Using Deep Learning to Count Albatrosses from Space

Ellen Bowler, Peter Fretwell, Geoffrey French, Michal Mackiewicz

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)
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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 interobserver 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.
Original languageEnglish
Number of pages4
Publication statusPublished - 14 Nov 2019
EventIEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019


ConferenceIEEE International Geoscience and Remote Sensing Symposium

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