5 ways to use Generative AI in Geospatial

CARTO
22 Feb 202453:23

TLDRMatt Forrest discusses the integration of generative AI within geospatial analytics, highlighting five practical applications from code generation to custom model development. He emphasizes the importance of human oversight and the potential of AI to democratize geospatial analysis, while cautioning about the risks of relying solely on AI without proper data curation and understanding.

Takeaways

  • 🚀 Generative AI is being increasingly applied within geospatial analytics, offering new ways to interact with and analyze spatial data.
  • 🔍 Matt Forrest discusses five levels of using generative AI in geospatial analytics, ranging from code generation to custom model development.
  • 📝 The first level involves using AI to assist with human-driven processes, such as writing code more efficiently.
  • 🏷️ Feature labeling uses large language models to generate human-readable labels for geospatial data, improving understanding for non-experts.
  • 🔍 Retrieving data involves using text inputs to find and understand datasets, which can be facilitated by AI tools.
  • 🗺️ Interactivity with maps allows users to manipulate applications or data using text inputs, making geospatial analysis more accessible.
  • 🛠️ Building custom models is the most advanced level, where AI models are specifically designed for geospatial tasks, though it's still in early stages.
  • 📈 Large language models like GPT-3.5 have been trained on vast amounts of data, including geospatial information, enabling them to generate relevant text outputs.
  • 🤖 AI models are not perfect and can produce inaccurate or 'hallucinated' data, so human oversight is crucial, especially at higher levels of interaction.
  • 🌐 The potential of AI in geospatial analytics is vast, but it's important to consider the risks and ensure data quality and relevance.
  • 🔗 The future of geospatial analytics with AI looks promising, with the potential to democratize access to complex geospatial tools and analyses.

Q & A

  • What is the main focus of the presentation by Matt Forrest?

    -The main focus of the presentation is on the practical applications of generative AI within the context of geospatial analytics.

  • What are the five components that can be used with generative AI in geospatial analytics?

    -The five components are: 1) Code generation to assist human-driven processes, 2) Feature labeling using large language models, 3) Retrieving data using text input, 4) Interactivity with maps or dashboards, and 5) Building custom models specifically for geospatial analytics.

  • How does generative AI assist in improving code writing?

    -Generative AI can assist in code writing by generating code snippets, checking code, and acting as a reference for documentation, making the process faster and more efficient.

  • What is the role of prompt engineering in generative AI?

    -Prompt engineering involves structuring the input data to get a specific response from the AI model. It is crucial for generating human-readable labels and descriptions for geospatial data.

  • How can generative AI be used for data retrieval in geospatial analytics?

    -Generative AI can be used for data retrieval by using text input to find and retrieve specific datasets, making it easier to access and analyze geospatial information.

  • What are the potential risks associated with using generative AI in geospatial analytics?

    -The potential risks include inaccuracies in the AI-generated outputs, the need for human oversight to ensure correct interpretation of data, and the challenge of managing the complexity of AI-generated solutions.

  • What is the IBM NASA model mentioned in the presentation?

    -The IBM NASA model is a generative AI model that uses harmonized Landsat and Sentinel 2 data to create a labeling system for satellite imagery, enabling the identification of features like fire scars, floods, and built areas.

  • How can generative AI be used for data discovery in geospatial analytics?

    -Generative AI can be used for data discovery by allowing users to input text-based questions to find relevant datasets, making it easier to locate and access geospatial data for analysis.

  • What is the importance of metadata in ensuring the accuracy of AI-generated results in geospatial analytics?

    -Metadata is crucial for understanding the context and content of the data, which helps in ensuring the accuracy of AI-generated results. It provides the necessary information about the data sources and their relationships, which is essential for accurate geospatial analysis.

  • What is the future outlook for generative AI in geospatial analytics?

    -The future outlook for generative AI in geospatial analytics is promising, with the potential to democratize access to complex geospatial analysis tools, improve efficiency in data preparation and querying, and enable more specific and localized problem-solving.

