Deep Learning for Remote Sensing pdf
Tutors: Loic Landrieu (IGN France), Sébastien Lefevre (IRISA/Université Bretagne Sud), Bertrand Le Saux (ONERA)
Deep Learning has led to significant breakthroughs in various fields including computer vision. Remote sensing also benefits from such methodological advances and deep networks currently achieve state-of-the-art results in many automatic tasks, such as object detection, semantic segmentation (e.g. for land cover mapping), change detection, etc. The goal of this course is to introduce deep learning, review the main architectures relevant for cartography, photogrammetry and other EuroSDR-related fields, as well as to train the participants with available software and codes.
It is complementary to the course “Topographic Maps through Description and Classification of Remotely Sensed Imagery and Cartographic Enhancement” that focuses on the traditional approach to automated classification (i.e. feature extraction and supervised classification) while deep learning brings a paradigm change by learning both the features and the classifier, at the possible cost of higher labelled datasets and higher computational resources. The audience will be welcome to come with their own data to discuss the lecturers about the relevance of deep learning solutions in their context.