Can AI be used to solve major planetary problems? Today I'm joined by the CEO and Co-Founder of Astraea, Daniel Bailey, to talk about leveraging geospatial data for sustainability pursuits. Astraea's platform uses satellite imagery and AI to enable customers to access and operationalize spatiotemporal insights across multiple industries including clean energy, agriculture, conservation, carbon finance, and real estate.
Daniel fills us in on the issues Astraea aims to solve and the role of machine learning in its mission. We find out what makes satellite imagery unique (and uniquely challenging to work with) and how Astraea ensures that its models continue to meet customers’ needs over time. Daniel shares insight into the ML development process and advice for other leaders of AI-powered startups. Tune in to discover the balance between model accuracy and explainability, the importance of transparency when it comes to voluntary carbon markets, and more!
Listen the full recording here
“We're in this golden age of measurement. There's more data than you can look at individually. You really have to have something like AI/ML to recognize those patterns and extract those valuable insights from the data.” — Daniel Bailey
“Satellite imagery is a unique beast, for sure … The dimensionality of the data is completely unique.” — Daniel Bailey
“We do champion challenger techniques so that when we have a model in production, we're constantly looking for a better model and innovating on that capability.” — Daniel Bailey
“Without transparency, the voluntary market will collapse. We will never reach our goal of a two-degree Celsius without the voluntary carbon markets that are by nature a deregulated marketplace.” — Daniel Bailey
“When we think about creating ML products and features within the product, we think about using the most simplistic approach first.” — Daniel Bailey
“In the geospatial AI space, it is going to take a community to provide the capabilities we need to resolve some of these intractable problems we're facing as a planet.” — Daniel Bailey
“We need more training data to build better models to meet the needs that we're seeing globally.” — Daniel Bailey