BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Geolunch seminar @ UiB - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://geolunch.w.uib.no
X-WR-CALDESC:Events for Geolunch seminar @ UiB
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Oslo
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Oslo:20250409T121500
DTEND;TZID=Europe/Oslo:20250409T130000
DTSTAMP:20260419T213020
CREATED:20250407T071615Z
LAST-MODIFIED:20250407T083536Z
UID:1057-1744200900-1744203600@geolunch.w.uib.no
SUMMARY:Julien Vadnais
DESCRIPTION:Deep transfer learning for mapping rare Arctic oil seeps observed from satellites \nJulien Vadnais – Department of Earth Science\, UiB \n  \nAbstract \nNatural seepage is a significant contributor to marine oil inputs. Remote and intermittent seeps are difficult to monitor in the field\, yet oil slicks can be observed by spaceborne synthetic aperture radar (SAR) because they reduce backscatter\, creating potential for automatic mapping. In mapping tasks like segmentation\, deep learning models excel\, albeit needing large amounts of labeled images. To deal with scarcity of labeled images\, transfer learning is an approach commonly used in computer vision\, though still underutilized in remote sensing. \nDifferent Sentinel-1 acquisition modes\, in the Arctic compared to elsewhere\, complicate direct model transfer for oil slick mapping. Here\, we present a use-case where transfer learning enhances the segmentation of natural oil slicks\, most notably in challenging and noisy images. Our method proves efficient even with limited training images\, offering new prospects for studying poorly understood or yet undiscovered hydrocarbon seeps.
URL:https://geolunch.w.uib.no/event/julien-vadnais/
LOCATION:Kontinentalsokkelen (2G16e)\, Realfagbygget\, Allégaten 41\, Bergen\, 5007\, Norway
END:VEVENT
END:VCALENDAR