Geospatial AI, An Overview
TLDRGeospatial AI is a powerful tool that applies spatial machine learning and deep learning techniques to solve complex problems and derive deeper 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 allows for the training of various geospatial deep learning models on your data. It supports a wide range of data types and has pre-trained models for immediate deployment. Additionally, it includes features for natural language processing to extract geospatial information from unstructured text. The technology is being used in real-world projects, showcasing its potential to solve real-world problems.
Takeaways
- 🌐 **Geospatial AI Definition**: Geospatial AI applies spatial machine learning and deep learning to solve complex problems and derive insights.
- 📈 **Expanding GIS Power**: It enhances GIS capabilities to support both 'human in the loop' systems and fully autonomous systems.
- 🔍 **End-to-End System**: The system covers data access, labeling, model training, QA, validation, and inference at scale in production.
- 🛠️ **ArcGIS Learn Module**: A core Python API module that simplifies the training of various geospatial deep learning models on user data.
- 🚀 **Pre-Trained Models**: Offers ready-to-deploy models that have been extensively computed and validated with curated data.
- 🔧 **Customization and Transfer Learning**: Allows for the incorporation of user-specific data for localized model training and validation.
- 📚 **AI as Analytic Tools**: Integrates AI models as tools within familiar ArcGIS environments, enhancing geoprocessing capabilities.
- 🏙️ **Real-World Applications**: Showcases how developers and users apply AI to solve real-world problems in various global cities.
- 📊 **Support for Diverse Data Types**: Handles oriented and overhead imagery, 3D point clouds, feature tabular, time series data, and unstructured text.
- ⏱️ **Time Series Network**: A new addition for deep learning in time series forecasting, enhancing predictions with convolutional neural networks.
- 📝 **Natural Language Processing**: Includes submodules for extracting geospatial information from unstructured text and improving address data quality.
Q & A
What is geospatial AI?
-Geospatial AI applies spatial machine learning and deep learning techniques to solve complex problems and derive deeper insights in innovative ways, expanding the power of GIS to support both human-in-the-loop systems and fully autonomous systems.
What does an end-to-end geospatial AI system entail?
-An end-to-end geospatial AI system includes data access and preparation, labeling, model training, quality assurance, validation, inference at scale in production environments, and decision-making based on inference outcomes, all within a single system.
How can users experience geospatial AI?
-Users can experience geospatial AI through core modules like ArcGIS Learn within the Python API, pre-trained models for immediate deployment, transfer learning with custom data, and ready-to-use tools within ArcGIS for familiar analytic processes.
What types of data does the ArcGIS Learn module support for geospatial deep learning models?
-The ArcGIS Learn module supports data from ArcGIS, oriented and overhead imagery, 3D point clouds, feature tabular, time series data, and even unstructured text.
What are some of the feature extraction tasks that the geospatial deep learning models can perform on imagery?
-The models can perform tasks such as object classification, object detection, pixel classification, instance segmentation, edge detection for extracting residential and agricultural parcels, road extraction, change detection, and image translation including image captioning.
How does the fully connected network complement the geoprocessing tools in ArcGIS Pro?
-The fully connected network allows data from feature layers to be fed directly into a deep neural network, providing superior results compared to most machine learning models, given sufficient training data.
What is the ML model in the context of ArcGIS?
-The ML model is a machine learning model that enables easy integration of ArcGIS with any classification, regression, or clustering model from the popular scikit-learn library.
What is the significance of the time series network added to ArcGIS Learn?
-The time series network brings the power of deep learning and convolutional neural networks to time series forecasting, enhancing the capabilities of handling geospatial data that is often unstructured.
How does the ArcGIS.learn.txt submodule assist with unstructured geospatial data?
-The ArcGIS.learn.txt submodule is designed for natural language processing, allowing users to extract geospatial information from text-based reports, determine countries from incomplete addresses, and automatically correct and normalize address spelling mistakes for geocoding.
What languages are supported by the open-source NLP models available in the tech submodule?
-The open-source NLP models available in the tech submodule are available for English and several other languages, enhancing the accessibility of geospatial AI for a broader user base.
