Annotating very high-resolution satellite imagery: A whale case study

Cubaynes, Hannah Charlotte ORCID: https://orcid.org/0000-0002-9497-154X; Clarke, Penny Joanna ORCID: https://orcid.org/0000-0002-2648-9639; Goetz, Kimberly Thea; Tyler, Aldrich; Fretwell, Peter Thomas ORCID: https://orcid.org/0000-0002-1988-5844; Leonard, Kathleen Elise; Khan, Christin Brangwynne. 2023 Annotating very high-resolution satellite imagery: A whale case study. MethodsX, 10, 102040. 10, pp. https://doi.org/10.1016/j.mex.2023.102040

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Abstract/Summary

The use of very high-resolution (VHR) optical satellites is gaining momentum in the field of wildlife monitoring, particularly for whales, as this technology is showing potential for monitoring the less studied regions. However, surveying large areas using VHR optical satellite imagery requires the development of automated systems to detect targets. Machine learning approaches require large training datasets of annotated images. Here we propose a standardised workflow to annotate VHR optical satellite imagery using ESRI ArcMap 10.8, and ESRI ArcGIS Pro 2.5., using cetaceans as a case study, to develop AI-ready annotations. • A step-by-step protocol to review VHR optical satellite images and annotate the features of interest. • A step-by-step protocol to create bounding boxes encompassing the features of interest. • A step-by-step guide to clip the satellite image using bounding boxes to create image chips.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.mex.2023.102040
ISSN:	22150161
Additional Keywords:	VHR optical satellite image, wildlife, cetacean, labelling, AI-ready data, machine learning
Date made live:	30 Jan 2023 11:10 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533499

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.mex.2023.102040

ISSN:

22150161

Additional Keywords:

VHR optical satellite image, wildlife, cetacean, labelling, AI-ready data, machine learning

Date made live:

30 Jan 2023 11:10 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/533499