geospatial data
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Near Space Labs closes $13M Series A to send more Earth-imaging robots to the stratosphere
The decreasing cost of launch and a slew of other tech innovations have brought about a renaissance in geospatial intelligence, with multiple startups aiming to capture higher-quality and more frequent images of Earth than have ever before been available.
Most of these startups, however, are focused on using satellites to collect data. Not so for Near Space Labs, a four-year-old company that instead aims to gather geospatial intelligence from the stratosphere, using small autonomous wind-powered robots attached to weather balloons. The company has named its platform “Swifty,” and each one is capable of reaching altitudes between 60,000 and 85,000 feet and capturing 400-1,000 square kilometers of imagery per flight.
The company was founded in 2017 by Rema Matevosyan, Ignasi Lluch and Albert Caubet. Matevosyan, who is an applied mathematician by training and previously worked as a programmer, did her masters in Moscow. There, she started doing research in systems engineering for aerospace systems and also flew weather balloons to test aerospace hardware. “It clicked that we can fly balloons commercially and deliver a much better experience to customers than from any other alternative,” she told TechCrunch in a recent interview.
Four years after launch, the company has closed a $13 million Series A round led by Crosslink Capital, with participation from Toyota Ventures and existing investors Leadout Capital and Wireframe Ventures. Near Space Labs also announced that Crosslink partner Phil Boyer has joined its board.
Near Space, which is headquartered in Brooklyn and Barcelona, Spain, is primarily focused on urbanized areas where change happens very rapidly. The robotic devices that attach to the balloons are manufactured at the company’s workshop in Brooklyn, which are then shipped to launch sites across the country. The company’s CTO and chief engineer are both based in Barcelona, so the hardware R&D takes place over there, Matevosyan explained.
The company currently has eight Swifies in operation. It sells the data it collects and has developed an API through which customers can access the data via a subscription model. The company doesn’t need to have specific launch sites — Matevosyan said Swifties can launch from “anywhere at any time” — but the company does work in concert with the Federal Aviation Administration and air traffic control.
The main value proposition of the Swifty as opposed to the satellite, according to Matevosyan, is the resolution: From the stratosphere, the company can collect “resolutions that are 50 times better than what you would get from a satellite,” she said. “We are able to provide persistent and near real-time coverage of areas of interest that change very quickly, including large metro areas.” Plus, she said Near Space can iterate it’s technology quickly using Swifties’ “plug-and-play” model, whereas it’s not so easy to add a new sensor to a satellite fleet that’s already in orbit.
Near Space Labs founders (from left): Ignasi Lluch, Rema Matevosyan and Albert Caubet. Image Credits: Near Space Labs (opens in a new window)
Near Space has booked more than 540 flights through 2022. While customers pay for the flights, the data generated from each trip is non-exclusive, so the data can be sold again and again. Looking ahead, the company will be using the funds to expand its geographical footprint and bring on a bunch of new hires. The goal, according to Matevosyan, is to democratize access to geospatial intelligence — not just for customers, but on the developer side, too. “We believe in diverse, equal and inclusive opportunities in aerospace and Earth imaging,” she said.
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Descartes Labs launches its new platform for analyzing geospatial data
Descartes Labs, a well–funded startup based in New Mexico, provides businesses with geospatial data and the tools to analyze it in order to make business decisions. Today, the company announced the launch of its Descartes Labs Platform, which promises to bring its data together with all of the tools data scientists — including those with no background in analyzing this kind of information — would need to work with these images to analyze them and build machine learning models based on the data in them.
Descartes Labs CEO Phil Fraher, who took this position only a few months ago, told me that the company’s current business often includes a lot of consulting work to get its customers started. These customers span the range from energy and mining companies to government agencies, financial services and agriculture businesses, but many don’t have the in-house expertise to immediately make use of the data that Descartes Labs provides.
“For the most part, we still have to evangelize how to use geospatial data to solve business problems. And so a lot of our customers rely on us to do consulting,” Fraher said. “But what’s really interesting is that even with some of our existing customers, we’re now seeing more early adopters, more business and analysis teams and data scientists being hired, that do focus on geospatial data. So what’s really exciting with this launch is we’re now going to put our platform tool in the hands of those particular individuals that now can do their own work.”
In many ways, this new platform gives these customers access to the tools and data that Descartes Labs’ own team uses and allows them to collaborate with the company to solve their problems and use the new modeling tools to build solutions for their individual businesses.
“Previously, a data science team at a company that’s interested in this kind of analysis would also have to know how to wrangle very large-scale or petabyte-scale Earth observation data sets,” Fraher said. “These are very unique and specific skillsets and because of that kind of barrier to entry, the adoption of some of this technology and data sources has been slow.”
To enable more businesses to get started with working with this data (and become Descartes Labs customers), the company is betting on the standard tools in the industry, with hosted Jupyter notebooks, Python support and a set of APIs. It also includes tools to transform and clean the incoming data from Descartes’ third-party partners in order to make it usable for data scientists.
“It’s not just like some simple ETL-like data processing pipeline,” Descartes Labs’ head of Engineering Sam Skillman noted. “It’s something where we have to combine very in-depth data science, remote sensing and large-scale compute capabilities to bring all of that data in in a way that normalizes it and gets it ready for analysis.”
All of this analysis is handled in the cloud, of course.
The new platform is now available to businesses that want to give it a try.
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