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Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
13.08.2024
Verantwortlich:
Li, Hao
Autorinnen / Autoren:
Li, Hao; Deuser, Fabian; Yin, Wenping; Luo, Xuanshu; Walther, Paul; Mai, Gengchen; Huang, Wei; Werner, Martin
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Titel:
CVIAN: Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery for Hurrican IAN
Identifikator:
doi:10.14459/2024mp1749324
Enddatum der Datenerzeugung:
30.06.2024
Fachgebiet:
BAU Bauingenieurwesen, Vermessungswesen; DAT Datenverarbeitung, Informatik; GEO Geowissenschaften
Quellen der Daten:
Experimente und Beobachtungen / experiments and observations; Statistik und Referenzdaten / statistics and reference data
Datentyp:
Bilder / images
Methode der Datenerhebung:
The dataset was processed using a geospatial data processing engine in order to curate the origin dataset into meaningful cross-view imagery pairs. Moreover, the damage perception reference data is manually collected based on on a list of quantifiable and disaster-related indicators.
Beschreibung:
CVIAN is a cross-view dataset to support geolocalization and disaster mapping with street-view and very high resolution (VHR) satellite imagery in Florida, USA after Hurricane IAN in 2022. CVIAN contains 4,121 pairs of street-view and VHR satellite imagery, which are manually classified into 3 classes (i.e., light, medium, and heavy damage). The VHR satellite imagery was originally provided by the National Oceanic and Atmospheric Administration (NOAA) at a spatial resolution of 30cm per pixel on...     »
Schlagworte:
Cross-view; Disaster Response; GeoAI; Street-view Imagery; Geolocalization
Technische Hinweise:
View and download (2,09 GB total, 8256 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1749324):
rsync rsync://m1749324@dataserv.ub.tum.de/m1749324/
Sprache:
en
Rechte:
by, http://creativecommons.org/licenses/by/4.0
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