WASHINGTON \u2014 Maxar Intelligence released a new mapping product developed with Ecopia AI that converts satellite imagery into detailed 2D and 3D maps. The product, called Vivid Features, combines Maxar\u2019s satellite imagery archive with Ecopia\u2019s artificial intelligence software to automatically identify and outline buildings, roads, vegetation, water bodies and other features.<\/p>\n
Vector maps, unlike traditional raster images, represent the world using geometric shapes \u2014 points, lines and polygons \u2014 instead of pixels. They store location data as mathematical equations, making them more precise and easier to update. That precision is critical for everything from urban planning to defense operations, but keeping vector datasets current has historically been labor-intensive and slow.<\/p>\n
Maxar and Ecopia say the partnership is focused on reducing those delays and inconsistencies.<\/p>\n
\u201cMapmakers are stuck with laborious processes to update vector maps that reflect the ground truth, a process that takes days and misses critical developments,\u201d Maxar said in a Sept. 17 release. \u201cMaxar and Ecopia\u2019s new Vivid Features product changes that completely by delivering precisely aligned vector maps that mapping systems and AI models can trust. Vivid Features maintains 3-meter accuracy across time, so when your AI flags a new building or road, you know it\u2019s there.\u201d<\/p>\n
AI automation\u00a0<\/strong><\/p>\n Ecopia, founded in 2013, specializes in using artificial intelligence to extract features from high-resolution satellite imagery and build what it calls a \u201cdigital twin of the Earth.\u201d Its machine learning models can pick out and outline buildings, roads, vegetation, waterways and more with minimal human intervention.<\/p>\n By combining those algorithms with Maxar\u2019s global satellite archive, the companies say they can automatically generate vector maps that update as the real world changes.<\/p>\n \u201cVivid Features, powered by Ecopia, solves one of the most persistent challenges in digital mapping \u2014 the time-consuming process of producing consistent vector map data of every place on Earth,\u201d said Peter Wilczynski, Maxar\u2019s chief product officer. \u201cTogether, we\u2019ve created a product that eliminates the thousands of hours customers typically spend manually creating and aligning vectors, providing a dynamic basemap suite that maintains fidelity as the world changes.\u201d<\/p>\n Commercial and defense demand<\/strong><\/p>\n The potential use cases include government agencies that rely on accurate geospatial data for emergency planning, disaster response and infrastructure management. Commercial players use it for risk modeling in insurance, planning telecom networks and tracking population growth.<\/p>\n For military users, the companies said, vector maps could serve as a backbone of geospatial intelligence feeding into mission planning and decision making.<\/p>\n \u201cGaining an up-to-date, three-dimensional understanding of our planet and how it is changing is one of the most complex challenges our customers face,\u201d said Jon Lipinski, Ecopia\u2019s president and co-founder. \u201cWhile millions of square kilometers of satellite images are captured each day, many organizations struggle to develop corresponding 3D vector maps and keep them current to reflect real-world change. This partnership with Maxar will enable unprecedented access to up-to-date 3D vector maps across the globe.\u201d<\/p>\n Maxar, based in Westminster, Colorado, operates a fleet of high-resolution imaging satellites and has long provided imagery to both commercial customers and the U.S. government. Toronto-based Ecopia AI has carved out a niche by applying artificial intelligence and machine learning to geospatial challenges and has already partnered with major players in the sector.<\/p>\n \n\tRelated<\/p>\n","protected":false},"excerpt":{"rendered":"WASHINGTON \u2014 Maxar Intelligence released a new mapping product developed with Ecopia AI that converts satellite imagery into…\n","protected":false},"author":3,"featured_media":233773,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21],"tags":[691,738,125755,125756,16988,158,67,132,68],"class_list":{"0":"post-233772","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial-intelligence","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-geospatial-intelligence","11":"tag-maxar-intelligence","12":"tag-sn","13":"tag-technology","14":"tag-united-states","15":"tag-unitedstates","16":"tag-us"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@us\/115219574492816792","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/233772","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/comments?post=233772"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/233772\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media\/233773"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media?parent=233772"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/categories?post=233772"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/tags?post=233772"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}