PHOENIX: Generative models and Deep Reinforcement Learning for Geospatial Computer Vision

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In 2017, the PI and Presagis Inc, Canada embarked on a collaboration to investigate deep learning for 3D reconstruction, object classification, and realistic appearance modelling. The collaboration was funded by an NSERC CRD/DND grant (~CAD $600K) and included the participation of Valcartier Defence Research & Development Canada (DRDC). The project was codenamed DAEDALUS (daedalus.theICTlab.org) and ran between 2018-2022.This proposal builds upon our existing research outcomes and aims to make a step-change by addressing some of the significant challenges identified in the duration of the previous project. Specifically, in this grant -codenamed PHOENIX- we will investigate the following two research objectives: (A) the development of novel models based on Generative Adversarial Networks (GANs) for structure-consistent image-to-image translation. Geospatial data such as high-resolution satellite imagery is expensive to acquire and specific to the capture's geographical location. A covariant shift is prominent and leads to failures in semantic segmentation networks trained on such data. Using structure-preserving generative models allows the transfer of semantic masks to the generated synthetic images and, therefore, widens the datasets' variability and reduces bias. (B) use deep Reinforcement Learning (RL) in complex, partially observable, large-scale environments for vision tasks such as road extraction. The objective is to investigate the use of RL for extracting geospatial information while ensuring the problem remains tractable.This project will facilitate the training of 11 HQP(2 Ph.D., 6 Masters, 3 USRAs). This research is expected to make substantial contributions to the solution of complex problems of high practical relevance to the field of machine learning for geospatial computer vision.