Aligning vision and action: Learning Google Earth Engine for research

Abstract:

Researchers today need the ability to rapidly upgrade their skills as data and platforms evolve. The availability of big data, ability to process it and analyze the results has fundamentally changed the research process. Three major challenges exist. First is learning how, where and when to apply specific languages and platforms. Second, is reconfiguring which parameters are used to guide problem-formulation. And third, users need to gain confidence yet be cognizant of complexity and technical challenges. Currently, formal learning opportunities for learning platforms like Google Earth Engine which combines big data and cloud-computing are limited. This makes it more difficult for early-career and established researchers to learn methodologies that are quickly becoming the norm in earth and environmental science. Most will cobble together existing resources including coursework that is available online, the developers guide, the help forum and lectures available as part of the user summits hosted by Google. In order to supplement this process, a three-part workshop series consisting of three-hour sessions was hosted over the fall, winter and spring of 2019/20. The workshop was designed in order to quickly introduce the key concepts of geospatial data science and apply these concepts in a platform like Google Earth Engine. It was delivered both in-person and remotely. The first workshop focused on how to effectively formulate problems by finding data, examining it and synthesizing results. Next, concepts including functions and models were examined. Finally, the emphasis was on visualization and culminated in building the app introduced in the first workshop. Lessons learned include the need to meet the demand for training which bridges the gap between a short-introductory workshop and a full-length semester long class. From an instructional point of view, this format requires balancing users having some prior knowledge of geospatial analysis with understanding which building blocks will give participants the most utility. Advancing the use of these tools within the research setting benefits from a variety of pathways to navigate learning these skills.