Remote sensing data is becoming crucial to solve some of the most important environmental problems, especially pertaining to agricultural applications and food security. Effectively working with this large data source requires different tools and processing, such as cloud computing and infrastructure. Participants will become familiar with data format and quality considerations, tools, and techniques to process remote sensing imagery at large scale from publicly available satellite sources, using cloud tools such as AWS S3, Databricks, and Parquet. Additionally, participants will learn how to analyze and train machine learning models for classification using this large source of data to solve environmental problems with a focus on agriculture. Participants will have a basic understanding of tools such as Pyspark, TensorFlow, and Uber H3. We hope that participants in this course will walk away with the skills and tools to train algorithms using satellite imagery to solve environmental problems anywhere on the planet.