Geospatial AI, An Overview

Geospatial AI is a field that integrates spatial machine learning and deep learning to solve complex problems and gain insights. It enhances GIS capabilities, supporting both human-in-the-loop systems and fully autonomous systems. The core of geospatial AI is the ArcGIS Learn module within the Python API, which facilitates end-to-end geospatial AI processes from data access to inference in production environments. It includes pre-trained models for immediate deployment and the option for transfer learning with custom data. Users can also leverage AI-powered tools within ArcGIS for geoprocessing and build ready-to-use apps without being aware of the underlying AI. The script highlights advancements in geospatial deep learning models for various tasks, including object classification, edge detection, and time series forecasting. Additionally, it introduces the ArcGIS Learn TXT submodule for natural language processing to extract geospatial information from unstructured text, making geospatial AI more accessible and versatile.

Takeaways

• 📈 **Geospatial AI Definition**: Geospatial AI applies spatial machine learning and deep learning techniques to solve complex problems and derive insights in innovative ways.
• 🌐 **Human in the Loop**: The system supports two types of systems - human in the loop, where human expertise is combined with AI augmentation, and fully autonomous systems.
• 🔍 **End-to-End System**: The geospatial AI system is end-to-end, from data access and preparation to inference and decision-making in production environments.
• 🛠️ **ArcGIS Module**: ArcGIS Learn module within the Python API is the core of geospatial AI, allowing for training of various geospatial deep learning models.
• 📚 **Pre-Trained Models**: Pre-trained models are available for deployment into production, which have been validated using curated data and significant compute resources.
• 🔄 **Transfer Learning**: Users can perform transfer learning by bringing in their own data for localization and then validate and deploy it to production.
• 🧩 **Ready-to-Use Tools**: Models are integrated as ready-to-use tools within ArcGIS, enhancing familiar analytic tools with AI capabilities.
• 🏙️ **Feature Extraction**: The system supports models for various feature extraction tasks from imagery, including object classification, detection, pixel classification, edge detection, and more.
• 📊 **Time Series Network**: A new addition to ArcGIS Learn, this model applies deep learning to time series forecasting, enhancing capabilities beyond traditional machine learning models.
• 📝 **Natural Language Processing**: The sub-module arcgis.learn.txt focuses on NLP, allowing for extraction of geospatial information from unstructured text and improving address data.
• 🌟 **Multilingual Support**: The NLP models are available in English and several other languages, expanding the system's usability globally.

Q & A

What is the core component of Geospatial AI?

The core component of Geospatial AI is the ArcGIS Learn module within the Python API, which allows for training a wide variety of geospatial deep learning models on your data.

How does Geospatial AI enhance GIS capabilities?

Geospatial AI enhances GIS capabilities by applying spatial machine learning and deep learning techniques to solve complex problems and derive deeper insights in innovative ways, thus expanding the power of GIS.

What are the two kinds of systems that Geospatial AI supports?

Geospatial AI supports two kinds of systems: 'human in the loop' systems that combine human expertise with AI augmentation, and fully autonomous systems.

What is an end-to-end geospatial AI system?

An end-to-end geospatial AI system refers to a comprehensive system that covers everything from data access and preparation for labeling, through model training, QA, validation, to inferencing at scale in production environments, and decision-making based on inference outcomes.

What types of data does the ArcGIS Learn module support for geospatial deep learning models?

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

How does the addition of the time series network to ArcGIS Learn impact time series forecasting?

The time series network brings the power of deep learning and convolutional neural networks to time series forecasting, potentially providing superior results compared to traditional machine learning models when sufficient training data is available.

What is the purpose of the 'arcgis.learn.txt' submodule?

The 'arcgis.learn.txt' submodule is dedicated to natural language processing tasks, allowing for the extraction of geospatial information from unstructured text-based reports and enabling tasks like entity recognition, address correction, and standardization for geocoding.

How does the fully connected network enhance the geoprocessing tools in ArcGIS Pro?

The fully connected network allows for the integration of data from future layers directly into a deep neural network, which can yield superior results compared to most machine learning models, given enough training data.

What is the role of the ML (Machine Learning) model in the integration of ArcGIS with other classification, regression, or clustering models?

The ML model facilitates the easy integration of ArcGIS with any classification, regression, or clustering model from the popular scikit-learn library, enhancing the analytical capabilities of ArcGIS.

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

Pre-trained models save significant time and computational resources as they have already been developed and validated using curated data. They can be readily deployed into production or used as a starting point for transfer learning with custom data.

How can Geospatial AI be experienced from a user perspective?

From a user perspective, Geospatial AI can be experienced through the core ArcGIS Learn module, pre-trained models for immediate deployment, and as ready-to-use tools within ArcGIS that are integrated into familiar analytic workflows.

What is the significance of the introduction of users, developers, and data scientists in the context of Geospatial AI?

The introduction of users, developers, and data scientists provides real-world examples and insights into how Geospatial AI is being applied to solve real-world problems across different cities and use cases, showcasing its practical applications and benefits.

Outlines

00:00

🌐 Geospatial AI: Expanding GIS Capabilities

This paragraph introduces the concept of geospatial AI, which applies spatial machine learning and deep learning techniques to solve complex problems and derive insights. It emphasizes the expansion of GIS's power to support two types of systems: 'human in the loop' for combining human expertise with AI augmentation, and fully autonomous systems. The paragraph outlines an end-to-end geospatial AI system, from data access and preparation to model training, quality assurance, validation, and inference at scale in production environments. It also discusses the user experience of geospatial AI through different interfaces, such as ArcGIS Learn within the Python API, pre-trained models for immediate deployment, transfer learning for localization, and ready-to-use tools within ArcGIS. The paragraph concludes with an introduction to real-world applications and a tour of geospatial AI by Rohit, highlighting its use in various cities.

