Resources for Unlocking the Power of Geospatial AI Using ArcGIS

[CanserinaKurnia]

(Updated 7/10/2024)

Geospatial AI is advancing the Science of GIS through AI-driven innovation, to automate data extraction, and uncover valuable insights faster than ever.   This blog provides links to the resources.

2024 Webinar recording on Leveraging AI in ArcGIS 

2023 Webinar recording on "Unlocking the Power of Deep Learning Applications using ArcGIS.

 

Multi levels approach to Geospatial Deep Learning in ArcGIS 

ArcGIS has tools to help with every step of the deep learning workflow including data preparation and exploratory data analysis, training deep learning models, deploying them for inferencing, and disseminating results using web layers and maps.  

Geospatial deep learning capabilities are available at multiple levels to accommodate a wide range of audiences.

The core of all geospatial deep learning in ArcGIS lies within the arcgis.learn module of Python API, at the very centermost circle. This module serves as a foundation for geospatial AI across imagery, 3D, tabular, text, and timeseries data.

While the Python API is designed for data scientists, scripters, and advanced users, the intermediate circle features GeoAI tools and apps that are primarily designed for geospatial analysts, or image analysts, which are user centered experiences. 

Finally, the outermost circle is built around ready-to-use pretrained models, aimed at making  geospatial deep learning more accessible to general users.

For the deployment, deep learning in ArcGIS can be performed in ArcGIS Online, ArcGIS Pro, ArcGIS Enterprise, and ArcGIS Notebooks, providing options for users with different proficiency levels.

Performing GeoAI using Ready-to-use pretrained models

We understand that the most time-consuming part of deep learning is collecting samples, labeling, and training the model. To make the application of deep learning more accessible, particularly for new users or those seeking expedited outcomes, Esri has made available a vast collection of geospatial deep learning models on the ArcGIS Living Atlas of the World. As of the date of this blog, there are currently 61 pretrained deep learning models available, with more expected in the future.

Through this storymap, you can explore a range of examples displaying how pretrained models have been implemented to extract features of interest including building footprints, parking lots, land cover classification, waterbody extraction, and more.

The list of  pretrained models are available to explore and download from with the ArcGIS Living Atlas of the World. 

Every pretrained model includes metadata and guide instructions. They can be utilized in ArcGIS Online Image Analysis tools, as well as in ArcGIS Pro and ArcGIS Enterprise.

For more information on these pre-built geospatial deep learning models, please refer to the following resources:

• Ready-to-Use Geospatial Deep Learning Models (blog)
• Introducing pre-trained geospatial deep learning models (blog)

It is also worth mentioning that the list includes the latest Segment Anything Model (SAM) from Meta:

• Segment Anything Model (SAM) - pretrained model
• Segment Anything Model (SAM) - samples

Tutorials

• Detect objects with a deep learning pretrained model (ArcGIS Pro)
• Identify infrastructure at risk of landslides  (ArcGIS Online | ArcGIS Pro)
• Detect objects with Text SAM (ArcGIS Pro)
• Improve a deep learning model with transfer learning (ArcGIS Pro)

 

Performing End-to-End Deep Learning and GeoAI in ArcGIS Pro

ArcGIS Pro is a powerful desktop application with the capability to perform GeoAI and deep learning analysis. ArcGIS Pro Image Analyst extension comes with a series of geoprocessing tools to perform end-to-end deep learning analysis from sample collection, export training data, to model training and inference.   Once a model has been successfully trained and evaluated, it can be applied for various applications such as object detection, terrain analysis, and image classification. The results can be published to ArcGIS Online for sharing. 

One important note is that deep learning capability is not inherently included in ArcGIS Pro. To gain access to this capability, it is necessary to first download the deep learning libraries installers for ArcGIS and install it into ArcGIS Pro. The installer includes a broad collection of deep learning components such as PyTorch, TensorFlow, Fast.ai and scikit-learn.

Check out the following resources to learn more about performing deep learning analysis using tools in ArcGIS Pro:

• Deep Learning Libraries Installers for ArcGIS
• Introduction to deep learning (doc)
• Try Deep Learning in ArcGIS (tutorial series)
• Deep Learning using ArcGIS Pro (web course)
• Deep learning frequently asked questions (doc)

Tutorials

• Classifying objects using deep learning in ArcGIS Pro
• Classify mangroves using deep learning
• Classify power lines using deep learning
• Use deep learning to assess palm tree health

Performing GeoAI analysis using Python in ArcGIS

The arcgis.learn is the core Python library of all geospatial deep learning within ArcGIS that provides deep learning capabilities within the ArcGIS desktop and web environments. This library simplifies the process of training diverse deep learning models, enabling users to not only train these models but also prepare data, manage that data, and infer at scale. The library is seamlessly integrated with ArcGIS and open science Python libraries.

Check out the following resources to learn more about performing deep learning analysis utilizing arcgis.learn python library:

• Deep learning models in arcgis.learn (blog)
• arcgis.learn module API Reference
• ArcGIS API for Python – Deep Learning Samples

 

Best Practices

There are several best practices to follow when performing geospatial deep learning which include deciding the ideal training dataset and size, knowing if the model is good, imagery and hardware considerations, etc. The following blogs and documentation provide some resources on this topic:

• Deep Learning with ArcGIS Pro Tips & Tricks: Part 1 (blog)
• Deep Learning with ArcGIS Pro Tips & Tricks: Part 2 (blog)
• Using deep learning for feature extraction (workflow)

Additional resources

• Imagery and Remote Sensing in Higher Education - Resources by topic: Deep Learning
• Higher Ed Guide to Esri E-Learning for Imagery & RS
• Deep learning in ArcGIS blogs
• GeoAI examples
• Imagery Workflows (Analysis and AI)
• Image analysis and AI capability
• Esri Community: Imagery and Remote Sensing

Overall, ArcGIS has powerful deep learning capabilities that are available at multiple levels to accommodate a wide range of users. Contact me at ckurnia@esri.com if you need more information or would like to discuss your geospatial deep learning project.