Work

CoralSCOP-LAT: Labeling and analyzing tool for coral reef images with dense semantic mask

Year: 2025

Type: article

Abstract: Coral reef imagery offers critical data for monitoring ecosystem health, in particular as the ease of image datasets continues to rapidly expand. Whilst semi-automated analytical platforms for reef imagery are becoming more available, the dominant approaches face fundamental limitations. To address these challenges, we propose CoralSCOP-LAT , a coral reef image analysis and labeling tool that automatically segments and analyzes coral regions. By leveraging advanced machine learning models tailored for coral reef segmentation, CoralSCOP-LAT enables users to generate dense segmentation masks with minimal manual effort, significantly enhancing both the labeling efficiency and precision of coral reef analysis. Our extensive evaluations demonstrate that CoralSCOP-LAT surpasses existing coral reef analysis tools in terms of time efficiency , accuracy , precision , and flexibility . CoralSCOP-LAT , therefore, not only accelerates the coral reef annotation process but also assists users in obtaining high-quality coral reef segmentation and analysis outcomes. • CoralSCOP-LAT is a novel tool for coral reef image analysis by ecologists. • It automatically segments coral regions and conduct statistic analysis. • It enhance time efficiency, precision, flexibility, and accuracy in coral studies.

Source: Ecological Informatics

DOI: 10.1016/j.ecoinf.2025.103402

Authors: Yuk Kwan Wong, Ziqiang Zheng, Mingzhe Zhang, David J. Suggett, Sai-Kit Yeung

Institutions: Hong Kong University of Science and Technology, University of Technology Sydney, King Abdullah University of Science and Technology

Language: English

Topic: Coral and Marine Ecosystems Studies

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: gold

Estimated APC paid: $2,770