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Frontiers in Computer Science
Article . 2021
License: CC BY
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Frontiers in Computer Science
Article
License: CC BY
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https://doi.org/10.48550/arxiv...
Article . 2021
License: arXiv Non-Exclusive Distribution
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Ice Core Science Meets Computer Vision: Challenges and Perspectives

Authors: Pascal Bohleber; Marco Roman; Carlo Barbante; Carlo Barbante; Sebastiano Vascon; Sebastiano Vascon; Kaleem Siddiqi; +2 Authors

Ice Core Science Meets Computer Vision: Challenges and Perspectives

Abstract

Polar ice cores play a central role in studies of the earth's climate system through natural archives. A pressing issue is the analysis of the oldest, highly thinned ice core sections, where the identification of paleoclimate signals is particularly challenging. For this, state-of-the-art imaging by laser-ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) has the potential to be revolutionary due to its combination of micron-scale 2D chemical information with visual features. However, the quantitative study of record preservation in chemical images raises new questions that call for the expertise of the computer vision community. To illustrate this new inter-disciplinary frontier, we describe a selected set of key questions. One critical task is to assess the paleoclimate significance of single line profiles along the main core axis, which we show is a scale-dependent problem for which advanced image analysis methods are critical. Another important issue is the evaluation of post-depositional layer changes, for which the chemical images provide rich information. Accordingly, the time is ripe to begin an intensified exchange among the two scientific communities of computer vision and ice core science. The collaborative building of a new framework for investigating high-resolution chemical images with automated image analysis techniques will also benefit the already wide-spread application of LA-ICP-MS chemical imaging in the geosciences.

Comment: 9 pages, 2 figures, submitted to Frontiers in Computer Science, section Computer Vision

Country
Italy
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Natural (archaeology) Task (project management) Ice core Paleoclimatology Computer vision Set (psychology) business.industry Single line Identification (information) Key (cryptography) Artificial intelligence business

Keywords

FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, FOS: Physical sciences, Physics - Geophysics, image analysis, laser ablation inductively-coupled plasma mass spectrometry, Settore CHIM/01 - Chimica Analitica, chemical imaging, ice core analysis; chemical imaging; laser ablation inductively-coupled plasma mass spectrometry; image analysis; scale spaces; co-localization; grain boundaries, Settore INF/01 - Informatica, co-localization, grain boundaries, QA75.5-76.95, Geophysics (physics.geo-ph), ice core analysis, Settore GEO/08 - Geochimica e Vulcanologia, Electronic computers. Computer science, scale spaces

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    Powered byBIP!BIP!
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Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Average
Funded by
NSERC
Project
  • Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
,
EC| Beyond EPICA
Project
Beyond EPICA
Beyond EPICA Oldest Ice Core: 1,5 Myr of greenhouse gas – climate feedbacks
  • Funder: European Commission (EC)
  • Project Code: 815384
  • Funding stream: H2020 | RIA
iis
,
EC| GOLD-ICE
Project
GOLD-ICE
Next generation analysis of the oldest ice core layers
  • Funder: European Commission (EC)
  • Project Code: 790280
  • Funding stream: H2020 | MSCA-IF-EF-ST
iis
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