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2021 | OriginalPaper | Buchkapitel

19. Harvesting Big Geospatial Data from Natural Language Texts

verfasst von : Yingjie Hu, Benjamin Adams

Erschienen in: Handbook of Big Geospatial Data

Verlag: Springer International Publishing

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Abstract

A vast amount of geospatial data exists in natural language texts, such as newspapers, Wikipedia articles, social media posts, travel blogs, online reviews, and historical archives. Compared with more traditional and structured geospatial data, such as those collected by the US Geological Survey and the national statistics offices, geospatial data harvested from these unstructured texts have unique merits. They capture valuable human experiences toward places, reflect near real-time situations in different geographic areas, or record important historical information that is otherwise not available. In addition, geospatial data from these unstructured texts are often big, in terms of their volume, velocity, and variety. This chapter presents the motivations of harvesting big geospatial data from natural language texts, describes typical methods and tools for doing so, summarizes a number of existing applications, and discusses challenges and future directions.

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Vorheriges Kapitel Big Historical Geodata for Urban and Environmental Research

Nächstes Kapitel Automating Information Extraction from Large Historical Topographic Map Archives: New Opportunities and Challenges

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Titel: Harvesting Big Geospatial Data from Natural Language Texts
verfasst von: Yingjie Hu
Benjamin Adams
Verlag: Springer International Publishing
Buch: Handbook of Big Geospatial Data
Print ISBN: 978-3-030-55461-3

Electronic ISBN: 978-3-030-55462-0

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-55462-0_19

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