Course information

The course provides a comprehensive introduction to remote sensing and its applications in machine learning. The syllabus covers topics such as Earth observation, multispectral and hyperspectral data, thermal, radar, LIDAR, and SAR data, as well as various data acquisition systems. Students will learn basic remote sensing data processing pipelines, supervised classification techniques, and neural network architectures, including convolutional neural networks. The course also includes object-based image analysis and practical applications of remote sensing. Upon successful completion of the course, students will have the skills to integrate scientific knowledge, implement remote sensing data processing pipelines, and develop software for visualizing and classifying multispectral data and filtering images. They will also be able to integrate, implement & develop software for statistical and machine learning (PyTorch) remote sensing analytics.