05:02

📈 ArcGIS Learn: Integrating Deep Learning with Geospatial Data

This paragraph delves into the capabilities of the ArcGIS Learn module, part of the ArcGIS API for Python, which facilitates the training of various geospatial deep learning models using user data. It covers the module's support for different types of data, including oriented and overhead imagery, 3D point clouds, feature tables, tabular data, and time series data, as well as unstructured text. The paragraph highlights the module's ability to integrate with popular machine learning libraries like scikit-learn and its new time series network for deep learning in time series forecasting. It also addresses the challenge of unstructured geospatial data, such as text-based reports, and introduces the new submodule arcgis.learn.txt for natural language processing tasks. The paragraph concludes by emphasizing the ease of using these models without the need for individual training, thanks to the availability of open-source NLP models in multiple languages.

Keywords

Geospatial AI

Geospatial AI refers to the application of spatial machine learning and deep learning techniques to solve complex problems and derive insights in innovative ways. It enhances the capabilities of Geographic Information Systems (GIS) to support both human-in-the-loop systems, where human expertise is combined with AI augmentation, and fully autonomous systems. In the video, geospatial AI is central to the discussion, as it enables the development of end-to-end systems for data access, labeling, model training, quality assurance, validation, and inference at scale in production environments.

ArcGIS

ArcGIS is a module within the Python API that serves as the core for geospatial AI applications. It allows users to train a wide variety of geospatial deep learning models on their data, integrate with popular machine learning libraries, and supports various data types including oriented and overhead imagery, 3D point clouds, feature, tabular, and time series data. The script mentions that ArcGIS Learn is a powerful tool for developers and users to take AI to the field and solve real-world problems.

Deep Learning Models

Deep learning models are a subset of machine learning algorithms inspired by the structure and function of the human brain. They are used in geospatial AI to process and analyze complex data types. The script discusses the support for over 30 geospatial deep learning models that can be trained on user data, covering tasks such as object classification, object detection, pixel classification, instance segmentation, and more.

Human-in-the-Loop

Human-in-the-loop is a concept where human intelligence and expertise are integrated with AI systems to enhance decision-making and problem-solving. In the context of the video, it refers to systems that combine human judgement with AI augmentation, allowing for more nuanced and accurate outcomes in geospatial analysis.

End-to-End System

An end-to-end system in the script refers to a comprehensive geospatial AI solution that covers the entire process from data access and preparation to labeling, model training, quality assurance, validation, and inference in production environments. It implies a seamless workflow where each stage of the process is integrated within the system for efficient and scalable operations.

Pre-trained Models

Pre-trained models are machine learning models that have already been trained on large datasets and can be readily deployed for specific tasks. In the video, it is mentioned that there are pre-trained models available for deployment into production, which saves users the time and resources needed to train models from scratch. These models can also be further customized through transfer learning for more personalized use cases.

Transfer Learning

Transfer learning is a technique in machine learning where a model trained on one task is reused as the starting point for a model on a second task. It is highlighted in the script as a method for users to bring in their own data and adapt pre-trained models to their specific needs, akin to a localization process before deploying them into production.

Analytic Tools

Analytic tools in the context of the video refer to the ready-to-use tools within ArcGIS that perform geospatial data analysis. These tools are enhanced with AI capabilities, although the AI aspect may be invisible to the end user. The script emphasizes that these tools are integrated into familiar workflows, making them more powerful without requiring the user to understand the underlying AI mechanisms.

Natural Language Processing (NLP)

Natural Language Processing is a field of AI that focuses on the interaction between computers and human languages. In the video, NLP is used through the ArcGIS Learn module to extract geospatial information from unstructured text, such as reports, and to perform tasks like entity recognition, address correction, and standardization, which are essential for geocoding.

Entity Recognizer Model

The entity recognizer model is an NLP model capable of identifying and classifying entities in text. In the script, it is used to extract geospatial information from text-based reports, allowing users to bring this information into a map for further analysis, thus enhancing the utility of unstructured data in geospatial AI applications.

Time Series Data

Time series data is a type of data that is collected at regular time intervals. It is used in various fields to analyze trends and patterns over time. The video discusses the addition of the time series network to ArcGIS Learn, which applies deep learning to time series forecasting, a significant advancement for analyzing and predicting trends in geospatial data.

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.

An end-to-end geospatial AI system includes data access, labeling, model training, QA, validation, and inference at scale.

ArcGIS Learn is a Python API module that simplifies training various geospatial deep learning models.

Pre-trained models are available for deployment into production environments.

Transfer learning allows for localization and validation of models using your own data.

ArcGIS includes ready-to-use tools and analytic capabilities that leverage AI without user awareness.

Geospatial AI can be used in real projects to solve real-world problems.

ArcGIS Learn supports over 30 geospatial deep learning models for various feature extraction tasks.

New models for edge detection and road extraction have been added for more accurate feature extraction.

The Time Series Network brings deep learning to time series forecasting in ArcGIS Learn.

ArcGIS Learn's sub-module for natural language processing can extract geospatial information from unstructured text.

Entity Recognizer model can extract geospatial entities and map them accurately.

ArcGIS Learn includes models for correcting and normalizing addresses for geocoding.

Open source NLP models are available for various tasks in multiple languages.

ArcGIS Learn supports integration with popular machine learning libraries like scikit-learn.

The fully connected network allows superior results in machine learning models with enough training data.

ArcGIS Learn is a powerful tool for developers and data scientists to innovate with geospatial AI.