What is the ultimate goal of introducing geospatial AI to real-world projects?
-The ultimate goal is to showcase the practical application of geospatial AI in solving real-world problems by introducing users, developers, and data scientists to its use in various projects across different cities worldwide.
How does the integration of AI in ready-to-use apps and solutions benefit the end-user without them noticing the AI component?
-The AI component is seamlessly integrated into the apps and solutions, making them more efficient and effective without the need for the end-user to understand the underlying AI technology. Users simply benefit from the improved functionality.
Outlines
🌐 Introduction to Geospatial AI
The video script introduces geospatial AI, which applies spatial machine learning and deep learning techniques to solve complex problems and derive insights. It discusses the expansion of GIS capabilities to support human-in-the-loop systems and fully autonomous systems. The system is described as end-to-end, from data access and preparation to model training, quality assurance, validation, and inferencing at scale in production environments. The user experience of geospatial AI is outlined, starting with the core ArcGIS Learn module within the Python API, which allows for training various geospatial deep learning models. Pre-trained models are available for deployment, and the option for transfer learning with user data is discussed. The integration of these models into ArcGIS as analytic tools and ready-to-use apps is also mentioned. The video aims to showcase the use of geospatial AI in real-world projects, with a focus on four cities where developers and users are applying AI to solve problems.
📚 ArcGIS Learn: Expanding AI Capabilities
The second paragraph delves into the capabilities of the ArcGIS Learn module, which is part of the ArcGIS API for Python. It allows for the integration of ArcGIS with various classification, regression, or clustering models from the scikit-learn library. The addition of the time series network to ArcGIS Learn is highlighted, which brings deep learning to time series forecasting. The paragraph also addresses the challenge of unstructured geospatial data, such as text-based reports, and how deep learning can be used to extract valuable information from them. The new submodule, arcgis.learn.txt, is introduced for natural language processing tasks, including entity recognition to extract geospatial information from reports, determining the country of incomplete addresses, and correcting spelling mistakes in addresses. The tech submodule is mentioned for using open-source NLP models in various tasks, with support for multiple languages. The paragraph concludes by emphasizing the potential of ArcGIS Learn and its support for numerous geospatial deep learning models.
Mindmap
Keywords
💡Geospatial AI
💡Human in the Loop
💡End-to-End Geospatial AI System
💡ArcGIS Learn Module
💡Pre-trained Models
💡Transfer Learning
💡Geoprocessing
💡Time Series Network
💡Natural Language Processing (NLP)
💡Entity Recognizer Model
💡Geocoding
Highlights
Geospatial AI applies spatial machine learning and deep learning techniques to solve complex problems and derive insights.
The power of GIS is expanded to support human-in-the-loop systems and fully autonomous systems.
An end-to-end geospatial AI system includes data access, preparation, labeling, model training, QA, validation, and inference at scale.
ArcGIS Learn is a Python API module that simplifies the training of various geospatial deep learning models.
Pre-trained models are available for immediate deployment into production environments.
Transfer learning allows for the localization process of pre-trained models with user-specific data.
ArcGIS Pro integrates with deep neural networks for superior results in feature extraction tasks.
The Time Series Network brings deep learning capabilities to time series forecasting.
ArcGIS Learn supports processing of oriented and overhead imagery, 3D point clouds, and unstructured text.
New models for edge detection, road extraction, and image translation have been added to the system.
The Point CNN model is used for 3D point cloud classification.
ArcGIS Learn's sub-module for natural language processing can extract geospatial information from text-based reports.
The Entity Recognizer model can accurately determine the country of incomplete addresses and correct spelling mistakes.
The tech sub-module allows the use of open-source NLP models for various tasks, available in multiple languages.
ArcGIS Learn supports dozens of geospatial deep learning models for a wide range of applications.
Geospatial AI is used in real projects to solve real-world problems, with examples from developers and data scientists.
The system is designed to be user-friendly, with AI capabilities often unseen by the end-user, providing better outcomes through improved functionality.
Geospatial AI is poised to revolutionize how geospatial data is processed and analyzed, enhancing decision-making capabilities.