![\"Planet](\"http:\/\/space.skyrocket.de\/img_sat\/dove-3__1.jpg\")
Historically, the space industry was characterized by decades long, billion dollar projects, making it close to impossible for commercial players to get involved. As recently as 2010, \u201cthe dominant imaging satellite was the size of a school bus and cost upwards of $700 million to build and launch.\u201d(2)\u00a0The development of modular, miniaturized space components has allowed companies such as Skybox (acquired by Google for $500 million in 2014 (3)) to develop much smaller (think \u201cmini-fridge\u201d or \u201cwine bottle\u201d), low-cost satellites called nanosats, without significant quality sacrifices.<\/p>\n
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Meanwhile, launches have become cheaper and more frequent. SpaceX first disrupted the industry with its relatively low-cost ($62 million) commercial rockets. Though these rockets are designed for large payloads, nanosats can \u201chitch a ride\u201d for a lower fee (4). Newer entrants like Virgin Galactic\u2019s LauncherOne project and Rocket Lab\u2019s $4.9 million Electron rocket are specifically targeting smaller satellites for low-cost, frequent launches\u00a0(5). With the legacy school bus-sized imaging satellites, images like those you see on Google Earth cost in the thousands and are updated monthly (6). A network of small satellites, however, can produce daily images of any point on the globe at \u201ca fraction of the cost (7).\u201d<\/p>\n
Until recently, processing this output would require warehouses of analysts manually poring over individual images. This is where Orbital Insights comes in. Founded in 2013 by James Crawford, an artificial intelligence expert with experience at NASA and Google, the company has developed a scalable software solution for space image analysis. Orbital Insights uses \u201cdeep learning\u201d neural networks and machine vision to algorithmically identify and quantify objects and patterns in individual images, and then big data techniques to aggregate those patterns and derive macro insights from micro inputs for its clients.<\/p>\n What does this actually mean? By counting the number of cars in the 50,000 parking lots of 90 major\u00a0U.S. retailers over time, Orbital Insights can predict retail sales trends before they are reported to financial markets.\u00a0By analyzing shadows inside 20,000 oil storage tanks globally (the tanks have floating lids whose height depends on the volume of oil inside), Orbital makes regional and global oil inventory trend estimates, including for regions where official data is non-existent or suspect. By tracking building shadows (which are proportional to building height), car and truck density, road building, and electricity consumption (indicated by nighttime illumination), Orbital measures economic development in China, a country known for its lack of transparency when it comes to economic data (7).<\/p>\n Orbital Insight\u2019s work is of obvious interest to investors hoping to find an informational edge in the public markets, and asset managers make up most of their client base, but the company also works with governments and NGOs. Last year, Orbital partnered with the World Bank, focusing on poverty data (8).<\/p>\n The company\u2019s operating model is focused only on the software side, thereby avoiding significant investment in hardware (8). Orbital doesn\u2019t have its own satellites, but by partnering with a broad range of 3rd<\/sup> party imagery providers like Digital Globe, Airbus, and Planet Labs (9),\u00a0it claims to achieve \u201ca level of data and picture resolution that no single satellite provider can compete with (8).\u201d<\/p>\n However, I believe the company and its VCs are underappreciating the vulnerability of their business model. In early 2016, Google renamed previously acquired satellite company Skybox Imaging to Terra Bella and announced a new focus on analyzing their images through software (10).\u00a0Google is no stranger to machine learning, and its announcement is a credible threat. Moreover, while Orbital Insight\u2019s edge of working with multiple imagery providers holds weight in the legacy world of large satellites with limited coverage, the nanosat future would enable any given satellite operator to have sufficient geospatial coverage to disintermediate Orbital Insights. To preserve its edge, Orbital Insights should seek to expand its imagery inputs from satellites to aircraft, drones, and anything else it can get its hands on.<\/p>\n <\/p>\n [Word Count: 790]<\/p>\n <\/p>\n <\/a>Works Cited<\/strong><\/p>\n (1) Roger Cheng, \u201cMoore\u2019s Law is the Reason Your iPhone is So Thin and Cheap,\u201d CNET<\/em>, April 16, 2015, \u00a0https:\/\/www.cnet.com\/news\/moores-law-is-the-reason-why-your-iphone-is-so-thin-and-cheap\/<\/a>, accessed November 15, 2016.<\/p>\n (2)\u00a0Murray Newlands, \u201cTo Infinity and Beyond with Investments: Rocketing into Space Investing,\u201d Forbes<\/em>, August 27, 2015, http:\/\/www.forbes.com\/sites\/mnewlands\/2015\/08\/27\/to-infinity-and-beyond-with-investments-rocketing-into-space-investing\/#42f1a7d6d312<\/a>, accessed November 15, 2016.<\/p>\n (3)\u00a0Christopher Mims, \u201cAmid Stratospheric Valuations, Google Unearths a Deal with Skybox,\u201d Wall Street Journal<\/em>, June 15, 2014, http:\/\/www.wsj.com\/articles\/amid-stratospheric-valuations-google-unearths-a-deal-with-skybox-1402864823<\/a>, accessed November 15, 2016.<\/p>\n (4)\u00a0Samantha Masunaga, \u201cSmall Satellites are Back, with Down-To-Earth Expectations,\u201d Los Angeles Times<\/em>, May 27, 2016, http:\/\/www.latimes.com\/business\/la-fi-adv-small-satellites-20160519-snap-story.html<\/a>, accessed November 15, 2016.<\/p>\n (5)\u00a0Bernie Lo, Nishka Chandran, \u201cRocket Lab Nears Completion of World\u2019s First Private Orbital Launch Site in New Zealand,\u201d CNBC<\/em>, August 28, 2016, http:\/\/www.cnbc.com\/2016\/08\/28\/rocket-lab-nears-completion-of-worlds-first-private-orbital-launch-site-in-new-zealand.html<\/a>, accessed November 15, 2016.<\/p>\n (6)\u00a0Klint Finley, \u201cHow AI Can Calculate Our Oil Surplus\u2026From Space,\u201d Wired<\/em>, March 16, 2015, \u00a0https:\/\/www.wired.com\/2015\/03\/orbital-insight\/<\/a>, accessed November 15, 2016.<\/p>\n (7) Orbital Insights, \u201cSolutions,\u201d https:\/\/orbitalinsight.com\/solutions<\/a>, accessed November 15, 2016.<\/p>\n (8)\u00a0Connie Loizos, \u201cOrbital Insight Lands $20 Million from Investors, Led by GV,\u201d Techcrunch<\/em>, June 27, 2016, https:\/\/techcrunch.com\/2016\/06\/27\/orbital-insight-lands-20-million-from-investors-led-by-gv\/<\/a>, accessed November 15, 2016.<\/p>\n (9)\u00a0Joshua New, \u201c5 Q\u2019s for James Crawford, Chief Executive Officer at Orbital Insight,\u201d Center for Data Innovation<\/em>, April 4, 2016, https:\/\/www.datainnovation.org\/2016\/04\/5-qs-for-james-crawford-chief-executive-officer-at-orbital-insight\/<\/a>, accessed November 15, 2016.<\/p>\n![\"Machine](\"https:\/\/orbitalinsight.com\/wp-content\/uploads\/2016\/01\/orbital-a.jpg\")
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