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

arXiv: https://arxiv.org/abs/2410.20436 · HTML: https://mlsanity.com/paper/2410.20436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 27 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SEEDS: Superpixels Extracted via Energy-Driven Sampling](https://mlsanity.com/paper/1309.3848/) (2013)
- [Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery](https://mlsanity.com/paper/2202.13487/) (2022)
- [CoralVOS: Dataset and Benchmark for Coral Video Segmentation](https://mlsanity.com/paper/2310.01946/) (2023)
- [Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study](https://mlsanity.com/paper/2401.02147/) (2024)
- [Combining Photogrammetric Computer Vision and Semantic Segmentation for Fine-grained Understanding of Coral Reef Growth under Climate Change](https://mlsanity.com/paper/2212.04132/) (2022)

## Built on by (most cited first, 3 of 3)

- [ORCA: Object Recognition and Comprehension for Archiving Marine Species](https://mlsanity.com/paper/2512.21150/) (2025)
- [MarineEval: Assessing the Marine Intelligence of Vision-Language Models](https://mlsanity.com/paper/2512.21126/) (2025)
- [MSC: A Marine Wildlife Video Dataset with Grounded Segmentation and Clip-Level Captioning](https://mlsanity.com/paper/2508.04549/) (2025)

## Abstract (arXiv)

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.