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We’ve collated a series of publications on Aerial Imagery Surveys, Processing Aerial Imagery Using Deep Learning Models, and Reports. We will be adding to this list regularly and welcome any suggestions.

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Aerial Imagery Surveys

Barnas AF, Chabot D, Hodgson AJ, Johnston DW, Bird DM, and Ellis-Felege SN. 2020. A standardized protocol for reporting methods when using drones for wildlife research. Journal of Unmanned Vehicle Systems 8:89-98. https://doi.org/10.1139/juvs-2019-0011

Bigal E, Galili O, van Rijn I, Rosso M, Cleguer C, Hodgson A, Scheinin A, and Tchernov D. 2022. Reduction of Species Identification Errors in Surveys of Marine Wildlife Abundance Utilising Unoccupied Aerial Vehicles (UAVs). Remote Sensing 14. https://doi.org/10.3390/rs14164118

Brown AM, Allen SJ, Kelly N, and Hodgson AJ. 2023. Using Unoccupied Aerial Vehicles to estimate availability and group size error for aerial surveys of coastal dolphins. Remote Sensing in Ecology and Conservation 9:340-35. https://doi.org/10.1002/rse2.313

Chabot D, Hodgson AJ, Hodgson JC, and Anderson K. 2022. ‘Drone’: technically correct, popularly accepted, socially acceptable. Drone Systems and Applications 10:399-405. https://doi.org/10.1139/dsa-2022-0041

Cleguer C, Kelly N, Tyne J, Wieser M, Peel D, and Hodgson A. 2021. A Novel Method for Using Small Unoccupied Aerial Vehicles to Survey Wildlife Species and Model Their Density Distribution. Frontiers in Marine Science 8. https://doi.org/10.3389/fmars.2021.640338

Hodgson, A. J., Kelly, N., & Peel, D. 2023. Drone images afford more detections of marine wildlife than real-time observers during simultaneous large-scale surveys. PeerJ, 11: e16186. https://doi.org/10.7717/peerj.16186

Hodgson A, Peel D, and Kelly N. 2017. Unmanned aerial vehicles for surveying marine fauna: assessing detection probability. Ecological Applications 27:1253-1267. https://doi.org/10.1002/eap.1519

Hodgson AJ, Kelly N, and Peel D. 2013. Unmanned Aerial Vehicles (UAVs) for surveying marine fauna: a dugong case study. PLoS ONE. https://doi.org/10.1371/journal.pone.0079556

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Processing Aerial Imagery Using Deep Learning Models

Axford, D., Sohel, F., Vanderklift, M. A., & Hodgson, A. J. 2024. Collectively advancing deep learning for animal detection in drone imagery: Successes, challenges, and research gaps. Ecological Informatics, 83:102842. https://doi.org/https://doi.org/10.1016/j.ecoinf.2024.102842

Maire F, Mejias L, and Hodgson A. 2015. Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: A Deep Learning Approach. In: Pfahringer B, and Renz J, eds. AI 2015: Advances in Artificial Intelligence: Springer International Publishing, 379-385. http://dx.doi.org/10.1007/978-3-319-26350-2_33

Maire F, Mejias L, and Hodgson A. 2014. A convolutional neural network for automatic analysis of aerial imagery. In: Wang LW, Ogunbona P, and Li W, editors. 2014 International Conference on Digital lmage Computing: Techniques and Applications (DlCTA). 25-27 November 2014, Wollongong, New South Wales, Australia. p 1-8. https://doi.org/10.1109/DICTA.2014.7008084

Maire F, Mejias L, Hodgson A, and Duclos G. 2013. Detection of dugongs from unmanned aerial vehicles. Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan. p 2750-2756. https://doi.org/10.1109/IROS.2013.6696745

Mejias L, Duclos G, Hodgson A, and Maire F. 2013. Automated marine mammal detection from aerial imagery. Proceedings of OCEANS ’13 IEEE/MTS. San Diego, USA. p 1-5. https://doi.org/10.23919/OCEANS.2013.6741088

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Reports

Bigal E, Galili O, Cleguer C, Rosso M, Tchernow D, Hodgson A, and Scheinin AP. 2020. Species identification of dolphins in digital imagery from unmanned aerial vehicles. Report to the ACCOBAMS Secretariat. Monaco, France: ACCOBAMS.

Hodgson A, Cleguer C, Scheinin AP, Bigal E, and Galili O. 2020. Potential use of Unmanned Aerial Vehicles for megafauna monitoring in the ACCOBAMS Agreement Area: transitioning to the new technology. Unpublished report prepared by Murdoch University, Perth, Western Australia, and Morris Kahn Marine Research Station, Leon H. Charney School of Marine Sciences, University of Haifa, Israel, for ACCOBAMS Secretariat, June 2020. p 48.

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