Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

Clemens Havas; Bernd Resch

doi:10.1007/s11069-021-04808-4

Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

Clemens Havas
, Bernd Resch

Game & Mixed Reality

Research output: Contribution to journal › Article › peer-review

Original language	Undefined/Unknown
Journal	Natural Hazards
DOIs	https://doi.org/10.1007/s11069-021-04808-4
Publication status	Published - 10 Sept 2021

Classification according to Österreichische Systematik der Wissenschaftszweige (ÖFOS 2012)

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10.1007/s11069-021-04808-4

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title = "Portability of semantic and spatial{\textendash}temporal machine learning methods to analyse social media for near-real-time disaster monitoring",

author = "Clemens Havas and Bernd Resch",

year = "2021",

month = sep,

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doi = "10.1007/s11069-021-04808-4",

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journal = "Natural Hazards",

issn = "0921-030X",

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T1 - Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

AU - Havas, Clemens

AU - Resch, Bernd

PY - 2021/9/10

Y1 - 2021/9/10

UR - https://doi.org/10.1007/s11069-021-04808-4

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DO - 10.1007/s11069-021-04808-4

M3 - Artikel

SN - 0921-030X

JO - Natural Hazards

JF - Natural Hazards

ER -

Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

Classification according to Österreichische Systematik der Wissenschaftszweige (ÖFOS 2012)

Applied Research Level (ARL)

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