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Liling Li

Publications and source records attributed to Liling Li.

2 recordsLinked to original sources

An underwater observation dataset for fish classification and fishery assessment

Using Dual-Frequency Identification Sonar (DIDSON), fishery acoustic observation data was collected from the Ocqueoc River, a tributary of Lake Huron in northern Michigan, USA. Data were collected March through July 2013 and 2016 and included the identification, via technology or expert analysis, of eight fish species as they passed through the DIDSON’s field of view. A set of short DIDSON clips containing identified fish was curated. Additionally, two other datasets were created that include visualizations of the acoustic data and longer DIDSON clips. These datasets could complement future research characterizing the abundance and behavior of valued fishes such as walleye ( Sander vitreus ) or white sucker ( Catostomus commersonii ) or invasive fishes such as sea lamprey ( Petromyzon marinus ) or European carp ( Cyprinus carpio ). Given the abundance of DIDSON data and the fact that a portion of it is labeled, these data could aid in the creation of machine learning tools from DIDSON data, particularly for invasive sea lamprey which are amply represented and a destructive invader of the Laurentian Great Lakes.

Scientific Data

A distributed pipeline for DIDSON data processing

Technological advances in the field of ecology allow data on ecological systems to be collected at high resolution, both temporally and spatially. Devices such as Dual-frequency Identification Sonar (DIDSON) can be deployed in aquatic environments for extended periods and easily generate several terabytes of underwater surveillance data which may need to be processed multiple times. Due to the large amount of data generated and need for flexibility in processing, a distributed pipeline was constructed for DIDSON data making use of the Hadoop ecosystem. The pipeline is capable of ingesting raw DIDSON data, transforming the acoustic data to images, filtering the images, detecting and extracting motion, and generating feature data for machine learning and classification. All of the tasks in the pipeline can be run in parallel and the framework allows for custom processing. Applications of the pipeline include monitoring migration times, determining the presence of a particular species, estimating population size and other fishery management tasks.

Conference Paper