A picture can be worth a thousand words and this describes the data necessary to identify and track global land cover change. Citizen science apps and social media are generating enormous amounts of…

This federal contract award, valued at $99,677.44, was issued by the Shared Services Center (SSC) of the National Aeronautics and Space Administration (NASA) to New Mexico State University (NMSU) under a Cooperative Agreement. The contract aims to explore and create machine learning (ML) tools to segment, characterize, classify, and label components of land cover photographs from the Globe Observer (GO) Land Cover app. This effort will involve data preparation, workshop design, and collaboration through Jupyter notebooks to support the understanding of Earth's physical processes and natural phenomena using citizen science data. The contract has no set-aside designation and is not associated with a larger contract vehicle. NMSU, a Hispanic-serving, land-grant research university, has experience in providing diverse research, technical assistance, and educational services to federal agencies across various disciplines, including space exploration, environmental sciences, and STEM education.

A picture can be worth a thousand words and this describes the data necessary to identify and track global land cover change. Citizen science apps and social media are generating enormous amounts of data in the form of photographs uploaded to the cloud. Photographs submitted via the globe observer APP provide the link between space and earth, fostering an understanding of earth s physical processes and natural phenomenon. The volume of photographs is outpacing human ability to organize, characterize, evaluate and classify data for search, comparison, change detection, and prediction. This is where the hack comes in. This proposal will include a year of activities to explore and create machine learning (ML) tools to segment, characterize, classify, and label components of land cover photographs from the globe observer (go) land cover APP. Data preparation chooses image replicates from the globe observer data set (e.g., download images, identify locations in close proximity over a range of ecosystems). Preparation of workshops includes designing outreach and application materials, and completing the IRB training. Designing workshops starts with preparing data and other resources for collaboration, practicing go activities, recording video and creating homework assignments. Organizing data and sample locations will involve collaboration through jupyter notebooks. Design of social components for daily schedules includes meet-and-greet, physical exercise, breaks, games, and meals.