Recent advances in satellite technology have led to a regular, frequent, and high-resolution monitoring of Earth at the global scale, providing an unprecedented amount of Earth observation (EO) ...
Recent advances in satellite technology have led to a regular, frequent, and high-resolution monitoring of Earth at the global scale, providing an unprecedented amount of Earth observation (EO) data. To efficiently process and analyze large amounts of EO data, remote sensing has evolved into a multidisciplinary field, where machine learning and computer vision algorithms play an important role nowadays. At the start of this project course, students receive project topics as well as some information material on Deep Learning for Earth Observation. After setting the project teams and topics, a project environment is decided (with the suitable tools for teamwork) with the assistance of the lecturer. Then, project planning, coordination and development start. During the weekly project meetings, each project team presents and discusses their progress as well as the next steps with the lecturer. The project is concluded with final reports as well as a final presentation. The general topics are related to the Chair’s current research activities and include, but are not limited to: i) image representation learning; ii) classification and retrieval of satellite images; iii) multimodal learning; iv) development of foundation models for vision and language.
