Geospatial AI - Full Length with Demos

Esri Events
21 Apr 202127:15

TLDRThe video script introduces Geospatial AI, a system that applies machine learning and deep learning techniques to enhance GIS capabilities. It covers the end-to-end system of RTS, the ArcGIS Learn module, and pre-trained models for various geospatial tasks. The script highlights real-world applications, such as updating base maps in Kuwait, extracting railway assets in New Delhi, and creating a tree inventory app in Johns Creek, Georgia. The technology aims to deliver an intelligent GIS that senses, learns, and adapts, improving as data flows through the system.

Takeaways

  • 🌐 Geospatial AI integrates machine learning and deep learning techniques to solve complex problems and enhance GIS capabilities.
  • 🤖 The system supports two types of operations: human-in-the-loop, where AI augments human expertise, and fully autonomous systems.
  • 🛠️ ArcGIS Learn is a Python API module that simplifies the training of various geospatial deep learning models on user data.
  • 📊 The module supports a wide range of data types, including imagery, 3D point clouds, feature tabular, and time series data.
  • 🚀 Pre-trained models are available for deployment in production, reducing the need for extensive training data and computational resources.
  • 📈 The AI system can process and analyze unstructured data, such as text-based reports, using natural language processing (NLP) models.
  • 🌍 Real-world applications of geospatial AI include updating base maps, extracting railway assets, and analyzing full-motion video data.
  • 🏙️ In Kuwait, AI is used to modernize business processes and update infrastructure data, significantly improving efficiency and supporting the country's vision 2035.
  • 🛤️ In New Delhi, pre-trained models are fine-tuned to extract precise railway asset measurements from point cloud data, enhancing safety and accuracy.
  • 📲 Johns Creek, Georgia, leverages AI to create a smartphone application for citizens to map and classify trees, contributing to a comprehensive tree inventory.
  • 🌳 AI-infused solutions are being integrated into ArcGIS products, such as the 3D base map solution, to create more realistic and useful GIS applications.

Q & A

  • What is Geospatial AI and how does it enhance GIS?

    -Geospatial AI applies spatial machine learning and deep learning techniques to solve complex problems and derive deeper insights. It enhances GIS by expanding its capabilities to support human-in-the-loop systems and building fully autonomous systems, improving efficiency and accuracy in data analysis and decision-making.

  • What are the two types of systems that Geospatial AI supports?

    -Geospatial AI supports human-in-the-loop systems, where human expertise is augmented by AI, and fully autonomous systems that can operate independently.

  • What does an end-to-end Geospatial AI system encompass?

    -An end-to-end Geospatial AI system includes data access, preparation, labeling, model training, QA, validation, inferencing at scale in production environments, and decision-making based on inferencing outcomes.

  • What is the role of ArcGIS Learn in Geospatial AI?

    -ArcGIS Learn is a module within the Python API that enables users to train various geospatial deep learning models on their data, supporting tasks like feature extraction from imagery and unstructured text processing.

  • How can users experience Geospatial AI without technical expertise?

    -Users can experience Geospatial AI through pre-trained models, ready-to-use tools within ArcGIS, and AI-infused apps and solutions where the AI aspect is unseen but enhances the user experience.

  • What kind of data does the ArcGIS Learn module support?

    -The ArcGIS Learn module supports oriented and overhead imagery, 3D point clouds, feature tabular and time series data, and unstructured text.

  • How does the Public Authority for Civil Infrastructure in Kuwait use Geospatial AI?

    -The authority uses Geospatial AI to update base maps by analyzing satellite imagery and logs to identify gaps in their state networks and data, significantly improving the efficiency of their mapping processes.

  • What are the benefits of using pre-trained models in Geospatial AI?

    -Pre-trained models allow users to deploy AI capabilities without extensive training data or expertise in Python, saving time, cost, and compute resources while providing high accuracy and recall.

  • How does the deep learning model for object tracking in full motion video work?

