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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.
By the end of this training attendees will be able to:
Research and applied scientists interested in learning how to apply basic machine learning techniques to Earth science data (e.g., satellite data such as MODIS, Landsat, Sentinel-2, etc.).