This data set contains 77,739 images sampled from video collected on and around shellfish aquaculture farms in an estuary in the Northeast Pacific, in which 67,990 objects (fish and crustaceans) have been annotated on 30,384 images (the remainder have been annotated as “empty”). This data set was used to develop a computer vision model to detect fish, allowing specialists from NOAA to examine images in which fish were detected to classify and quantify their species more efficiently. Incorporating artificial intelligence into ecological and resource management fields will advance our understanding of potential changes in the marine environment in the context of fisheries and aquaculture expansion, shoreline development, and climate change.
These data were collected in a collaborative effort between the NOAA Northwest Fisheries Science Center, The Nature Conservancy, and shellfish aquaculture farms in WA, USA. Funding was provided by the NOAA Office of Aquaculture Grant (NA17OAR4170218) and Washington Sea Grant (UWSC10159). The data were labelled in a collaborative effort between the NOAA Northwest Fisheries Science Center and the Microsoft AI for Good Research Lab.
Citation, license, and contact information
If you use these data in a publication or report, please use the following citation to refer to the data collection:
Ferriss B, Veggerby K, Bogeberg M, Conway-Cranos L, Hoberecht L, Kiffney P, Litle K, Toft J, Sanderson B. Characterizing the habitat function of bivalve aquaculture using underwater video. Aquaculture Environment Interactions. 2021 Nov 18;13:439-54.
…and/or the following citation to refer to the annotations and public data set:
Farrell DM, Ferriss B, Trivedi A, Pathak S, Muppalla S, Dodhia R, Wang J, Veggerby K, Morris D, Sanderson B, Scheuerell M. 2022. Using a computer vision model to locate fish in underwater video: a case study in shellfish aquaculture. 4th ICES PICES Early Career Scientist Conference, St. John’s, Newfoundland, Canada, 9–12 May 2022.
This data set is released under the Community Data License Agreement (permissive variant).
Annotations are provided in COCO Camera Traps .json format.
Downloading the data
Posted by Dan Morris.