ZooplanktonBench: A Geo-Aware Zooplankton Recognition and Classification Dataset from Marine Observations
Fukun Liu, Adam T. Greer, Gengchen Mai, Jin Sun

TL;DR
ZooplanktonBench is a comprehensive dataset with images and videos of zooplankton, designed to advance computer vision methods for marine ecological monitoring through challenging detection, classification, and tracking tasks with rich geospatial metadata.
Contribution
This work introduces ZooplanktonBench, a novel benchmark dataset with geospatial annotations for zooplankton, enabling improved AI-based marine ecosystem analysis.
Findings
Dataset includes diverse zooplankton images and videos with geospatial data.
Defines challenging tasks like detection, classification, and tracking in complex environments.
Facilitates development of geo-aware computer vision systems for marine research.
Abstract
Plankton are small drifting organisms found throughout the world's oceans and can be indicators of ocean health. One component of this plankton community is the zooplankton, which includes gelatinous animals and crustaceans (e.g. shrimp), as well as the early life stages (i.e., eggs and larvae) of many commercially important fishes. Being able to monitor zooplankton abundances accurately and understand how populations change in relation to ocean conditions is invaluable to marine science research, with important implications for future marine seafood productivity. While new imaging technologies generate massive amounts of video data of zooplankton, analyzing them using general-purpose computer vision tools turns out to be highly challenging due to the high similarity in appearance between the zooplankton and its background (e.g., marine snow). In this work, we present the…
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Taxonomy
TopicsMarine Invertebrate Physiology and Ecology · Water Quality Monitoring Technologies · Oil Spill Detection and Mitigation
