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AM-94 - Machine Learning Approaches

Machine learning approaches are increasingly used across numerous applications in order to learn from data and generate new knowledge discoveries, advance scientific studies and support automated decision making. In this knowledge entry, the fundamentals of Machine Learning (ML) are introduced, focusing on how feature spaces, models and algorithms are being developed and applied in geospatial studies. An example of a ML workflow for supervised/unsupervised learning is also introduced. The main challenges in ML approaches and our vision for future work are discussed at the end.

DM-79 - U.S. National Spatial Data Infrastructure

Spatial data infrastructures may be thought of as socio-technical frameworks for coordinating the development, management, sharing and use of geospatial data across multiple organizational jurisdictions and varying geographic extents. The United States was an early adopter of the SDI concept and the U.S. National Spatial Data Infrastructure (NSDI) is an example of a country-wide SDI implementation facilitated by coordination at the federal-government level. At the time of its establishment in the early 1990s, a unique characteristic of the NSDI was a mandate for federal agencies to establish partnerships with state- and local-level government. This entry summarizes the origins of the NSDI’s establishment, its original core components and how they’ve evolved over the last 25 years, the role of the Federal Geographic Data Committee (FGDC), and the anticipated impact of passage of the Geospatial Data Act of 2018. For broader technical information about SDIs, readers are referred to GIST BoK Entry DM-60: Spatial Data Infrastructures (Hu and Li 2017). For additional details on the history of the NSDI, readers are referred to Rhind (1999). For the latest information on recent and emerging NSDI initiatives, please visit the FGDC web site (