ZooplanktonBench: A Geo-Aware Zooplankton Recognition and Classification Dataset from Marine Observations

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 ZooplanktonBench, a benchmark dataset containing images and videos of zooplankton associated with rich geospatial metadata (e.g., geographic coordinates, depth, etc.) in various water ecosystems. ZooplanktonBench defines a collection of tasks to detect, classify, and track zooplankton in challenging settings, including highly cluttered environments, living vs non-living classification, objects with similar shapes, and relatively small objects. Our dataset presents unique challenges and opportunities for state-of-the-art computer vision systems to evolve and improve visual understanding in dynamic environments characterized by significant variation and the need for geo-awareness. The code and settings described in this paper can be found on our website: https://lfk118.github.io/ZooplanktonBench_Webpage.