Data Study Group Final Report: British Antarctic Survey

Data Study Groups are week-long events at The Alan Turing Institutebringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Seals from space: automated Antarctic ecosystem monitoring via high-resol...

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Main Author: Data Study Group team
Format: Report
Language:English
Published: Zenodo 2020
Subjects:
Online Access:https://doi.org/10.5281/zenodo.3670637
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spelling ftzenodo:oai:zenodo.org:3670637 2024-09-15T17:43:32+00:00 Data Study Group Final Report: British Antarctic Survey Data Study Group team 2020-02-17 https://doi.org/10.5281/zenodo.3670637 eng eng Zenodo https://doi.org/10.5281/zenodo.3670636 https://doi.org/10.5281/zenodo.3670637 oai:zenodo.org:3670637 info:eu-repo/semantics/openAccess Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode Data Study Groups The Alan Turing Institute Remote sensing Antarctic Climate change Classifying Automation Satellite Neural networks Computer vision info:eu-repo/semantics/report 2020 ftzenodo https://doi.org/10.5281/zenodo.367063710.5281/zenodo.3670636 2024-07-25T13:03:27Z Data Study Groups are week-long events at The Alan Turing Institutebringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Seals from space: automated Antarctic ecosystem monitoring via high-resolution satellite imagery The Antarctic is constantly evolving as the ecosystem recovers from past exploitation (e.g. whaling, seal harvesting), adapts to climate change, and responds to current anthropogenic impacts including fishing (krill and Patagonian toothfish), shipping and tourism. Due to its vastness, relatively little is known about the ecology of the region and its species, and how best to mitigate and control anthropogenic impacts in this region. Traditional field methods are costly and limited in geographical extent due to areas of interest being difficult to access by ships. Remote sensing provides a low-cost, non-invasive method that can be used for ecological monitoring. The overall aim of the challenge was to create an automated system for classifying sea ice and mapping seals which can then be used to transform the satellite raster images into images with vectorised features of ice and seals. Using these two outputs (seal counts and ice classification/environmental features) we can explore ecological questions such as what habitat features do seal prefer and how is the habitat changing over time. This report presents the outputs of a week-long collaboration between the Alan Turing Institute and the British Antarctic Survey (BAS), to scope an automated system to classify sea ice, count seals, and explore the environmental factors influencing seal density. Report Antarc* Antarctic British Antarctic Survey Patagonian Toothfish Sea ice Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
topic Data Study Groups
The Alan Turing Institute
Remote sensing
Antarctic
Climate change
Classifying
Automation
Satellite
Neural networks
Computer vision
spellingShingle Data Study Groups
The Alan Turing Institute
Remote sensing
Antarctic
Climate change
Classifying
Automation
Satellite
Neural networks
Computer vision
Data Study Group team
Data Study Group Final Report: British Antarctic Survey
topic_facet Data Study Groups
The Alan Turing Institute
Remote sensing
Antarctic
Climate change
Classifying
Automation
Satellite
Neural networks
Computer vision
description Data Study Groups are week-long events at The Alan Turing Institutebringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Seals from space: automated Antarctic ecosystem monitoring via high-resolution satellite imagery The Antarctic is constantly evolving as the ecosystem recovers from past exploitation (e.g. whaling, seal harvesting), adapts to climate change, and responds to current anthropogenic impacts including fishing (krill and Patagonian toothfish), shipping and tourism. Due to its vastness, relatively little is known about the ecology of the region and its species, and how best to mitigate and control anthropogenic impacts in this region. Traditional field methods are costly and limited in geographical extent due to areas of interest being difficult to access by ships. Remote sensing provides a low-cost, non-invasive method that can be used for ecological monitoring. The overall aim of the challenge was to create an automated system for classifying sea ice and mapping seals which can then be used to transform the satellite raster images into images with vectorised features of ice and seals. Using these two outputs (seal counts and ice classification/environmental features) we can explore ecological questions such as what habitat features do seal prefer and how is the habitat changing over time. This report presents the outputs of a week-long collaboration between the Alan Turing Institute and the British Antarctic Survey (BAS), to scope an automated system to classify sea ice, count seals, and explore the environmental factors influencing seal density.
format Report
author Data Study Group team
author_facet Data Study Group team
author_sort Data Study Group team
title Data Study Group Final Report: British Antarctic Survey
title_short Data Study Group Final Report: British Antarctic Survey
title_full Data Study Group Final Report: British Antarctic Survey
title_fullStr Data Study Group Final Report: British Antarctic Survey
title_full_unstemmed Data Study Group Final Report: British Antarctic Survey
title_sort data study group final report: british antarctic survey
publisher Zenodo
publishDate 2020
url https://doi.org/10.5281/zenodo.3670637
genre Antarc*
Antarctic
British Antarctic Survey
Patagonian Toothfish
Sea ice
genre_facet Antarc*
Antarctic
British Antarctic Survey
Patagonian Toothfish
Sea ice
op_relation https://doi.org/10.5281/zenodo.3670636
https://doi.org/10.5281/zenodo.3670637
oai:zenodo.org:3670637
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.367063710.5281/zenodo.3670636
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