Session Chairs: Dr. Connie Ko, and Dr. Gunho Sohn, York University, Department of Earth and Space and Engineering, Lassonde School of Engineering
Description: The expansion in accessibility of remote sensing data combined with increases in data quantity and complexity of the incoming data has also increased. This challenge requires new theory, methods and system implementations in remote sensing data analytics. Recently, deep learning has been demonstrating an enormous success in visual data analytics. Deep learning is a non-representation learning model, which allows data to learn from its own representation. Due to its generalizability, this technology has been widely used in both computer science and remote sensing community. However, when applying these methods to the outdoor natural environment and natural objects, there are still many problems to be solved, which include, but are not limited to: learning with small, noisy, out-of-distribution training data, domain and knowledge transfer, active, continual and fine-grained learning, integrating physical priors, and data fusions. This special session will bring an opportunity to discuss the current status of the AI technology adopted in the remote sensing research community and discuss its limitations and potential for directing our future research efforts.
41st Canadian Symposium on Remote SensingLandscapes of Change; Remote Sensing for a Sustainable Future
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