The point is the mask: scaling coral reef segmentation with weak supervision

TL;DR

This paper introduces a scalable, cost-effective weakly supervised deep learning framework for coral reef segmentation from drone imagery, effectively transferring ecological information across scales to enable large-area, high-resolution reef monitoring.

Contribution

The study presents a novel multi-scale weakly supervised segmentation method that leverages underwater data to improve aerial coral reef mapping with minimal manual annotations.

Findings

Effective large-area coral morphotype segmentation

Flexible integration of new classes in the model

Cost-effective approach combining low-cost data and weak supervision

Abstract

Monitoring coral reefs at large spatial scales remains an open challenge, essential for assessing ecosystem health and informing conservation efforts. While drone-based aerial imagery offers broad spatial coverage, its limited resolution makes it difficult to reliably distinguish fine-scale classes, such as coral morphotypes. At the same time, obtaining pixel-level annotations over large spatial extents is costly and labor-intensive, limiting the scalability of deep learning-based segmentation methods for aerial imagery. We present a multi-scale weakly supervised semantic segmentation framework that addresses this challenge by transferring fine-scale ecological information from underwater imagery to aerial data. Our method enables large-scale coral reef mapping from drone imagery with minimal manual annotation, combining classification-based supervision, spatial interpolation and self-distillation techniques. We demonstrate the efficacy of the approach, enabling large-area segmentation of coral morphotypes and demonstrating flexibility for integrating new classes. This study presents a scalable, cost-effective methodology for high-resolution reef monitoring, combining low-cost data collection, weakly supervised deep learning and multi-scale remote sensing.