Introduction#

The Self-training section provides a comprehensive set of resources designed to equip users with the essential skills in data science, machine learning and earth observation technologies. Whether users are new to the field or want to deepen their expertise, these modules will guide them step-by-step through key concepts, practical tools and cutting-edge methods used in the field today.

The following sub-sections offer valuable insights:

  1. Pre-processing: this section covers how to clean, transform and prepare raw data, ensuring accuracy and efficiency in workflows.

  2. Machine Learning: this module teaches the fundamentals of machine learning, including classification, regression and clustering algorithms, helping users extract meaningful insights from complex datasets.

  3. Deep Learning: this section covers neural networks, convolutional architectures and advanced AI methods to tackle tasks in computer vision, natural language processing and more.

  4. GeoDataCubes: this section introduces GeoDataCubes, a crucial tool for managing large-scale geospatial datasets, enabling more efficient analysis of environmental and earth science data.

  5. xcube: this module demonstrates the use of this open-source Python library for generating, manipulating and exploiting multi-dimensional data cubes, particularly for Earth observation applications.

  6. EO-College - Cube & Clouds: this section focuses on the use of data cubes and cloud computing for processing and analyzing vast volumes of Earth observation data.

Each section combines theoretical knowledge with practical exercises, allowing users to progress at their own pace while building a strong foundation for real-world applications.