Building GeoAI with Lego-Like Simplicity Through Visual Programming Building GeoAI with Lego-Like Simplicity Through Visual Programming
Editor’s note: Lingbo Liu, PhD is a speaker for ODSC East this April 23rd-25th. Be sure to check out his talk,... Building GeoAI with Lego-Like Simplicity Through Visual Programming

Editor’s note: Lingbo Liu, PhD is a speaker for ODSC East this April 23rd-25th. Be sure to check out his talk, “Workflow-based GeoAI Analysis with No/Low-Code Visual Programming,” there!

Hello, I’m Lingbo Liu, a Postdoctoral Research Fellow leading the Spatial Data Lab project at the Center for Geographic Analysis, Harvard University. Our project is driven by a singular vision: to democratize Geospatial AI, making it accessible and understandable to everyone, regardless of their background in data science. By leveraging innovative technologies and educational tools, we aim to break down the barriers that traditionally separate complex scientific disciplines, ensuring that the powerful combination of geospatial analysis and artificial intelligence (AI) can be harnessed by a broader audience. 

In the rapidly evolving world of data science, the fusion of geospatial analysis and artificial intelligence (AI) has opened up new frontiers of exploration and innovation. At the upcoming conference, we are thrilled to introduce a unique training session designed to demystify this intersection, dubbed GeoAI. Our goal is to make learning GeoAI as intuitive and engaging as building with Legos. This session will not only provide a solid understanding of GeoAI but also inspire participants to expand their skills and apply them creatively in various contexts.

Understanding GeoAI with Ease

Our training leverages the KNIME Analytics Platform (AP), a powerful yet user-friendly open-source visual programming environment. This platform makes it possible for data science novices and experts alike to embark on their GeoAI journey without the need for extensive programming expertise. The session begins with a comprehensive introduction to the KNIME AP, where participants will get acquainted with its basic features and intuitive interface, setting the stage for a deeper exploration into GeoAI.

Learning GeoAI, Block by Block

The core of our training session unfolds in four meticulously designed segments, each focusing on a critical aspect of GeoAI. We start with the basics of the visual programming platform, guiding participants through the KNIME AP’s environment, akin to the foundational block of a Lego structure. This segment ensures everyone is on the same page, providing the tools needed to build more complex GeoAI models.

Next, we delve into the advanced AI functionalities available within KNIME AP. This part of the session equips participants with the ‘blocks’ necessary to construct sophisticated AI models, including those based on machine learning, deep learning, and Explainable AI. It’s an opportunity to see the versatility of KNIME’s AI tools in action, offering a glimpse into the potential of GeoAI applications.

Expanding the GeoAI Landscape

Our journey through GeoAI further explores its application in geospatial analysis. Participants will learn how to manage spatial data and execute detailed spatial analyses within KNIME. This segment is akin to adding layers and textures to our growing Lego masterpiece, enriching the model with detailed geospatial insights.

The culmination of our session is a practical case demonstration, where the concepts learned are applied to build a GeoAI model. This hands-on experience reinforces the session’s teachings and showcases the real-world applicability of GeoAI, illustrating how the blocks come together to form a functional and insightful model.

Join Us and Transform Your GeoAI Skills

Scheduled for a concise yet impactful one-hour hands-off training, this session is designed to accommodate an unlimited number of participants. Our time will be efficiently distributed to cover all critical aspects of GeoAI, from an introduction to the KNIME platform to the exploration of AI functionalities and geospatial analysis, concluding with a stimulating case demonstration.

This training session is more than just a learning opportunity; it’s an invitation to become part of a community that’s pushing the boundaries of GeoAI. Whether you’re a novice eager to explore the field of data science or an expert looking to enhance your skills, this session promises to lay a solid foundation for your continued learning and application in GeoAI. Join us to experience the simplicity and power of building GeoAI, one block at a time, and let’s innovate together for a smarter, more connected world.

More links:

Project Website: Spatial Data Lab 

Conference Link: The Symposium on Spatiotemporal Data Science: GeoAI for Social Sciences

About the author:

Lingbo Liu, a postdoctoral fellow at Harvard’s Center for Geographic Analysis, specializes in merging Open Geospatial Artificial Intelligence (GeoAI) with visual programming to revolutionize urban health initiatives. With a profound background in health-focused city planning and over 730 citations for his scholarly work, Lingbo significantly influences urban public health policy and sustainable design. His pioneering use of open-source AI for urban analytics, coupled with his commitment to education through visual programming, embodies his dedication to making GeoAI accessible to all. This innovative approach to harnessing spatiotemporal big data is pivotal in fostering sustainable, health-oriented urban development and democratizing GeoAI knowledge.

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