A Standardised Vocabulary for Identifying Benthic Biota and Substrata from Underwater Imagery: The CATAMI Classification Scheme

Abstract

Imagery collected by still and video cameras is an increasingly important tool for minimal impact, repeatable observations in the marine environment. Data generated from imagery includes identification, annotation and quantification of biological subjects and environmental features within an image. To be long-lived and useful beyond their project-specific initial purpose, and to maximize their utility across studies and disciplines, marine imagery data should use a standardised vocabulary of defined terms. This would enable the compilation of regional, national and/or global data sets from multiple sources, contributing to broad-scale management studies and development of automated annotation algorithms. The classification scheme developed under the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) project provides such a vocabulary. The CATAMI classification scheme introduces Australian-wide acknowledged, standardised terminology for annotating benthic substrates and biota in marine imagery. It combines coarse-level taxonomy and morphology, and is a flexible, hierarchical classification that bridges the gap between habitat/biotope characterisation and taxonomy, acknowledging limitations when describing biological taxa through imagery. It is fully described, documented, and maintained through curated online databases, and can be applied across benthic image collection methods, annotation platforms and scoring methods. Following release in 2013, the CATAMI classification scheme was taken up by a wide variety of users, including government, academia and industry. This rapid acceptance highlights the scheme’s utility and the potential to facilitate broad-scale multidisciplinary studies of marine ecosystems when applied globally. Here we present the CATAMI classification scheme, describe its conception and features, and discuss its utility and the opportunities as well as challenges arising from its use.

Scaling up Ecological Measurements of Coral Reefs Using Semi-Automated Field Image Collection and Analysis

“…Hard corals comprise 11 functional groups classified based on a combination of taxonomy (i.e., family) and colony shape (i.e., branching, massive, encrusting, plating, and tabular). These groups were derived, modified and simplified from existing classification schemes [39,40]. Soft corals were classified and represented by three main functional groups: (1) Alcyoniidae soft corals, in particular the dominant genera; (2) sea fans and plumes from the family Gorgoniidae; and (3) other soft corals.…”

“…Furthermore, depending on the taxonomic aims of the study, taxonomically challenging categories are more prone to human errors, which are carried across to machine estimations from training data sets [22,23,30]. Therefore, the classification reference or label set needs to be designed in such a way that it conveys the taxonomic resolution which is functionally relevant for the intended study while acknowledging the taxonomic limitations of underwater image analysis, both manually and automatically [30,40].…”

“…While we acknowledge a higher taxonomic resolution can be achieved from underwater images [22,23,40], here we used a conservative approach. In this study, we amplified the benthic categorization compared to a previous study [24] whilst maintaining a relatively low number of taxonomic groups categorised by their functional traits (Table 1) and ensuring minimal overlap of visual features among labels.…”

Changes in deep reef benthic community composition across a latitudinal and environmental gradient in temperate Eastern Australia

“…Although underwater imagery now provides a unique way to study and monitor deeper ocean ecosystems (Schlacher et al 2010, Pizarro et al 2013, accurate, consistent and detailed description of the biota from seafloor imagery remains challenging, time-consuming, and subject to human error (Althaus et al 2015). Identification of invertebrate habitat-forming species based only on underwater imagery is limited and can rarely be performed at the species level.…”

Comparison of image annotation data generated by multiple investigators for benthic ecology

“…Recently developed frameworks, such as CATAMI (Althaus et al 2015), and software tools, such as the Video Annotation and Reference System (Schlining & Stout 2006) and BIIGLE (Schoening et al 2009), have been designed to facilitate the manual annotation of image sets by groups of investigators. In contrast to other methods involving multiple visual as ses sors, such as crowd-sourced image annotation, investigators often work alone, and consensus among investigators is rarely employed to reduce potential error or bias in the data.…”