Impromptu Crisis Mapping to Prioritize Emergency Response

To visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machine-learning classifier to analyze relevant tweets, and interactive maps to rank extracted situational information. The system was validated against data from two recent disasters in Italy.

Gespeichert in:

Autor*in:	Avvenuti, Marco [verfasserIn] Cresci, Stefano Vigna, Fabio Del Tesconi, Maurizio

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Earthquakes Data mining computing and social issues emergency response Knowledge based systems Semantics Emergency services data analysis visualization disaster management situational awareness crisis mapping Geology Data acquisition Information Artificial intelligence Mapping Italy

Systematik:	SA 3520

Übergeordnetes Werk:	Enthalten in: Computer - Los Alamitos, Calif. : IEEE Computer Soc. Publ. Off., 1970, 49(2016), 5, Seite 28-37
Übergeordnetes Werk:	volume:49 ; year:2016 ; number:5 ; pages:28-37

Links:	Volltext Link aufrufen

DOI / URN:	10.1109/MC.2016.134

Katalog-ID:	OLC1974547922

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container_start_page	28
author_browse	Avvenuti, Marco
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format_se	Aufsätze
author-letter	Avvenuti, Marco
doi_str_mv	10.1109/MC.2016.134
dewey-full	004
title_sort	impromptu crisis mapping to prioritize emergency response
title_auth	Impromptu Crisis Mapping to Prioritize Emergency Response
abstract	To visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machine-learning classifier to analyze relevant tweets, and interactive maps to rank extracted situational information. The system was validated against data from two recent disasters in Italy.
abstractGer	To visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machine-learning classifier to analyze relevant tweets, and interactive maps to rank extracted situational information. The system was validated against data from two recent disasters in Italy.
abstract_unstemmed	To visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machine-learning classifier to analyze relevant tweets, and interactive maps to rank extracted situational information. The system was validated against data from two recent disasters in Italy.
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container_issue	5
title_short	Impromptu Crisis Mapping to Prioritize Emergency Response
url	http://dx.doi.org/10.1109/MC.2016.134 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7469993
remote_bool	false
author2	Cresci, Stefano Vigna, Fabio Del Tesconi, Maurizio
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doi_str	10.1109/MC.2016.134
up_date	2024-07-04T04:32:53.305Z
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