Case Studies of Landsat Users in Machine Learning and AI: Mapping the Benefits of EO Data in a Data Ecosystem

Abstract:

As a free and open data source, the Landsat archive provides numerous benefits to data users. In recent years, social scientists have used economic valuation methods to quantify the dollar value of Landsat imagery, but these studies do not capture the depth and variation of specific use cases or non-economic measures of societal benefits important to many users. The use of qualitative research methods, including semi-structured interviews and case studies, offers a complementary approach to understanding the users, uses, and value of Landsat data. Communication of societal benefits to stakeholders, decision-makers, and members of the public is also enhanced through the narrative nature of case study research. This presentation will summarize recent case studies of Landsat data users and uses from the private, nonprofit, and academic sectors. Specific examples of Landsat data use for machine learning/artificial intelligence (ML/AI) applications will be emphasized, particularly in the context of recent advances in cloud computing, storage, and analytics. The presentation will conclude with an overview of ongoing efforts to map the flow of Landsat data through the Earth observation “data ecosystem”, using case studies to highlight the actors, infrastructural components, and governance mechanisms critical to value generation in the Earth observation sector.