Spatial Data Reduction Through Element-of-Interest (EOI) Extraction

Samantha T. Arundel () and E. Lynn Usery
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Samantha T. Arundel: Center of Excellence for Geospatial Information Science, U.S. Geological Survey
E. Lynn Usery: Center of Excellence for Geospatial Information Science, U.S. Geological Survey

Chapter Chapter 5 in Handbook of Big Geospatial Data, 2021, pp 119-134 from Springer

Abstract: Abstract Any large, multifaceted data collection that is challenging to handle with traditional management practices can be branded ‘Big Data.’ Any big data containing geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data domain can be summarized as the ‘Big Vs’ – variety, volume, velocity, veracity and value. Big spatial data sources can be considered in two broad classes, active and passive, as each is impacted to varying degrees. Some of these challenges may be alleviated by reducing unprocessed, or minimally processed, (raw) data to features, which we refer to as the extraction of Elements of Interest (EOI). In fact, many applications require EOI extraction from raw data to enable their basic employment. This chapter presents current state-of-the-art methods to create EOI from some types of georeferenced big data. We classify the data types into two realms: active and passive. Active data are those collected specifically for the purpose to which they are applied. Passive data are those collected for purposes other than those for which they are utilized, included those ‘collected’ for no particular purpose at all. The chapter then presents use cases from both the active and passive spatial realms, including the active applications of terrain feature extraction from digital elevation models and vegetation mapping from remotely-sensed imagery and passive applications like building identification from VGI and point-of-interest data mining from social networks for land use classification. Finally, the chapter concludes with future research needs.

Keywords: Data reduction; Feature extraction remote sensing; Big spatial data; Object-based image analysis; Spatial machine learning (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-55462-0_5

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