A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA
Abstract The sustainable management of fisheries and aquaculture requires an understanding of how these activities interact with natural fish populations. GoPro cameras were used to collect an underwater video data set on and around shellfish aquaculture farms in an estuary in the NE Pacific from Ju...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02557-6 |
| _version_ | 1797578253338148864 |
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| author | Dara M. Farrell Bridget Ferriss Beth Sanderson Karl Veggerby Lauren Robinson Anusua Trivedi Shreyaan Pathak Sreya Muppalla Jane Wang Dan Morris Rahul Dodhia |
| author_facet | Dara M. Farrell Bridget Ferriss Beth Sanderson Karl Veggerby Lauren Robinson Anusua Trivedi Shreyaan Pathak Sreya Muppalla Jane Wang Dan Morris Rahul Dodhia |
| author_sort | Dara M. Farrell |
| collection | DOAJ |
| description | Abstract The sustainable management of fisheries and aquaculture requires an understanding of how these activities interact with natural fish populations. GoPro cameras were used to collect an underwater video data set on and around shellfish aquaculture farms in an estuary in the NE Pacific from June to August 2017 and June to August 2018 to better understand habitat use by the local fish and crab communities. Images extracted from these videos were labeled to produce a data set that is suitable for use in training computer vision models. The labeled data set contains 77,739 images sampled from the collected video; 67,990 objects (fishes and crustaceans) have been annotated in 30,384 images (the remainder have been annotated as “empty”). The metadata of the data set also indicates whether a physical magenta filter was used during video collection to counteract reduced visibility. These data have the potential to help researchers address system-level and in-depth regional shellfish aquaculture questions related to ecosystem services and shellfish aquaculture interactions. |
| first_indexed | 2024-03-10T22:19:23Z |
| format | Article |
| id | doaj.art-75b686c0d3df493080108159f592ee03 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-10T22:19:23Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-75b686c0d3df493080108159f592ee032023-11-19T12:19:24ZengNature PortfolioScientific Data2052-44632023-11-011011910.1038/s41597-023-02557-6A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USADara M. Farrell0Bridget Ferriss1Beth Sanderson2Karl Veggerby3Lauren Robinson4Anusua Trivedi5Shreyaan Pathak6Sreya Muppalla7Jane Wang8Dan Morris9Rahul Dodhia10University of Washington (UW), School of Aquatic and Fishery SciencesAlaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration (NOAA)Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric AdministrationUniversity of Washington (UW), School of Aquatic and Fishery SciencesiMerit, 985 University AvenueMicrosoft (MS), Microsoft AI for Good Research LabMicrosoft (MS), Microsoft AI for Good Research LabMicrosoft (MS), Microsoft AI for Good Research LabMicrosoft (MS), Microsoft AI for Good Research LabMicrosoft (MS), Microsoft AI for Good Research LabMicrosoft (MS), Microsoft AI for Good Research LabAbstract The sustainable management of fisheries and aquaculture requires an understanding of how these activities interact with natural fish populations. GoPro cameras were used to collect an underwater video data set on and around shellfish aquaculture farms in an estuary in the NE Pacific from June to August 2017 and June to August 2018 to better understand habitat use by the local fish and crab communities. Images extracted from these videos were labeled to produce a data set that is suitable for use in training computer vision models. The labeled data set contains 77,739 images sampled from the collected video; 67,990 objects (fishes and crustaceans) have been annotated in 30,384 images (the remainder have been annotated as “empty”). The metadata of the data set also indicates whether a physical magenta filter was used during video collection to counteract reduced visibility. These data have the potential to help researchers address system-level and in-depth regional shellfish aquaculture questions related to ecosystem services and shellfish aquaculture interactions.https://doi.org/10.1038/s41597-023-02557-6 |
| spellingShingle | Dara M. Farrell Bridget Ferriss Beth Sanderson Karl Veggerby Lauren Robinson Anusua Trivedi Shreyaan Pathak Sreya Muppalla Jane Wang Dan Morris Rahul Dodhia A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA Scientific Data |
| title | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
| title_full | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
| title_fullStr | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
| title_full_unstemmed | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
| title_short | A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA |
| title_sort | labeled data set of underwater images of fish and crab species from five mesohabitats in puget sound wa usa |
| url | https://doi.org/10.1038/s41597-023-02557-6 |
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