Drones and deep learning produce accurate and efficient monitoring of large-scale colonies of black browed albatross and southern rockhopper penguins on Steeple Jason. Field work 2018 - 2019. Publication 2022

Photographs obtained using drones in automated flight covered entire colony areas of black browed albatross and southern rockhopper penguins on Grand and Steeple Jason over two seasons were analysed using convolutional neural networks (CNN) to detect and enumerate individuals of both species for population assessment. Results showed accuracies for detecting and counting birds at 97.66% (Black browed albatross) and 87.16% (southern rockhopper penguin), with 90% of automated counts being within 5% of manual counts from imagery, clearly demonstrating the value of these techniques for seabird monitoring in large colonies.

Data and Resources

This dataset has no data

Additional Info

Field Value
Last Updated September 3, 2024, 02:41 (PDT)
Created September 3, 2024, 02:38 (PDT)
Region Falkland Islands
Language eng
Topic Category Biota; flora and/or fauna in natural environment
Temporal Extent Start 2018-11-01
Temporal Extent End 2019-11-30
Dataset Reference Date 2021
Lineage Aerial photography: Phantom 4 Pro equiped with Camera: 1-inch 20MP CMOS sensor; automated flight pathway using Drone Deploy software. Analysis using CNN: Keras implementation of the one-stage RetinaNet object detection architecture with the ResNet-50-FPN backbone.
West Longitude -61.2530
South Latitude -61.0686
East Longitude -54.0786
North Latitude -51.0127
Spatial Reference System WGS84
Responsible Organisation Name Duke university - https://www.duke.edu - Duke Marine Robotics and Remote Sensing Lab https://MarineUAS.net -https://www.wcs.org/
Contact Mail Address david.johnston@Duke.edu
Responsible Party Role WCS: originator; owner / Duke university: procesor; author; custodian
Access Limitations Open access
Use Constraints Open, but copyright and/or Intellectual Property Rights apply
Resource Reference Falkland Islands Government Research Licence No: R28/2019.
Data Format jpeg
Update Frequency Quinquennial
Accuracy 1 pixel equvalent to between 0.48cm to 5cm on the ground
Resource Type Format of compiled data: 12 orthomosaics (size ranging from 400MB to 3.6GB) stitched using Pix4Dmapper software.
Original Title Hidden (internal use only)
Metadata Date 2024-08-19
Metadata Point of Contact datamanager@saeri.ac.fk
Contact Consent Contact details published
Research Permit Application ID Hidden (internal use only)

Dataset extent

Map Data by OpenStreetMap