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arXiv · 2410.20436

CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask

Yuk-Kwan Wong·Ziqiang Zheng·Mingzhe Zhang·David Suggett·Sai-Kit Yeung
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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. Github Page: https://github.com/ykwongaq/CoralSCOP-LAT.

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Yuk-Kwan Wong, Ziqiang Zheng, Mingzhe Zhang, David Suggett, Sai-Kit Yeung. 2024-10-27. CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask. https://doi.org/10.1016/j.ecoinf.2025.103402

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