Deep Learning has led to significant breakthroughs in various fields including natural language processing and 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. The audience will be offered to know about and practice with recent architectures proposed by the lecturers.

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 labeled datasets and higher computational resources.