Using Deep Learning to Count Albatrosses from Space
In this paper we test the use of a deep learning approach to automatically count Wandering Albatrosses in Very High Resolution (VHR) satellite imagery. We use a dataset of manually labelled imagery provided by the British Antarctic Survey to train and develop our methods. We employ a U-Net architect...
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ftdatacite:10.48550/arxiv.1907.02040 2023-05-15T13:34:05+02:00 Using Deep Learning to Count Albatrosses from Space Bowler, Ellen Fretwell, Peter T. French, Geoffrey Mackiewicz, Michal 2019 https://dx.doi.org/10.48550/arxiv.1907.02040 https://arxiv.org/abs/1907.02040 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Article CreativeWork article Preprint 2019 ftdatacite https://doi.org/10.48550/arxiv.1907.02040 2022-03-10T16:41:39Z In this paper we test the use of a deep learning approach to automatically count Wandering Albatrosses in Very High Resolution (VHR) satellite imagery. We use a dataset of manually labelled imagery provided by the British Antarctic Survey to train and develop our methods. We employ a U-Net architecture, designed for image segmentation, to simultaneously classify and localise potential albatrosses. We aid training with the use of the Focal Loss criterion, to deal with extreme class imbalance in the dataset. Initial results achieve peak precision and recall values of approximately 80%. Finally we assess the model's performance in relation to inter-observer variation, by comparing errors against an image labelled by multiple observers. We conclude model accuracy falls within the range of human counters. We hope that the methods will streamline the analysis of VHR satellite images, enabling more frequent monitoring of a species which is of high conservation concern. : 4 pages, 5 figures, to be presented at IEEE 2019 International Geoscience & Remote Sensing Symposium (IGARSS 2019), scheduled for July 28 - August 2, 2019 Article in Journal/Newspaper Antarc* Antarctic British Antarctic Survey DataCite Metadata Store (German National Library of Science and Technology) Antarctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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unknown |
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Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
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Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Bowler, Ellen Fretwell, Peter T. French, Geoffrey Mackiewicz, Michal Using Deep Learning to Count Albatrosses from Space |
topic_facet |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
description |
In this paper we test the use of a deep learning approach to automatically count Wandering Albatrosses in Very High Resolution (VHR) satellite imagery. We use a dataset of manually labelled imagery provided by the British Antarctic Survey to train and develop our methods. We employ a U-Net architecture, designed for image segmentation, to simultaneously classify and localise potential albatrosses. We aid training with the use of the Focal Loss criterion, to deal with extreme class imbalance in the dataset. Initial results achieve peak precision and recall values of approximately 80%. Finally we assess the model's performance in relation to inter-observer variation, by comparing errors against an image labelled by multiple observers. We conclude model accuracy falls within the range of human counters. We hope that the methods will streamline the analysis of VHR satellite images, enabling more frequent monitoring of a species which is of high conservation concern. : 4 pages, 5 figures, to be presented at IEEE 2019 International Geoscience & Remote Sensing Symposium (IGARSS 2019), scheduled for July 28 - August 2, 2019 |
format |
Article in Journal/Newspaper |
author |
Bowler, Ellen Fretwell, Peter T. French, Geoffrey Mackiewicz, Michal |
author_facet |
Bowler, Ellen Fretwell, Peter T. French, Geoffrey Mackiewicz, Michal |
author_sort |
Bowler, Ellen |
title |
Using Deep Learning to Count Albatrosses from Space |
title_short |
Using Deep Learning to Count Albatrosses from Space |
title_full |
Using Deep Learning to Count Albatrosses from Space |
title_fullStr |
Using Deep Learning to Count Albatrosses from Space |
title_full_unstemmed |
Using Deep Learning to Count Albatrosses from Space |
title_sort |
using deep learning to count albatrosses from space |
publisher |
arXiv |
publishDate |
2019 |
url |
https://dx.doi.org/10.48550/arxiv.1907.02040 https://arxiv.org/abs/1907.02040 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic British Antarctic Survey |
genre_facet |
Antarc* Antarctic British Antarctic Survey |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1907.02040 |
_version_ |
1766048768516423680 |