Geospatial AI, An Overview

Esri Events
7 Apr 202107:24

TLDRGeospatial AI is revolutionizing problem-solving by applying machine learning and deep learning techniques to spatial data. It enhances GIS capabilities, supporting both human-in-the-loop systems and fully autonomous AI systems. The core of this technology is the ArcGIS Learn module, which simplifies the training of various deep learning models on spatial data. With pre-trained models and the ability to incorporate custom data, users can deploy AI for tasks like object classification, change detection, and image translation. The release also introduces natural language processing capabilities, allowing for the extraction of geospatial information from unstructured text and improving geocoding accuracy. ArcGIS Learn's versatility and accessibility are set to empower developers and data scientists to tackle real-world challenges more effectively.

Takeaways

  • 🌐 Geospatial AI combines spatial machine learning and deep learning to solve complex problems and gain insights.
  • 🤖 It enhances GIS capabilities to support both human-in-the-loop systems and fully autonomous systems.
  • 🔍 The core of Geospatial AI is the ArcGIS Learn module within the Python API, facilitating end-to-end AI processes.
  • 📊 ArcGIS Learn supports various data types, including imagery, 3D point clouds, feature data, and time series data.
  • 🏙️ The module offers over 30 geospatial deep learning models for tasks like object classification, detection, and segmentation.
  • 🚀 New models have been added for edge detection, road extraction, change detection, and image translation.
  • 📈 The Time Series Network model brings deep learning to time series forecasting within ArcGIS Learn.
  • 📝 ArcGIS Learn now includes a sub-module for natural language processing, allowing extraction of geospatial information from text reports.
  • 🌐 The tech sub-module enables the use of open-source NLP models for tasks like geocoding and address normalization.
  • 🌍 The script highlights real-world applications of Geospatial AI in various cities, showcasing its practical use cases.
  • 🔥 The excitement around the potential of ArcGIS Learn and its supported models is evident, encouraging developers to explore and innovate.

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 capabilities of GIS to support human-in-the-loop systems and fully autonomous systems.

  • How does Geospatial AI enhance GIS?

    -Geospatial AI enhances GIS by integrating AI to assist humans with their expertise and by building fully autonomous systems, allowing for more efficient and powerful analysis and decision-making in geospatial contexts.

  • What is an end-to-end Geospatial AI system?

    -An end-to-end Geospatial AI system, like RTS, encompasses the entire process from data access and preparation, through model training, QA, validation, to inferencing at scale in production environments, and decision-making based on inferencing outcomes.

  • What is the core component of Geospatial AI in the ArcGIS API?

    -The core component is the ArcGIS Learn module within the Python API, which simplifies the training of various geospatial deep learning models on user data.

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

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

  • What are some applications of the deep learning models supported by ArcGIS Learn?

    -Applications include object classification, object detection, pixel classification, instance segmentation, edge detection, road extraction, change detection, and image translation, including image captioning.

  • How can users deploy pre-trained models in production?

    -Users can deploy pre-trained models directly into production after validating them for their specific cases, or they can perform transfer learning with their own data and then validate and deploy the models.

  • What is the role of the ML model in integrating ArcGIS with machine learning?

    -The ML model allows for easy integration of ArcGIS with classification, regression, or clustering models from the scikit-learn library, enhancing the capabilities of ArcGIS with machine learning algorithms.

  • What new feature has been added to ArcGIS Learn for time series data?

    -The time series network has been added to ArcGIS Learn, bringing the power of deep learning and convolutional neural networks to time series forecasting.

  • How does the new sub-module in ArcGIS Learn facilitate natural language processing?

    -The new sub-module, arcgis.learn.txt, is dedicated to natural language processing, allowing users to extract geospatial information from unstructured text reports and bring them to a map, as well as correct and standardize addresses for geocoding.

Outlines

00:00

🌐 Introduction to Geospatial AI

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 discusses the expansion of GIS capabilities and the two types of systems supported: human-in-the-loop, where human expertise is augmented by AI, 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 at scale in production environments. The user experience of geospatial AI is described, starting with the core ArcGIS Learn module within the Python API, pre-trained models for deployment, transfer learning, ready-to-use tools within ArcGIS, and the integration of AI in ready-to-use apps and solutions.

