Remote sensing-based maps are essential for understanding Earth processes. All maps contain errors, yet some errors are less important than others. Ground reference information also has errors. As a result, rigorous map quality assessment and statistical inference from mapped products remain foundational challenges for both producers and users of remote sensing data. This session solicits contributions that advance the theory, methods, best practices, applications of map accuracy & uncertainty characterization, and statistical inference from maps. We welcome submissions spanning methodological developments, community standards and workflows, and case studies demonstrating how map uncertainty propagates into downstream estimates, decisions, and conclusions. Topics of interest include: novel approaches to map quality assessment; combining probability with non-probability samples; advances in model-assisted and model-based inference from maps; small area estimation; efficiency and cost-benefit assessment of satellite data; uncertainty of reference information; sensitivity to spatial & temporal resolutions; assessment of change during a time series.

• B - Biogeosciences
• IN - Informatics