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

Cubaynes, H.C.; Clarke, P.J.; Goetz, K.T.; Aldrich, T.; Fretwell, P.T.; Leonard, K.E.; Khan, C.B.

doi:10.1016/j.mex.2023.102040

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

2023
By Cubaynes, H.C. ; Clarke, P.J. ; Goetz, K.T. ; ...
Source: MethodsX 10 (2023) 102040

[PDF-1.86 MB]

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Journal Title:

MethodsX
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Cubaynes, H.C. ; Clarke, P.J. ; Goetz, K.T. ; Aldrich, T. ; Fretwell, P.T. ; ... More +

Cubaynes, H.C. ; Clarke, P.J. ; Goetz, K.T. ; Aldrich, T. ; Fretwell, P.T. ; Leonard, K.E. ; Khan, C.B. Less -
NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; NEFSC (Northeast Fisheries Science Center) ; AFSC (Alaska Fisheries Science Center) ; AKR (Alaska Region)

NMFS (National Marine Fisheries Service) ; NEFSC (Northeast Fisheries Science Center) ; AFSC (Alaska Fisheries Science Center) ; AKR (Alaska Region) Less -
Description:

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.
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Cetacea Machine Learning AI-ready Data Labeling VHR Optical Satellite Image Wildlife
Source:

MethodsX 10 (2023) 102040
DOI:

https://doi.org/10.1016/j.mex.2023.102040
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urn:sha256:c689c2455a3980d68d73e601d83e66f9bcc6939a83b320100a9dbedec7aad74b
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