{"id":1149,"date":"2021-11-04T08:57:12","date_gmt":"2021-11-04T08:57:12","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/11\/04\/pixels-galore-metaspectrals-migel-tissera-on-using-ai-to-manage-image-data-for-space-exploration-and-more\/"},"modified":"2021-11-04T08:57:12","modified_gmt":"2021-11-04T08:57:12","slug":"pixels-galore-metaspectrals-migel-tissera-on-using-ai-to-manage-image-data-for-space-exploration-and-more","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/11\/04\/pixels-galore-metaspectrals-migel-tissera-on-using-ai-to-manage-image-data-for-space-exploration-and-more\/","title":{"rendered":"Pixels Galore: Metaspectral\u2019s Migel Tissera on Using AI to Manage Image Data for Space Exploration and More"},"content":{"rendered":"<div data-url=\"https:\/\/blogs.nvidia.com\/blog\/2021\/11\/03\/migel-tissera\/\" data-title=\"Pixels Galore: Metaspectral\u2019s Migel Tissera on Using AI to Manage Image Data for Space Exploration and More\">\n<p>Moondust, minerals and soil types are just some of the materials that can be quickly identified and analyzed with AI based on their images.<\/p>\n<p>Migel Tissera is co-founder and CTO of <a href=\"https:\/\/metaspectral.com\/\">Metaspectral<\/a>, a Vancouver-based startup that provides an AI-based data management and analysis platform for ultra-high-resolution images.<\/p>\n<p>He spoke with <a href=\"https:\/\/blogs.nvidia.com\/ai-podcast\/\" target=\"_blank\" rel=\"noopener\">NVIDIA AI Podcast<\/a> host Noah Kravitz about how Metaspectral\u2019s technologies help space explorers make quicker and better use of the massive amounts of image data they collect out in the cosmos.<\/p>\n<p>In addition to space, the startup\u2019s platform is used across industries such as agriculture, forensics and recycling.<\/p>\n<p>\u00a0<\/p>\n<h2><b>Key Points From This Episode:<\/b><\/h2>\n<ul>\n<li>Hyperspectral imaging collects and processes information from across the electromagnetic spectrum, pixel by pixel. It can be used to find objects or identify materials \u2014 like moondust, which informs where on the lunar surface an astronaut could land.<\/li>\n<li>Analyzing such ultra-high-resolution images can be difficult and slow, due to their huge amounts of data. Metaspectral\u2019s 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.<\/li>\n<\/ul>\n<h2><b>Tweetables:<\/b><\/h2>\n<p>\u201cWe have to come up with really good technologies that can efficiently use data within an allocated time frame.\u201d \u2014 Migel Tissera [2:40]<\/p>\n<p>By capturing the entire spectrum of light per pixel, \u201cyou can figure out the underlying material of that pixel and map the geological formation.\u201d \u2014 Migel Tissera [5:04]<\/p>\n<h2><b>You Might Also Like:<\/b><\/h2>\n<p><a href=\"https:\/\/soundcloud.com\/theaipodcast\/ai-sherd\" target=\"_blank\" rel=\"noopener\"><b>Researchers Chris Downum and Leszek Pawlowicz Use Deep Learning to Accelerate Archaeology<\/b><\/a><\/p>\n<p>Researchers in the Department of Anthropology at Northern Arizona University are using GPU-based deep learning algorithms to categorize sherds \u2014 tiny fragments of ancient pottery.<\/p>\n<p><a href=\"https:\/\/soundcloud.com\/theaipodcast\/nvidia-sifei-liu-3d-reconstructions-endangered-species\" target=\"_blank\" rel=\"noopener\"><b>Wild Things: NVIDIA\u2019s Sifei Liu Talks 3D Reconstructions of Endangered Species<\/b><\/a><\/p>\n<p>Endangered species can be challenging to study, as they are elusive and the very act of observing them can disrupt their lives. Now, scientists can take a closer look at endangered species by studying AI-generated 3D representations of them.