    -The deep learning model for object tracking can be used to automate the process of tracking objects in video data, saving the critical information directly to a geodatabase, and can be fine-tuned using specific data for enhanced accuracy.

  • What is the purpose of the deep learning tools for point cloud classification in ArcGIS Pro?

    -These tools, based on the Point CNN model, allow for the classification of point clouds, such as distinguishing between different types of buildings, trees, and ground points, enhancing the accuracy and efficiency of 3D data analysis.

Outlines

00:00

🌐 Introduction to Geospatial AI

This paragraph introduces the concept of geospatial AI, explaining its application of spatial machine learning and deep learning techniques to solve complex problems and derive insights. It highlights the expansion of GIS capabilities and the two types of systems it supports: human-in-the-loop and fully autonomous systems. The paragraph also outlines the end-to-end geospatial AI system, from data access and preparation to model training, QA, validation, and inferencing in production environments.

05:02

📚 ArcGIS Learn and Machine Learning Models

The paragraph discusses the ArcGIS Learn module within the Python API, which allows for the training of various geospatial deep learning models. It mentions the pre-trained models available for deployment and the possibility of transfer learning with user data. The paragraph also covers the integration of AI models into ArcGIS as ready-to-use tools and apps, emphasizing the ease of use and the potential for users to benefit from AI without needing to understand its underlying processes.

10:04

🌟 Real-world Applications of Geospatial AI

This section showcases real-world applications of geospatial AI, with a focus on the Public Authority for Civil Infrastructure in Kuwait. It describes how they use ArcGIS Learn to update base maps for infrastructure projects, significantly improving efficiency and supporting Kuwait's 2035 vision. The paragraph also highlights the use of deep learning for feature extraction from imagery and other geospatial datasets.

15:07

🚀 Pre-Trained Models and Deep Learning Accessibility

The paragraph emphasizes the release of pre-trained geospatial deep learning models on Living Atlas, making deep learning more accessible. It discusses the models available for tasks like building footprint extraction and land cover classification, and how these models can be fine-tuned for specific use cases. The paragraph also mentions the efficiency gains and cost savings achieved through the use of these models.

20:08

🔍 Advanced Tools and AI Integration in ArcGIS Pro

This section introduces advanced tools in ArcGIS Pro that leverage AI for tasks such as object tracking in full-motion video and point cloud classification. It discusses the capabilities of these tools, including the ability to analyze videos, capture critical information, and automate workflows. The paragraph also mentions the upcoming release of deep learning tools for point cloud classification and interactive object detection in 3D scenes.

25:08

🌳 AI-Infused Apps and Solutions

The final paragraph focuses on the development of AI-infused apps and solutions using ArcGIS frameworks. It describes a case study from Johns Creek, Georgia, where a smartphone application was developed to help citizens map and classify trees using deep learning capabilities within ArcGIS Learn. The paragraph highlights the process of setting up the training environment, collecting training data, and building the application, resulting in a lightweight, deep learning-enabled app for tree inventory.

Mindmap

Keywords

💡Geospatial AI

Geospatial AI refers to the application of machine learning and deep learning techniques to geospatial data to solve complex problems and gain deeper insights. In the video, it is used to enhance GIS (Geographic Information System) capabilities, allowing for more efficient and accurate data analysis and decision-making in various fields, such as urban planning and environmental monitoring.

💡Human-in-the-loop

This concept involves integrating human expertise with AI systems to enhance decision-making processes. In the context of the video, it means that while AI assists in tasks like data analysis, human input is still required for validation and critical decision-making, ensuring a balance between automated processes and human oversight.

💡Autonomous Systems

Autonomous systems are those that can operate independently without human intervention. The video discusses the development of AI systems that can perform tasks such as data labeling, model training, and inference on their own, which is a significant advancement in the field of geospatial AI.

💡ArcGIS Learn

ArcGIS Learn is a module within the Python API for ArcGIS, which simplifies the process of training various geospatial deep learning models on user data. It supports a wide range of data types and tasks, such as object classification and detection, and is a core component of the end-to-end geospatial AI system discussed in the video.

