The Earth is awash in data about itself. Every day, satellites capture around 100 terabytes of imagery.
But making sense of it isn’t always easy. Seemingly simple questions can be fiendishly complex to answer. Take this question that is of vital economic importance to California: How many fire breaks does the state have that might stop a wildfire in its tracks, and how have they changed since the last fire season?
“Originally, you’d have a person look at pictures. And that only scales so far,” Nathaniel Manning, co-founder and CEO of LGND, told TechCrunch. In recent years, neural networks have made it a bit easier, allowing machine learning experts and data scientists to train algorithms how to see fire breaks in satellite imagery.
“You probably sink, you know, couple hundred thousand dollars — if not multiple hundred thousand dollars — to try to create that data set, and it would only be able to do that one thing,” he said.
LGND wants to slash those figures by an order of magnitude or more.
“We are not looking to replace people doing these things,” said Bruno Sánchez-Andrade Nuño, LGND’s co-founder and chief scientist. “We’re looking to make them 10 times more efficient, one hundred times more efficient.”
LGND recently raised a $9 million seed round led by Javelin Venture Partners, the company exclusively told TechCrunch. AENU, Clocktower Ventures, Coalition Operators, MCJ, Overture, Ridgeline, and Space Capital participated. A number of angel investors also joined, including Keyhole founder John Hanke, Ramp co-founder Karim Atiyeh, and Salesforce executive Suzanne DiBianca.
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The startup’s core product is vector embeddings of geographic data. Today, most geographic information exists in either pixels or traditional vectors (points, lines, areas). They’re flexible and easy to distribute and read, but interpreting that information requires either deep understanding of the space, some nontrivial amount of computin …