Imagine a satellite orbiting the globe, quietly taking snapshots of the Earth\u2019s oceans and cities to cover the entire world, often in less than one hour. Now, almost 2000 satellites are operating at this pace, providing imagery with higher-resolution and faster speed than ever before. As data from space become more varied, and satellite imagery becomes commercially available, new applications are starting to emerge \u2014 from 3D mapping for wildfire prevention to realtime natural disaster response. But there\u2019s one challenge. When small disruptions hit without warning, how does a satellite know where to look? Social media has something to do with it.<\/p>\n
At the intersection of the human language and cutting-edge technology is natural language processing<\/strong> (NLP), a sub-division of machine-learning that deals with \u201cunstructured data,\u201d better known in this case as the human language. To put it simply, NLP is the process of training an algorithm to understand text-based data \u2014 often coming from Twitter or other news streams \u2014 and attach meaning to it.<\/p>\n \u201cNatural language processing is about data that are understandable by humans, but not by machines. So, we have to start from a human representation of this concept and move it into a mathematical universe where it means something to a machine. If two sentences have the same meaning but no word in common, how can you make a machine understand that it means the same thing? In other words, you\u2019re trying to build a space where different sentences with one meaning are encoded in the same way,\u201d explained Data Scientist Vincent Chabot.<\/p>\n At Kayrros, data scientists are building NLP algorithms to scrape, categorize and detect breaking news in realtime \u2014 often coming from Twitter \u2014 giving satellites in orbit a connecting point on the ground. The biggest challenge? Training algorithms to learn all the languages spoken across the energy industry\u2026 the largest industry in the world.<\/p>\n \u201cTaking energy as an example, there are countless industry assets spread across the world, filled with people communicating in many different languages. When training an algorithm, first, there\u2019s a language barrier. Then, you have the syntax; even within the same language, there are tons of ways to communicate the same message,\u201d noted Data Scientist Matthieu Mazzolini.<\/p>\n Kayrros uses NLP to gain early insight on emerging energy supply and demand disruptions, tracking initial intelligence that can allow teams to analyze the impact of events as they unfold. Before news hits markets, the first signs of disruptions \u2014 like an explosion, for example \u2014 show on social media feeds, when people in the surrounding area hear a blast or see smoke. Being the first to catch wind of intelligence on an event allows Kayrros to provide market intelligence to clients ahead of the market.<\/p>\n Apart from guiding the lens of a satellite, the news gathered from social media also provides the story behind satellite imagery and other quantitative or alternative data that wouldn\u2019t be obvious from analyzing the data alone.<\/p>\n \u201cIt brings context to data. If you just see a time series of satellite images, you might not understand the context surrounding them. But if you place this alongside relevant events in the news, you\u2019ll be able to make sense of the data derived from the imagery. Context is something that you can\u2019t guess just by looking at quantitative data,\u201d Matthieu explained.<\/p>\n Kayrros is continuing to build on its proprietary technologies, leveraging geospatial data to bring transparency to operations of the world\u2019s energy industry while also taking advantage of opportunities to apply similar techniques in other fields. Natural language processing adds a valuable layer to realtime monitoring by connecting the technology, algorithms and Earth observation with people on the ground.<\/p>","protected":false},"excerpt":{"rendered":"