Fundamentals of Machine Learning for Earth Science

Artificial intelligence and machine learning have grown in popularity in recent decades as a result of advances in high-performance computing and open-source software. At the core, machine learning provides a statistical inference based on the inputs provided by the user, in which algorithms learn relationships between input data and output results. The complexity of these algorithms allows for the discovery of patterns and trends invisible to the human analyst, making it important to create analysis-appropriate input for these models to ensure that they answer the questions we are asking. This training will provide attendees an overview of machine learning in regards to Earth Science, and how to apply these algorithms and techniques to remote sensing data in a meaningful way. Attendees will also be provided with end-to-end case study examples for generating a simple random forest model for land cover classification from optical remote sensing. We will also present additional case studies to apply the presented workflows using additional NASA data.

Relevant UN Sustainable Development Goals:
• Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture
• Goal 13: Take urgent action to combat climate change and its impacts
• Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development
• Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss

Course Dates: April 20, 27, and May 4, 2023