Evidence map›Paper›PMID 36793672›Full record

ArticleMethodsX2023

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

Hannah Charlotte Cubaynes, Penny Joanna Clarke, Kimberly Thea Goetz, Tyler Aldrich, Peter Thomas Fretwell, Kathleen Elise Leonard, Christin Brangwynne Khan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MethodsX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hannah Charlotte CubaynesBritish Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, United Kingdom.
Penny Joanna ClarkeBritish Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, United Kingdom.
Kimberly Thea GoetzMarine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, Washington, United States.
Tyler AldrichNortheast Fisheries Science Center, National Marine Fisheries Service, NOAA, Woods Hole, MA, United States.
Peter Thomas FretwellBritish Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, United Kingdom.
Kathleen Elise LeonardProtected Resources Division, Alaska Regional Office, National Marine Fisheries Service, NOAA, Anchorage, AK, United States.
Christin Brangwynne KhanNortheast Fisheries Science Center, National Marine Fisheries Service, NOAA, Woods Hole, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AI-ready dataCetaceanLabelingMachine learningSatellite image annotation to create point, bounding boxes and image datasets to train automated systems.VHR optical satellite imageWildlife

Identifiers

PMID36793672
PMCPMC9923222

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.