The World Economic Forum estimates that by 2030, Earth observation insights will contribute to more than $700 billion in economic opportunity. Enabled by a growing number of satellites, drones, weather balloons, and other devices, Earth observation technologies have dramatically evolved from capturing simple low-resolution images to producing high-resolution images with a far greater breadth and variety of data. Hyperspectral imagery (HSI) is one such technology that splits up an object’s electromagnetic spectrum into very fine imaging bands, creating incredibly detailed and accurate images.
The rich data unlocks new possibilities for innovative applications, representing a major advancement in the field of Earth observation. Which is why we are delighted to announce Toyota Ventures’ investment in Matter Intelligence, a technology startup revolutionizing ultra-broadband hyperspectral sensor technology to fundamentally change how we perceive and understand the world around us.
Based in California, Matter Intelligence is developing a sensor package that combines centimeter-scale hyperspectral data, (RGB) imagery, and thermal imagery at extreme resolutions like never before. The company’s technology provides critical insights into the shape, composition, and temperature of natural and artificial materials, which is essential for industries like mining and insurance.
Matter is putting its advanced imaging sensors on its commercial satellites to directly measure these properties simultaneously globally using satellites for the first time at extreme (sub-meter) precision to see objects and phenomena that are invisible to today’s imagers. Matter’s EARTH-1 will be the first satellite to capture centimeter-scale resolution data on composition, temperature, and imagery, distinguishing small differences like cars from trees, natural from artificial, from hundreds of kilometers away.
Matter’s founding team brings deep expertise in space and hyperspectral imaging. CEO Vishnu Sridhar led key NASA Jet Propulsion Laboratory (JPL) missions like SuperCam on Mars 2020 and REASON on Europa Clipper, and served as Flight Director for the Mars Opportunity rover. CTO Nathan Stein contributed to science operations for NASA’s Curiosity and Dawn missions and led defense-related computer vision projects. Technical Director Thomas Chrien, with over 35 years in remote sensing, developed AVIRIS — one of the first hyperspectral imaging spectrometers — at NASA JPL and contributed to HYDICE, Hyperion, and more.
“Sub-one meter resolution hyperspectral imagery has the potential to provide unprecedented real-time insights across many industries. We look forward to supporting the team as they work to provide the first end-to-end hyperspectral imaging platform and data fusion engine for mapping precise material properties.” –Chris Abshire, principal, Toyota Ventures
Matter’s core imaging system is platform-agnostic, so its sensors can integrate with satellites, aircrafts, high-altitude balloons, and drones. The company is also developing a search engine with geospatial analytics. It will allow users to explore image analytics through spectral libraries and text searches. By using generative AI and ML, Matter will enable users to fuse images and other geospatial datasets for complex and novel applications including identifying deposits of rare earth elements, early crop disease detection, object detection and tracking, assessing biodiversity, and evaluating fire and flood risks and methane emissions.
Toyota Ventures is proud to support the Matter team by participating in the company’s $12M seed funding round. Chris Abshire, a principal on the Toyota Ventures Frontier Fund, drove our participation in the round, which was led by Lowercarbon Capital. Other participating investors include Pear VC, Level Cubed, Earth-to-Mars-Capital, Terranova VC, and Defense Angels.
Matter is engaging in early partnerships and pre-tasking in advance of the launch of its EARTH-1 satellite. To learn more about Matter Intelligence, visit the company’s website or the Toyota Ventures portfolio page.