How Open Data and AI Are Transforming Environmental Monitoring | Gracie Ermi

Open||Source||Data

Machine learning scientist Gracie Ermi joins Charna Parkey to explore how AI and open-source satellite data are changing the way we understand land use, climate impact, and environmental risk. At Impact Observatory, she helps create high-resolution, publicly available maps used by educators, researchers, and global organizations alike. A conversation about the technical challenges behind these tools, what open access really looks like in practice, and the role AI plays in making environmental data faster and more useful.

Quotes

Charna Parkey

“One of the most exciting things about where AI is headed is that we’re finally expanding its use beyond language. Gracie’s work is a prime example of how machine learning can interpret physical space, detect environmental change, and deliver insights that matter. It’s a reminder that AI isn't just a chatbot—it’s a tool to see, sense, and protect the planet.”

Gracie Ermi

“The biggest innovation we need right now isn’t necessarily a new AI model. It’s better, cheaper satellite imagery—especially higher-resolution data that’s still open access. Right now, we’re working mostly with Sentinel imagery, which has a 10-meter resolution. That’s great for a lot of things, but it limits what you can detect. Individual buildings, small changes—they get lost at that scale. If higher-res data became more affordable or openly available, it would change everything.”

Timestamps

00:00:00 – Introduction to Gracie Ermi and Impact Observatory’s mission using AI and open data for environmental monitoring.

00:02:00 – Gracie shares how she discovered computer science and open source, and how that shaped her interest in using tech for impact.

00:04:00 – Why Gracie chose to work at a mission-driven organization that prioritizes open access and environmental good.

00:06:00 – Real-world uses of Impact Observatory’s open-source maps

00:08:00 – Challenges around tracking open-source usage and the tension between openness and attribution in the ecosystem.

00:10:00 – How AI speeds up the creation of land-use maps

00:12:00 – Discussion on classical computer vision versus GenAI in geospatial work

00:14:00 – The technical limitations of current satellite imagery, particularly resolution and frequency, and how they affect output.

00:16:00 – Ethical considerations of increasing image resolution and what it might mean for privacy and surveillance.

00:18:00 – Reflections on unexpected risks and consequences that come with technological advancement in mapping.

00:24:00 – Advice for people with nontraditional backgrounds who want to enter AI or conservation tech.

00:26:00 – How Gracie uses GenAI tools like ChatGPT to overcome creative friction and emotional resistance to complex tasks.

00:28:00 – How large language models might help make geospatial tools more accessible, and what’s next for the field.