Data

Benthic substrate classification ML training image snips

Commonwealth Scientific and Industrial Research Organisation
Jackett, Chris ; Maguire, Kylie ; Untiedt, Candice ; Althaus, Franzis ; Shanks, Peter
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/jfsp-zd42&rft.title=Benthic substrate classification ML training image snips&rft.identifier=https://doi.org/10.25919/jfsp-zd42&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=Collection of images labelled into substrate classes for training machine learning algorithms. \nLineage: Benthic Stereo still imagery was collected during the MNF Investigator voyage IN2018_V06, sampling seamounts off Tasmania. Random points (5per m2) were annotated within measured quadrats for substrate classes including matrix-forming stony coral (Metadata description in Marlin). Snips centred on these random points were extracted from the original images, and associcated with their relevant substrate class label.&rft.creator=Jackett, Chris &rft.creator=Maguire, Kylie &rft.creator=Untiedt, Candice &rft.creator=Althaus, Franzis &rft.creator=Shanks, Peter &rft.date=2023&rft.edition=v2&rft.coverage=westlimit=146.3717; southlimit=-43.81966666666667; eastlimit=148.6322; northlimit=-41.03; projection=WGS84&rft_rights=Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence https://creativecommons.org/licenses/by-nc-sa/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2023.&rft_subject=Imagery&rft_subject=Machine learning&rft_subject=Annotation&rft_subject=Training data&rft_subject=Label&rft_subject=Substrate&rft_subject=Coral&rft_subject=Image classification&rft_subject=Solenosmilia variabilis&rft_subject=Artificial intelligence not elsewhere classified&rft_subject=Artificial intelligence&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=Machine learning not elsewhere classified&rft_subject=Machine learning&rft.type=dataset&rft.language=English Access the data
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CC-BY-NC-SA

Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence
https://creativecommons.org/licenses/by-nc-sa/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2023.

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Brief description

Collection of images labelled into substrate classes for training machine learning algorithms.
Lineage: Benthic Stereo still imagery was collected during the MNF Investigator voyage IN2018_V06, sampling seamounts off Tasmania. Random points (5per m2) were annotated within measured quadrats for substrate classes including matrix-forming stony coral (Metadata description in Marlin). Snips centred on these random points were extracted from the original images, and associcated with their relevant substrate class label.

Available: 2023-03-29

Data time period: 2018-12-11 to 2022-04-29

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148.6322,-41.03 148.6322,-43.81967 146.3717,-43.81967 146.3717,-41.03 148.6322,-41.03

147.50195,-42.424833333333

Subjects
Annotation | Artificial Intelligence | Artificial Intelligence Not Elsewhere Classified | Coral | Information and Computing Sciences | Image classification | Imagery | Label | Machine Learning | Machine learning | Machine Learning Not Elsewhere Classified | Solenosmilia variabilis | Substrate | Training data |

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