<\/p>\n<p><a href=\"https:\/\/soundcloud.com\/theaipodcast\/ai-opseyes\" target=\"_blank\" rel=\"noopener\"><b>Waste Not, Want Not: AI Startup Opseyes Revolutionizes Wastewater Analysis<\/b><\/a><\/p>\n<p>What do radiology and wastewater have in common? Hopefully, not much. But at startup Opseyes, founder Bryan Arndt and data scientist Robin Schlenga are using AI to analyze wastewater samples.<\/p>\n<h2><b>Subscribe to the AI Podcast<\/b><\/h2>\n<p>Get the <a href=\"https:\/\/blogs.nvidia.com\/ai-podcast\/\">AI Podcast<\/a> through <a href=\"https:\/\/itunes.apple.com\/us\/podcast\/the-ai-podcast\/id1186480811?mt=2&amp;adbsc=social_20161220_68874946&amp;adbid=811257941365882882&amp;adbpl=tw&amp;adbpr=61559439\">iTunes<\/a>, <a href=\"https:\/\/podcasts.google.com\/?feed=aHR0cHM6Ly9mZWVkcy5zb3VuZGNsb3VkLmNvbS91c2Vycy9zb3VuZGNsb3VkOnVzZXJzOjI2NDAzNDEzMy9zb3VuZHMucnNz\">Google Podcasts<\/a>, <a href=\"https:\/\/play.google.com\/music\/listen?u=0#\/ps\/I4kyn74qfrsdhrm35mcrf3igxzm\">Google Play<\/a>, <a href=\"https:\/\/castbox.fm\/channel\/The-AI-Podcast-id433488?country=us\">Castbox<\/a>, DoggCatcher, <a href=\"https:\/\/overcast.fm\/itunes1186480811\/the-ai-podcast\">Overcast<\/a>, <a href=\"https:\/\/player.fm\/series\/the-ai-podcast\">PlayerFM<\/a>, Pocket Casts, <a href=\"http:\/\/www.podbay.fm\/show\/1186480811\">Podbay<\/a>, <a href=\"https:\/\/www.podbean.com\/podcast-detail\/cjgnp-4a6e0\/The-AI-Podcast\">PodBean<\/a>, PodCruncher, PodKicker, <a href=\"https:\/\/soundcloud.com\/theaipodcast\">Soundcloud<\/a>, <a href=\"https:\/\/open.spotify.com\/show\/4TB4pnynaiZ6YHoKmyVN0L\">Spotify<\/a>, <a href=\"http:\/\/www.stitcher.com\/s?fid=130629&amp;refid=stpr\">Stitcher<\/a> and <a href=\"https:\/\/tunein.com\/podcasts\/Technology-Podcasts\/The-AI-Podcast-p940829\/\">TuneIn<\/a>. If your favorite isn\u2019t listed here, drop us a note.<\/p>\n<p><a href=\"https:\/\/podcasts.apple.com\/us\/podcast\/the-ai-podcast\/id1186480811\"><\/p>\n<p><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/blogs.nvidia.com\/wp-content\/uploads\/2019\/10\/applepodcasts_ai.png\" alt=\"\" width=\"300\" height=\"80\"><\/p>\n<p><\/a> <a href=\"https:\/\/podcasts.google.com\/?feed=aHR0cHM6Ly9mZWVkcy5zb3VuZGNsb3VkLmNvbS91c2Vycy9zb3VuZGNsb3VkOnVzZXJzOjI2NDAzNDEzMy9zb3VuZHMucnNz\"><\/p>\n<p><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/blogs.nvidia.com\/wp-content\/uploads\/2019\/10\/listen-on-google-podcasts-1-300x76.png\" alt=\"\" width=\"300\" height=\"76\"><\/p>\n<p><\/a><a href=\"https:\/\/open.spotify.com\/show\/4TB4pnynaiZ6YHoKmyVN0L\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone \" src=\"https:\/\/blogs.nvidia.com\/wp-content\/uploads\/2019\/10\/spotify-podcast-badge-blk-grn-660x160-400x97.png.webp\" width=\"301\" height=\"73\"><\/a><\/p>\n<h2><b>Make the AI Podcast Better<\/b><\/h2>\n<p>Have a few minutes to spare? Fill out <a href=\"http:\/\/survey.podtrac.com\/start-survey.aspx?pubid=I5V0tOQFNS8j&amp;ver=short\">this listener survey<\/a>. Your answers will help us make a better podcast.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>http:\/\/feedproxy.google.com\/~r\/nvidiablog\/~3\/FN13-pQHMFI\/<\/p>\n","protected":false},"author":0,"featured_media":1150,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/1149"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=1149"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/1149\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/1150"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=1149"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=1149"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=1149"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}