The course focuses on Earth observation big data and its application in deep learning and data analytics. The syllabus covers a broad range of topics including Earth observation data analytics, deep learning fundamentals, convolutional neural networks, transfer learning, autoencoders, transformers, generative models, self-supervised learning, and object detection. Practical lab sessions provide hands-on experience with tools like Google Earth Engine, PyTorch, and TensorFlow. Students will gain skills in implementing server-side technologies, front-end services, and machine learning tools. They will also learn to develop software for data ingestion, querying, management, processing, and visualization. The course emphasizes the analysis of various types of data (1D, 2D, nD, time series, multispectral, hyperspectral) using advanced statistical and machine learning techniques. Upon successful completion of the course, students will be able to present and discuss their findings on theoretical and technical challenges effectively.
- Teacher: Κωνσταντίνος Καράντζαλος
ECTS : 0
Language : el, en