Some error occured while loading the Quick View. Please close the Quick View and try reloading the page.
Transform your spatial analysis capabilities with the power of GeoAI.
Learn the latest geospatial AI models and tools with Exploring GeoAI: Tools and Workflows. Use comprehensive, hands-on tutorials for implementing cutting-edge deep learning models and workflows—perfect for anyone ready to apply the transformative potential of GeoAI in their organizational operations.
Start by learning how to install deep learning frameworks, confirm hardware capabilities, and optimize system settings for peak performance. Then, progress through the complete GeoAI workflow: determining project needs, reviewing available data types and formats, assessing and training models, and evaluating performance with confidence.
Work through chapters that reinforce learning through carefully crafted tutorials featuring real-world datasets and step-by-step guidance. This approach enables you to quickly build the expertise to address complex spatial challenges. The tutorials demonstrate workflows for a variety of ready-to-use, pretrained deep learning models in ArcGIS Pro and ArcGIS Online, showcasing an evolving platform with powerful geospatial tools.
Key topics include:
- Installing and configuring deep learning frameworks
- Troubleshooting common technical challenges
- Selecting and evaluating models
- Detecting and classifying objects
- Transfer learning
- Classifying lidar point clouds
- Using predictive spatial analysis
This essential guide will empower readers with the practical skills to implement GeoAI and efficiently automate, predict, and optimize their geospatial work.
Introduction: Overview of the GeoAI capability
How to use this book
Chapter 1: Preparing for deep learning using ArcGIS Pro
Chapter 2: Object detection using pretrained deep learning models
Chapter 3: Improving a deep learning model with transfer learning
Chapter 4: Training a SAMLoRA model to identify specific features
Chapter 5: Classifying objects using deep learning
Chapter 6: Using deep learning for point cloud classification
Chapter 7: Training a model using automated deep learning
Chapter 8: Classifying land cover with a pretrained deep learning model in ArcGIS Online
Chapter 9: Training a regression model to estimate biomass in aerial imagery
Afterword: What’s next