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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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 |
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Data Study Groups The Alan Turing Institute Remote sensing Antarctic Climate change Classifying Automation Satellite Neural networks Computer vision |
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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 |
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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 |
_version_ |
1810490561105231872 |