Cloud Native Remote Sensing with Python

This intermediate-level course provides a structured introduction to cloud-native remote sensing using Python. It focuses on tools and techniques for processing large climate and earth observation datasets within a modern cloud environment. Participants learn to leverage powerful Python packages such as XArray and Dask to efficiently handle and analyze extensive spatio-temporal raster data, moving beyond traditional desktop GIS limitations.

The curriculum is designed for practical application, utilizing Google Colab for all exercises. This setup eliminates the need for participants to install any packages or download datasets locally, providing a ready-to-use, browser-based environment. Course notebooks are easily accessible and can be copied to a personal Google Drive account for modification, saving, and sharing, fostering an interactive learning experience.

Within the Colab environment, users are guided through essential aspects of package management, including installing third-party libraries via pip, and data management, such as creating local folders and downloading external data from the internet. This hands-on approach ensures participants gain practical experience in setting up and managing their remote sensing workflows effectively in a cloud-based setting, preparing them for real-world applications.