💡Pre-trained Models

Pre-trained models are AI models that have already been trained on large datasets and can be deployed for specific tasks without the need for additional training. In the video, these models are made available for users to deploy directly into production, saving time and resources that would otherwise be spent on training new models from scratch.

💡Transfer Learning

Transfer learning is a method where a pre-trained model is fine-tuned with new data to adapt to a specific task or domain. The video provides an example of how a pre-trained model for power line classification was fine-tuned to extract railway asset measurements, demonstrating the flexibility and efficiency of this approach.

💡3D Point Clouds

3D point clouds are datasets representing the three-dimensional structure of physical objects or space. They are used in various applications, such as building classification and feature extraction. The video mentions the use of point cloud data for tasks like railway asset measurement and building footprint extraction, showcasing the versatility of geospatial AI in handling different types of geospatial data.

💡Natural Language Processing (NLP)

NLP is a subfield of AI that deals with the interaction between computers and human language. In the video, NLP is used to extract geospatial information from unstructured text reports, such as those generated by field inspections, and bring this data into a GIS system for further analysis and visualization.

💡Deep Learning for Time Series

Deep learning for time series involves applying deep learning techniques to analyze and forecast time-dependent data. The video introduces the addition of a time series network to ArcGIS Learn, which leverages deep learning for time series forecasting, a novel application in the field of geospatial AI.

💡Full Motion Video (FMV)

FMV refers to video data that captures the full range of motion in a scene, which can be analyzed for various applications, such as object tracking. The video demonstrates how FMV can be used in conjunction with deep learning models to track objects like vehicles, providing real-time data for applications like traffic analysis.

💡Line of Sight Analysis

Line of sight analysis is a technique used to determine the visibility between two points, often used in urban planning and telecommunications. In the video, this concept is illustrated through the use of deep learning models to detect and analyze the line of sight from windows to a specific target, showing how AI can enhance traditional GIS analysis.

Highlights

Geospatial AI applies spatial machine learning and deep learning techniques to solve complex problems and derive insights.

Geospatial AI expands the power of GIS to support human-in-the-loop systems and fully autonomous systems.

RTS is an end-to-end geospatial AI system that includes data preparation, model training, QA, validation, and inferencing.

ArcGIS Learn is a Python API module that enables easy training of geospatial deep learning models on your data.

ArcGIS Learn supports various data types including oriented and overhead imagery, 3D point clouds, feature tabular, and time series data.

ArcGIS Learn offers pre-trained models for immediate deployment into production, with options for transfer learning and customization.

ArcGIS Learn's txt sub-module includes natural language processing capabilities for extracting geospatial information from unstructured text.

The ML Model feature allows easy integration of ArcGIS with classification, regression, or clustering models from the scikit-learn library.

ArcGIS Learn's Time Series Network brings deep learning to time series forecasting for geospatial data.

Geospatial AI can process mobile point clouds and categorize buildings, trees, and ground points within ArcGIS Pro.

Public Authority for Civil Infrastructure in Kuwait uses ArcGIS Learn to update base maps efficiently for the 2035 country vision.

Pre-trained geospatial deep learning models on Living Atlas make deep learning more accessible for users without extensive training data or resources.

S3 India fine-tuned pre-trained models to extract railway assets and their precise dimensions from point cloud data.

ArcGIS Pro's deep learning tools and image analysts have been extended to work with multi-spectral imagery and full motion video.

Object tracking in ArcGIS Pro can automate workflows, such as tracking vehicles and saving data to a geodatabase.

Deep learning tools for point cloud classification are being integrated into ArcGIS Pro's 3D Analyst extension for enhanced analysis capabilities.

AI-infused apps and solutions, like the 3D base map solution, use deep learning models to extract features like trees from lidar data.

Johns Creek, Georgia, developed a smartphone application using ArcGIS Learn's deep learning capabilities for tree inventory mapping and classification.

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