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A temporal-ensembling active self-learning CNN framework for urban flood mapping by means of multi-temporal SAR intensity/coherence

Li, Yu and Martinis, Sandro and Wieland, Marc (2019) A temporal-ensembling active self-learning CNN framework for urban flood mapping by means of multi-temporal SAR intensity/coherence. In: ESA Living Planet Symposium. ESA Living Planet Symposium, 13.-15.05.2019, Mailand, Italien.

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Official URL: https://lps19.esa.int/NikalWebsitePortal/living-planet-symposium-2019/lps19/Agenda/AgendaItemDetail?id=c04de204-147a-438f-8758-7c4c52609e09


Item URL in elib:https://elib.dlr.de/127520/
Document Type:Conference or Workshop Item (Speech)
Title:A temporal-ensembling active self-learning CNN framework for urban flood mapping by means of multi-temporal SAR intensity/coherence
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Li, Yuyu.li (at) dlr.deUNSPECIFIED
Martinis, Sandrosandro.martinis (at) dlr.dehttps://orcid.org/0000-0002-6400-361X
Wieland, Marcmarc.wieland (at) dlr.dehttps://orcid.org/0000-0002-1155-723X
Date:2019
Journal or Publication Title:ESA Living Planet Symposium
Refereed publication:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Floods, urban areas, TerraSAR-X, CNN
Event Title:ESA Living Planet Symposium
Event Location:Mailand, Italien
Event Type:international Conference
Event Dates:13.-15.05.2019
Organizer:ESA
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Martinis, Sandro
Deposited On:19 Jun 2019 09:24
Last Modified:19 Jun 2019 09:24

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