| Workshop Information | |
|---|---|
| Dates | Sept. 4, 11, & 18 - 2025 |
| When | Thursdays 10 am Arizona Time |
| Where | Hybrid at Weaver Science-Engineering Library Room 212 & Zoom |
Worshop Registration [Feeback Form] Youtube Playlist
Since the 'chatgpt moment' in late 2022, multimodal AI models have surged forward with ever more astonishing capabilities. The impact of these AI systems in the fields of GIS, remote sensing, and spatial data analysis (collectively called Geospatial) is starting to take shape.
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AI models are increasingly capable of writing custom analysis code and are morphing from a source of advice to an agent capable of executing GIS commands directly on your computer.
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In the domain of imagery analysis, the open ecosystem of machine learning is producing ever more capable models for a variety of tasks like object detection and image classification. The ML workflow is on the cusp of being automated.
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Remote sensing foundation models hold the promise of accelerating ML accuracy and text-vision models are making it possible to perform complex image analyis with text prompts.
This 3-Part series will begin with a broad overview of developments in GeoAI. It aims to be more accessible to a general audience and demystify the jargon for researchers working with GIS/Remote Sensing data.
The following two sessions will be more technical and guide attendees through hands-on examples of GeoAI technology in practice. Together, we will 'vibe code' our way through Google Earth Engine, QGIS, and run through a ML Vision workflow using a state-of-art cloud computer Jetstream2. Following along during the technical sessions will require some pre-workshop setup.
The series is FREE and open to all University of Arizona personnel and is tailored for graduate students, postdocs, and early career faculty looking to expand their geospatial skills. Basic knowledge of scripting languages (i.e., python) and some prior geospatial experience will be helpful. Please register here for the workshop
As we explore these topics together, I hope it will spark curiosity and wonder, but also appropriate caution. As our AI systems are getting more powerful and more autonomous, it's important that we integrate them into our current workflows with thought and care.
| Date | Topic | Description |
|---|---|---|
| 09/04/2025 | The Geospatial AI Landscape | Join us as we explore the convergence of GIS/Remote Sensing with AI and how this will lower technical barriers while being a force multiplier in geographic data analyis. |
| 09/11/2025 | Hands-On 👋 Coding Agents in QGIS and Earth Engine |
The session will demonstrate two AI tools for autonomous geospatial analysis: - Execute commands in QGIS with Anthropic's Claude - Generate analysis code in Earth Engine using Google's Gemini. To follow along, please see the pre-session setup instructions here. |
| 09/18/2025 | Hands-On 👋 End-to-End Deep Learning Workflow for Aerial Imagery Analysis |
This session will walk through a complete machine learning workflow from getting a pretrained model, to zero-shot predictions, to image labeling and training. Several open-source tools will be used including Data-to-Science, jupyter notebooks, pytorch, github, and Huggingface. To follow along, please see the pre-session setup instructions here. |
The series is expert-led by educators at the University of Arizona Data Science Institute and Cyverse.
Jeffrey Gillan Ph.D, is a research data scientist with Cyverse and has 17 years of experience in geospatial science. His remote sensing expertise includes drone-based photogrammetry, LiDAR, and hyperspectral image analysis. He is an advovate open science and enjoys building imagery processing pipelines.
Dr. Gillan is available for consulations and collaborations if you are looking to incorporate geospatial data science in your research or grant proposal.
UArizona DataLab, Data Science Institute, University of Arizona, 2025.
