AI tutorials: a prediction that looks convincing is not necessarily a prediction that should be trusted.

Throughout the workflows, you will learn to examine model errors, avoid spatial leakage, validate predictions geographically, compare AI approaches with simpler alternatives, and connect model outputs to actual GIS decisions.

Whether you are a GIS analyst learning machine learning, a student building geospatial projects, a remote-sensing practitioner, or a developer exploring AI-powered geographic applications, this book provides a practical path from GIS data to evaluated GeoAI results.

Learn the tools. Build the models. Test the predictions. Understand where they fail.

• Format: Paperback
• Language: English
• ISBN: B0HGC2WR3Q
• ISBN13: 9798194677726
• Release Date: August 2026
• Publisher: Independently published
• Length: 164 Pages
• Weight: 0.88 lbs.
• Dimensions: 11.0" x 0.4" x 8.5"