Using Spatial Data in AI Workflows

Course details

AI is transforming how we analyze, interpret, and interact with spatial data, and this course shows you how to be at the front of that shift. Join geospatial data expert Matt Forrest as he covers how language models work, their advantages, and their limitations when applied to spatial data. Learn how to design and scope an AI-powered spatial tool with clear architecture, logical boundaries, and human review. Explore ways to build an MCP integration that enables Claude to analyze and respond to spatial information, and create an agentic workflow that connects location-based insights to language-driven tasks. Finally, learn how to build a dynamic map application that uses AI to filter results and interact with spatial data in real time. This course is an ideal fit for AI developers, data analysts, and geospatial and GIS specialists.

Learning objectives
Understand how language models work and their advantages and limitations in working with spatial data.
Create and design a scope for an AI powered spatial tool with a complete scope, architecture, logical limits, and human review.
Develop and build an MCP integration to analyze and provide spatial information with Claude.
Build an agentic workflow to connect location based insights into a language based workflow.
Create a map application that uses AI to filter results and move the map.

Concepts

Introduction

Unlocking the power of spatial data in AI workflows

Core Principles of Building AI-Powered Spatial Tools

How language models work with spatial data and real-world application
Ethical and responsible LLM use with spatial data

Select the Best Architecture and Scope Your Spatial AI Project

Compare spatial AI architectures - MCP, agentic, and app
Decision framework for selecting spatial AI architectures in projects
Scope an AI-powered spatial tool - Boundaries, controls, and review

Implement MCP Integration for AI-Powered Spatial Data

What MCP enables for spatial AI
Prepare and format spatial data for MCP analysis
Implement MCP integration with Claude
Validation strategies for MCP-based spatial outputs

Build Agentic Spatial AI Workflows

Design location to language agentic workflows
Implement an agentic spatial workflow in practice
Add human in the loop oversight to spatial AI
Test and debug agentic spatial workflows

Develop Interactive Map Applications with AI-Driven Features

Role of AI in map-based applications
Design AI-driven map interactions
Build the map application
Test and validate map outputs

Conclusion

Extend spatial-AI patterns for your future projects