Moondust, minerals and soil types are just some of the materials that can be quickly identified and analyzed with AI based on their images.
Migel Tissera is co-founder and CTO of Metaspectral, a Vancouver-based startup that provides an AI-based data management and analysis platform for ultra-high-resolution images.
He spoke with NVIDIA AI Podcast host Noah Kravitz about how Metaspectral’s technologies help space explorers make quicker and better use of the massive amounts of image data they collect out in the cosmos.
In addition to space, the startup’s platform is used across industries such as agriculture, forensics and recycling.
Key Points From This Episode:
- Hyperspectral imaging collects and processes information from across the electromagnetic spectrum, pixel by pixel. It can be used to find objects or identify materials — like moondust, which informs where on the lunar surface an astronaut could land.
- Analyzing such ultra-high-resolution images can be difficult and slow, due to their huge amounts of data. Metaspectral’s AI-based solution compresses data up to 90 percent while maintaining its integrity. This allows the data to be transmitted from space to earth, processed by a high-compute system and sent back to users for real-time action.
“We have to come up with really good technologies that can efficiently use data within an allocated time frame.” — Migel Tissera [2:40]
By capturing the entire spectrum of light per pixel, “you can figure out the underlying material of that pixel and map the geological formation.” — Migel Tissera [5:04]
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