Reference number : GC-2017-Q4-00865, amendment number : , agreement type : G, recipient legal name : Jodoin, Pierre-Marc (Université de Sherbrooke), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : In collaboration with UrtheCast inc., the goal of this project is to explore the concrete usability of deep_x000D_
convolutional neural networks in the field of satellite imagery. Since this project spans over a short period of_x000D_
time (only 6 months) we will focus on image segmentation, a topic at the core of the research activities of pr._x000D_
Jodoin and a glaring issue for UrtheCast. As such, we look forward to answer the following four questions:_x000D_
1) Which state-of-the-art network configuration works best and what is its accuracy compared to the_x000D_
none-deep-learning solutions currently deployed by UrtheCast?_x000D_
2) How many manually annotated satellite images are required to properly train a convolutional neural_x000D_
network? Also, does weakly annotated data can be used to increase the accuracy of a network?_x000D_
3) How can very large satellite images (from 2,000 x 2,000 to 6,000 x 6,000 pixels) be processed (both at_x000D_
training and at test time) on a single 12 Gb GPU?_x000D_
4) Can a model trained on a RGB dataset be transferred to a multispectral dataset as well as data from the_x000D_
UrtheCast satellites?_x000D_
Since deep segmentation models are new to the world of remote sensing, this project is fundamentally_x000D_
important both for UrtheCast and the Canadian society as a whole. With its "UrtheDaily(TM)" project,_x000D_
UrtheCast is about to deploy a constellation of satellites which will acquire an unprecedented number of_x000D_
images that no one can manually analyse. This project will thus provide the company with the tools for_x000D_
processing those images. The same models could also be used to process data acquire by other Canadian_x000D_
satellites such as RadarSat2 and SCISAT., program purpose french : In collaboration with UrtheCast inc., the goal of this project is to explore the concrete usability of deep_x000D_
convolutional neural networks in the field of satellite imagery. Since this project spans over a short period of_x000D_
time (only 6 months) we will focus on image segmentation, a topic at the core of the research activities of pr._x000D_
Jodoin and a glaring issue for UrtheCast. As such, we look forward to answer the following four questions:_x000D_
1) Which state-of-the-art network configuration works best and what is its accuracy compared to the_x000D_
none-deep-learning solutions currently deployed by UrtheCast?_x000D_
2) How many manually annotated satellite images are required to properly train a convolutional neural_x000D_
network? Also, does weakly annotated data can be used to increase the accuracy of a network?_x000D_
3) How can very large satellite images (from 2,000 x 2,000 to 6,000 x 6,000 pixels) be processed (both at_x000D_
training and at test time) on a single 12 Gb GPU?_x000D_
4) Can a model trained on a RGB dataset be transferred to a multispectral dataset as well as data from the_x000D_
UrtheCast satellites?_x000D_
Since deep segmentation models are new to the world of remote sensing, this project is fundamentally_x000D_
important both for UrtheCast and the Canadian society as a whole. With its "UrtheDaily(TM)" project,_x000D_
UrtheCast is about to deploy a constellation of satellites which will acquire an unprecedented number of_x000D_
images that no one can manually analyse. This project will thus provide the company with the tools for_x000D_
processing those images. The same models could also be used to process data acquire by other Canadian_x000D_
satellites such as RadarSat2 and SCISAT., agreement title english : Development of deep segmentation methods applied to satellite imagery, agreement title french : Development of deep segmentation methods applied to satellite imagery, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, 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 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., 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