Outlines

00:00

📝 Introduction to Generative AI in Geospatial Analytics

Matt Forrest introduces the topic of using generative AI within geospatial analytics. He mentions tracking developments in this field and acknowledges the rapid changes occurring. Matt emphasizes the practicality of the topic, noting that much of the existing literature is theoretical. He plans to share current developments and practical applications, focusing on generative AI's ability to create text or images and its relevance to geospatial analytics.

05:01

🔍 Five Levels of Generative AI Application

Matt outlines five levels of generative AI application in geospatial analytics. These include code generation to assist human-driven processes, feature labeling using large language models, data retrieval through text input, interactivity with maps using text input, and building custom models specific to geospatial data. He emphasizes the importance of understanding the evolving nature of AI and its applications in geospatial analytics.

10:02

📈 Progressing Through AI Application Levels

Matt discusses the progression of AI models, particularly large language models like GPT, and their increasing capabilities. He highlights the importance of recognizing the imperfections in AI models and the potential for inaccuracies or 'hallucinations.' Matt also touches on the early phase of some AI applications and the need to understand how they are used once released to the public.

15:03

💡 Prompt Engineering and Data Labeling

Matt explains the concept of prompt engineering, which involves structuring input data to receive a specific response from AI models. He provides examples of using generative AI for labeling geospatial data, making it more understandable for non-experts. Matt also discusses the importance of crafting clear prompts and the potential risks associated with text generation in AI models.

20:05

🔎 Using Text for Data Retrieval

Matt explores the use of text input to retrieve and analyze geospatial data, particularly using OpenStreetMap data. He discusses the complexity of querying nested data structures and how AI can simplify this process. Matt also mentions the potential for AI to assist in data discovery and the importance of having clear prompts for accurate results.

25:06

🗺️ Interacting with Maps and Dashboards

Matt discusses the potential of generative AI to interact with maps and dashboards, changing the way users interact with geospatial data. He suggests that AI could serve as a new user interface, allowing users to input text queries and receive spatial analysis results. Matt also highlights the risks associated with this level of AI interaction, including the need for accurate data interpretation and the potential for misunderstandings.

30:08

🚀 Custom Models for Geospatial Analytics

Matt talks about the development of custom generative AI models specifically for geospatial analytics. He mentions the IBM NASA model, which uses satellite imagery for labeling and analysis, and the University of South Carolina's autonomous GIS model, which can generate code and analysis from text prompts. Matt emphasizes the potential of these models to revolutionize geospatial analysis and make it more accessible.

35:10

🤖 The Future of GeoAI and Human Involvement

Matt reflects on the future of geospatial AI (GeoAI) and the role of human involvement. He discusses the importance of human oversight in AI processes, especially when dealing with complex geospatial data. Matt also touches on the potential risks of relying too heavily on AI and the need for a balance between AI automation and human expertise.

40:11

📋 Addressing Accuracy and Data Quality

Matt addresses concerns about the accuracy of AI-generated results and the importance of data quality. He emphasizes the need for well-curated and labeled data to ensure reliable AI outputs. Matt also discusses the role of metadata in understanding data and the potential for AI to assist in data discovery and preparation.

45:12

🤔 Final Thoughts and Q&A

Matt concludes the discussion by inviting questions and offering his contact information for further inquiries. He reiterates the exciting potential of generative AI in geospatial analytics and expresses his curiosity about the future developments in this field. Matt also mentions the availability of a recording for those who may have missed the live session.

Mindmap

Keywords

💡Generative AI

Generative AI refers to artificial intelligence systems capable of creating new content, such as text or images. In the context of the video, it is used to enhance geospatial analytics by automating tasks, generating human-readable labels, and interacting with maps and data. The video discusses how generative AI can be applied at various levels of complexity, from code generation to creating custom models for geospatial analysis.

💡Geospatial Analytics

Geospatial analytics involves the use of geographic information systems (GIS) and other tools to analyze and interpret spatial data. The video emphasizes how generative AI can be integrated into geospatial analytics to improve efficiency, understanding, and interaction with geospatial data. Examples include feature labeling, data retrieval, and map interactivity.

💡Large Language Models (LLMs)

LLMs are AI models trained on vast amounts of text data, capable of generating human-like text. The video mentions GPT-3.5 as an example of an LLM that can be utilized for tasks such as code generation and creating human-readable labels for geospatial data. LLMs are a key component in the generative AI applications discussed.

💡Code Generation

Code generation is the process of creating computer code automatically, often to assist with human-driven processes. In the video, it is highlighted as a low-risk application of generative AI, where AI can help write or optimize code for geospatial tasks, improving efficiency and reducing the time spent on coding.

💡Feature Labeling

Feature labeling involves assigning human-readable labels to geospatial data features. The video discusses how generative AI can be used to design prompts for LLMs to generate such labels, making geospatial data more accessible and understandable to a broader audience.

💡Data Retrieval

Data retrieval in the context of the video refers to using text inputs to access and bring data back onto a map or into a geospatial dataset. This application of generative AI allows for more intuitive querying and can simplify the process of finding specific data sets within complex geospatial databases.

💡Map Interactivity

Map interactivity involves using text inputs to manipulate geospatial applications or data, such as filtering or analyzing data on a map. The video suggests that generative AI can enhance map interactivity by allowing users to interact with maps in a more natural, text-based manner, similar to how people use search engines.

💡Custom Models

Custom models in the video refer to AI models specifically designed for geospatial tasks. These models are trained on geospatial data and can perform specialized tasks, such as satellite imagery analysis or addressing data cleaning. The video mentions the IBM NASA model and the University of South Carolina's autonomous GIS model as examples of custom models.

💡Risk Management

Risk management in the context of generative AI involves understanding and mitigating the potential inaccuracies or 'hallucinations' that AI models may produce. The video emphasizes the importance of human oversight and the need for accurate data labeling to ensure the reliability of AI-generated outputs in geospatial analytics.

💡Prompt Engineering

Prompt engineering is the process of structuring inputs to AI models to elicit specific and desired responses. The video discusses the importance of crafting clear and effective prompts to guide AI models in generating accurate and useful outputs for geospatial analysis, such as generating SQL queries or interpreting satellite imagery.

Highlights

Generative AI's application in geospatial analytics is being explored, with developments rapidly changing the field.

There are five components where generative AI can be used with geospatial analytics, starting from low-risk code generation to high-risk custom model creation.

Generative AI can assist in writing code faster, improving human-driven processes, and serving as a reference for documentation.

Large language models can generate human-readable labels for geospatial data, making it more accessible to non-experts.

Text input can be used to retrieve data and put it back on the map, simplifying data interaction.

Generative AI can be used to interact with maps or dashboards, potentially changing how users interact with geospatial applications.

Custom generative AI models for geospatial analytics are in early stages but show promise for specific applications like satellite imagery labeling.

The IBM NASA model uses harmonized Landsat and Sentinel 2 data to create a labeling system for satellite imagery.

The University of South Carolina's autonomous GIS model can generate a solution graph, code, and an executable program from a text prompt.

Generative AI models are trained on vast amounts of data, but their accuracy depends on the quality and relevance of the training data.

Human input is crucial for refining AI models, especially for localized and specific geospatial applications.

The risk associated with using generative AI increases with the level of automation and reliance on the AI model for decision-making.

Metadata and data curation are becoming increasingly important as AI models rely on well-labeled and structured data.

The future of geospatial analysts may involve working alongside AI to enhance geospatial analysis and decision-making.

Generative AI has the potential to democratize access to geospatial analysis, making it more accessible to a wider audience.

The integration of AI with geospatial tools is still in its infancy, with much research and development needed to fully realize its potential.

The accuracy of AI-generated results can be assessed by comparing them with known data and human expertise.

The speaker, Matt Forrest, encourages further exploration and discussion on the integration of generative AI in geospatial analytics.

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