Skip to main content
U.S. flag

An official website of the United States government

Spatial data reduction through element -of-interest (EOI) extraction

May 8, 2021

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.

Publication Year 2021
Title Spatial data reduction through element -of-interest (EOI) extraction
DOI 10.1007/978-3-030-55462-0_5
Authors Samantha Arundel, E. Lynn Usery
Publication Type Book Chapter
Publication Subtype Book Chapter
Index ID 70220508
Record Source USGS Publications Warehouse
USGS Organization National Geospatial Program; Center for Geospatial Information Science (CEGIS)