Smart deep learning by incorporating remote sensing domain knowledge in vegetation characterization

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The goal of this research program is to develop innovative methods to exploit the synergy between Earth observation and artificial intelligence (specifically deep learning). It is motivated by the demand for advancing science and innovation in the inventory and assessment of vegetation canopies. This is driven by technological developments in observing the Earth's surface from space, satellites or aircraft (remote sensing), and enabled by the development of deep learning. The past several years have witnessed a massive growth of deep learning in remote sensing. Even though some progress has been made, deep learning research in remote sensing is still in its infancy. It has mainly focused on applying and fine-tuning existing networks, and deep learning approaches are currently only data-driven and without any explicitly expressed existing knowledge in the domain. It is not necessary to learn what we have already known. To fully realize the potential generated by deep learning in revolutionizing remotely sensed data analysis, research is needed for in-depth integration between deep learning and remote sensing. To date, deep learning has been used to classify broad categories of land cover, but rarely for characterizing forest canopies and identifying tree species, which is important but challenging in Earth observation. The objective of this research program is to develop innovative approaches to integrate remote sensing domain knowledge and deep learning to address challenges in three related areas: 1) individual tree crown delineation, 2) individual tree species classification, and 3) the retrieval of biophysical parameters of vegetation canopies. Moreover, we will develop algorithms by exploiting the use of traditional machine learning methods and prior knowledge to solve the issues related to limited training data, the incorporation of the prior knowledge in the design the deep learning network and in the learning process, and the effective utilization of multi-source remotely sensed data. As the outcome of this research program we will ensure that Canada remains at the forefront of the artificial intelligence revolution by bridging the gap between deep learning and remote sensing. The research results will deepen our understanding of deep learning in advancing scientific and industrial applications. By providing efficient and effective ways to keep track of the species, functional status and productivity of forests, from proposed research we will help Canada be a better steward of its greatest natural resource. The practical applications are significant and wide ranging, from wildlife habitat mapping, to biofuel production, to forest fire prevention. Finally, over the next five years, HQP including 4 PhD, 4 MSc and 4 undergraduate students, will be trained to gain advanced skills to excel in either academia or industry, using their expertise to enhance Canada's brain trust in artificial intelligence and geospatial technologies.