Reference number : GC-2017-Q4-00365, amendment number : , agreement type : G, recipient legal name : Clausi, David (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : DND/NSERC Discovery Grant Supplement, program name french : Supplément aux subventions à la découverte MDN-CRSNG, program purpose english : Remote sensing, the science of aerial or satellite data capture of the earth, provides crucial information across land, oceans, and atmosphere. As a growing part of the world economy, the remote sensing market is expected to reach nearly $10 billion in 2017. Given the vast amount of remote sensing data being produced now and increasing in the future, automated algorithms are urgently needed to increase throughput, reduce turnaround time, save money, and perform routine monitoring while experts must be trained to understand and investigate scientific questions and lead future missions. My research focuses on the design and implementation of automated algorithms to monitor the earth s oceans using satellite data. The key images are produced by a world-class Canadian satellite, RADARSAT-2 and, after the launch date in 2018, by a group of three Canadian satellites called the RADARSAT Constellation Mission (RCM). The priority of these satellites is the monitoring of ice-infested waters in and around Canada and the Canadian Ice Service (CIS) is the government agency tasked with producing the required ice maps. CIS personnel manually interpret 10,000 scenes annually, providing ice typing and extents over huge (500km by 500km) regions in order to infer ice thickness, extent, and strength to facilitate the decision support systems for shipping and ice breaking. I develop artificial intelligence algorithms that can read and interpret the imagery as effectively as the human eye, but my algorithms provide far more detail than a human operator. My algorithms can determine whether a particular pixel is ice or water, and can further break this down into determining the ice type (e.g., young or old ice). Sea ice types and extents are crucial information for ship navigation and ship captains do not want to enter ice-infested waters without this information &ndash; my algorithms can deliver this information to them. Lake ice can be monitored for freeze up and melt to establish the overall ice concentration on the lake, and this is crucial information for understanding the impact of climate change. Further, I am developing algorithms that are able to detect oil spills in the radar-based imagery. Some ships illegally dump bilge oil into the ocean and this leaves a trail of oil that must be identified by satellite imagery quickly. My algorithms will seek to find these oil spills so that the perpetrators can be apprehended. Other northern nations would be interested in purchasing maps derived from the proposed algorithm. My research in automated image interpretation to solve these problems has also been and will continue to be successfully applied for automation to other domains such as medical imaging, 3d reconstruction, sports analytics, and video analytics, while simultaneously training students in computer vision to tackle these advanced intelligent systems problems and gain leadership roles in industry., program purpose french : Remote sensing, the science of aerial or satellite data capture of the earth, provides crucial information across land, oceans, and atmosphere. As a growing part of the world economy, the remote sensing market is expected to reach nearly $10 billion in 2017. Given the vast amount of remote sensing data being produced now and increasing in the future, automated algorithms are urgently needed to increase throughput, reduce turnaround time, save money, and perform routine monitoring while experts must be trained to understand and investigate scientific questions and lead future missions. My research focuses on the design and implementation of automated algorithms to monitor the earth s oceans using satellite data. The key images are produced by a world-class Canadian satellite, RADARSAT-2 and, after the launch date in 2018, by a group of three Canadian satellites called the RADARSAT Constellation Mission (RCM). The priority of these satellites is the monitoring of ice-infested waters in and around Canada and the Canadian Ice Service (CIS) is the government agency tasked with producing the required ice maps. CIS personnel manually interpret 10,000 scenes annually, providing ice typing and extents over huge (500km by 500km) regions in order to infer ice thickness, extent, and strength to facilitate the decision support systems for shipping and ice breaking. I develop artificial intelligence algorithms that can read and interpret the imagery as effectively as the human eye, but my algorithms provide far more detail than a human operator. My algorithms can determine whether a particular pixel is ice or water, and can further break this down into determining the ice type (e.g., young or old ice). Sea ice types and extents are crucial information for ship navigation and ship captains do not want to enter ice-infested waters without this information &ndash; my algorithms can deliver this information to them. Lake ice can be monitored for freeze up and melt to establish the overall ice concentration on the lake, and this is crucial information for understanding the impact of climate change. Further, I am developing algorithms that are able to detect oil spills in the radar-based imagery. Some ships illegally dump bilge oil into the ocean and this leaves a trail of oil that must be identified by satellite imagery quickly. My algorithms will seek to find these oil spills so that the perpetrators can be apprehended. Other northern nations would be interested in purchasing maps derived from the proposed algorithm. My research in automated image interpretation to solve these problems has also been and will continue to be successfully applied for automation to other domains such as medical imaging, 3d reconstruction, sports analytics, and video analytics, while simultaneously training students in computer vision to tackle these advanced intelligent systems problems and gain leadership roles in industry., agreement title english : Advanced Intelligent Computer Vision for Remote Sensing Scene Interpretation, agreement title french : Advanced Intelligent Computer Vision for Remote Sensing Scene Interpretation, agreement number : DGDND, agreement value : 120000.0, agreement start date : 2018-01-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2020-2021)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2020-2021)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada