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.
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 computing, or both.
Geographic embeddings summarize spatial data in a way that makes it easier to find relationships between different points on Earth.
“Embeddings get you 90% of all the undifferentiated compute up front,” Nuño said. “Embeddings are the universal, super-short summaries that embody 90% of the computation you have to do anyways.”
Take the example of fire breaks. They might take the form of roads, rivers, or lakes. Each of them will appear differently on a map, but they all share certain characteristics. For one, pixels that make up an image of a fire break won’t have any vegetation. Also, a fire break will have to be a certain minimum width, which often depends on how tall the vegetation is around it. Embeddings make it much easier to find places on a map that match those descriptions.
LGND has built an enterprise app to help large companies answer questions involving spatial data along with an API which users with more specific needs can hit directly.
Manning sees LGND’s embeddings encouraging companies to query geospatial data in entirely new ways.
Imagine an AI travel agent, he said. Users might ask it to find a short-term rental with three rooms that’s close to good snorkeling. “But also, I want to be on a white sand beach. I want to know that there’s very little sea weed in February, when we’re going to go, and maybe most importantly, at this time of booking, there’s no construction happening within one kilometer of the house,” he said.
Building traditional geospatial models to answer those questions would be time consuming for just one query, let alone all of them together.
If LGND can succeed in delivering such a tool to the masses, or even just to people who use geospatial data for their jobs, it has the potential to take a bite out of a market valued near $400 billion.
“We’re trying to be the Standard Oil for this data,” Manning said.