05:02

📚 ArcGIS Learn and AI Models

The second paragraph delves into the details of the ArcGIS Learn module, which is part of the Python API and enables the training of various geospatial deep learning models on user data. It mentions the support for different types of data and tasks, such as object classification, detection, pixel classification, instance segmentation, edge detection, road extraction, change detection, and image translation. The paragraph also highlights the addition of models for 3D data processing and the integration of these models into ArcGIS Pro. It discusses the ML model for integrating ArcGIS with classification, regression, or clustering models from the scikit-learn library, the time series network for forecasting, and the new sub-module for natural language processing, which includes entity recognition and address processing models.

Mindmap

Keywords

💡Geospatial AI

Geospatial AI refers to the application of spatial machine learning and deep learning techniques to analyze and interpret geographical data. It helps in solving complex problems by deriving deeper insights. In the context of the video, geospatial AI is used to enhance the capabilities of GIS (Geographic Information Systems) to support both human-in-the-loop systems, where human expertise is augmented by AI, and fully autonomous systems.

💡Human-in-the-loop

This concept refers to systems where human involvement is an integral part of the process, alongside AI. It ensures that the expertise of humans is combined with the computational power of AI to make better decisions. In the video, this is illustrated by the use of geospatial AI to assist humans in tasks such as data analysis and decision-making.

💡ArcGIS Learn

ArcGIS Learn is a module within the Python API for ArcGIS, which simplifies the process of training geospatial deep learning models. It allows users to import data from ArcGIS, select from various supported models, train the model, and then use it for different tasks without the need for GIS expertise.

💡Deep Learning Models

Deep learning models are a subset of machine learning algorithms that are designed to simulate the behavior of the human brain—neural networks with many layers (hence 'deep'). These models are particularly effective for tasks like image and speech recognition, natural language processing, and more. In the video, deep learning models are used for feature extraction from geospatial data.

💡Pre-trained Models

Pre-trained models are machine learning models that have already been trained on a large dataset and can be used directly or with minimal adjustments for specific tasks. They save time and resources by avoiding the need to start training from scratch. In the video, pre-trained models are available for deployment into production environments.

💡Transfer Learning

Transfer learning is a method where a pre-trained model is adapted to a new, but related, problem. It involves fine-tuning the model on a new dataset to improve its performance on the specific task. This approach leverages the knowledge from the original task to accelerate learning and improve results.

💡Geoprocessing

Geoprocessing refers to the analysis and manipulation of geographic data. It involves tasks such as data transformation, analysis, and modeling. In the context of the video, geoprocessing tools within ArcGIS are enhanced by the integration of AI, providing superior results compared to traditional machine learning models.

💡Time Series Forecasting

Time series forecasting is the process of predicting future values based on historical data points collected over time. It's a statistical technique widely used in various fields, including finance, weather forecasting, and now, geospatial analysis with the integration of deep learning models.

💡Natural Language Processing (NLP)

NLP is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human (natural) languages. It involves tasks like sentiment analysis, machine translation, and information extraction. In the video, NLP is used to extract geospatial information from unstructured text data.

💡Geocoding

Geocoding is the process of converting addresses or other location references into geographic coordinates, such as latitude and longitude. It's a crucial step in mapping and spatial data analysis. The video discusses models that can automatically correct and standardize addresses for geocoding, improving the accuracy of location data.

Highlights

Geospatial AI applies spatial machine learning and deep learning techniques.

It helps solve complex problems and derive deeper insights.

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

RTS is an end-to-end geospatial AI system for data labeling, model training, QA, validation, and inferencing at scale.

ArcGIS Learn is a module within the Python API for geospatial AI.

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

The module handles oriented and overhead imagery, 3D point clouds, feature tabular, and time series data.

ArcGIS Learn includes models for object classification, object detection, pixel classification, and instance segmentation.

New models for edge detection, road extraction, change detection, and image translation have been added.

The Point CNN point cloud classification model processes mobile point clouds and categorizes buildings, trees, and ground points.

ArcGIS Learn's machine learning model integrates ArcGIS with classification, regression, or clustering models from scikit-learn.

The time series network brings deep learning to time series forecasting in ArcGIS Learn.

ArcGIS Learn.txt is a sub-module for natural language processing to extract geospatial information from unstructured text.

The entity recognizer model extracts information from reports and brings it to a map.

Models can determine countries from incomplete addresses and correct spelling mistakes for geocoding.

ArcGIS Learn supports English and several other languages for NLP tasks.

ArcGIS Learn is a powerful tool for developers and users to apply AI to real-world geospatial problems.

Turn Your Ideas to Images for Free

Generate professional visuals with text or images in seconds.

Turn Your Ideas to Images for Free Try It Free