Description
1 day
Who is this course for?
GIS Analysts
GIS Technical Leads
Data Management Professionals
GIS Application Developers
Goals
Effective prompt writing techniques and managing dynamic JSON outputs from AI services.
A practical comparison of AI models from OpenAI, Gemini, Anthropic and local models using Ollama, understanding when to use each.
Learn how to incorporate custom image recognition using models trained and deployed in Roboflow as part of a data workflow.
PDF document extraction and conversion into spatially enabled feature class attributes.
Sentiment analysis and text classification techniques for unstructured text or image content.
Introduction to generative AI in spatial data workflows, understanding the value of structured data extraction as a key LLM capability.
A comparison of cloud-based and local AI models and setting up API connections to AI services using certified FME Hub Transformers.
Prompt engineering for geospatial data processing, writing effective prompts, handling dynamic JSON and GeoJSON responses and strategies for consistent structured outputs from generative AI.
Working with multiple AI providers (including OpenAI, Gemini and others) APIs, understanding the pros and cons of each provider and managing API keys and authentication within the Data Interoperability Extension.
Overview of open-source AI models, their value in addressing security and data privacy requirements, the capabilities and performance considerations.
Learn how custom image classification models can be trained and how such computer vision models can be used within Data Interoperability workflows, including their practical applications for feature attachment classification.
Document (unstructured data) extraction including extracting tables and spatial data from PDF documents, converting unstructured document content to feature attributes and handling multi-page document extraction.
Text classification and sentiment analysis, classifying unstructured text content, performing sentiment analysis and enriching geodatabase feature attributes with AI-derived values and Google-Search grounded data.
Building reusable workflows with AI, creating modular workspace designs, error handling for AI responses and strategies for scaling AI integration across your ArcGIS Enterprise or ArcGIS Online environment.
Attendees should have a basic working knowledge of the Esri Data Interoperability Extension for ArcGIS Pro, including understanding of readers, writers and transformers.
Attendees should understand how to:
- Know how to run a Data Interoperability Workspace
- Work with readers and writers for common spatial data formats including Shapefile, GeoJSON and file geodatabase
- Apply basic transformers for data manipulation
