Identifying deep sea fish from videos of a benthic ecosystem in Barkley Canyon
收藏Mendeley Data2024-03-27 更新2024-06-29 收录
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https://borealisdata.ca/citation?persistentId=doi:10.5683/SP2/AAGZDG
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Ocean Networks Canada (ONC) operates several cabled ocean observatories in the Pacific and Arctic oceans, which collect a variety of data types. This data set contains 1004 high-resolution 30-second video clips from Barkley Canyon Axis and accompanying species annotations. Barkley Canyon is a submarine canyon located approximately 80 km off the west coast of Vancouver Island, British Columbia, Canada. The canyon axis site is located in roughly the middle of the canyon. The 30 second clips (about 60MB each) are captured 3 times a day for a year (2:00AM, 10:00AM, 6:00PM UTC), with higher frequency sampling every 2 hours for the first week of the collection period. The subsampling period was selected to reduce aliasing from tides. For all videos, the camera was facing northeast with a view of the seafloor at 45° down from horizontal, so that the field of view imaged was approximately 2m^2 of the sediment-covered seabed. Four species of interest were identified in the videos – hagfish (Eptatretus sp.), eelpouts (Lichenchelys sp.), poachers (Agonidae family) and sablefish (Anoplopoma fimbria). Videos were reviewed by a biological expert annotator who recorded what species were present and when individuals entered and exited the field of view for each video. The dataset was originally curated to support the development of computer vision algorithms capable of identifying the four species of interest.
加拿大海洋网络(Ocean Networks Canada,ONC)在太平洋与北冰洋部署了多套缆线式海洋观测站,采集多类型数据。本数据集包含源自巴克利峡谷轴区(Barkley Canyon Axis)的1004段高分辨率30秒视频片段,以及配套的物种标注信息。巴克利峡谷是一处位于加拿大不列颠哥伦比亚省温哥华岛西海岸约80公里外的海底峡谷,其轴区站点大致处于峡谷中部。该30秒视频片段(单段大小约60MB)在为期一年的采集周期内每日采集三次(协调世界时凌晨2:00、上午10:00、下午6:00),采集首周内采用每2小时一次的高频采样方案,该采样间隔的选取旨在降低潮汐引发的混叠效应。所有视频的摄像头均朝向东北方向,以与水平面成45°向下的视角拍摄海底,单段视频的成像视场约覆盖2平方米的覆沉积物海床。视频中共标注有四类目标物种:粘盲鳗(Eptatretus sp.)、绵鳚(Lichenchelys sp.)、八角鱼科(Agonidae family)鱼类以及裸盖鱼(Anoplopoma fimbria)。所有视频均由生物专家进行标注,记录每段视频中出现的物种,以及个体进入和离开成像视场的时间。本数据集最初旨在为可识别上述四类目标物种的计算机视觉算法研发提供支撑。
创建时间:
2023-06-28



