The point is the mask: scaling coral reef segmentation with weak supervision
This work addresses the problem of scalable and cost-effective coral reef monitoring for conservation efforts, though it is incremental as it builds on existing weakly supervised and multi-scale techniques.
The paper tackles the challenge of large-scale coral reef monitoring by developing a multi-scale weakly supervised semantic segmentation framework that transfers fine-scale ecological information from underwater imagery to aerial drone data, enabling large-area segmentation of coral morphotypes with minimal manual annotation.
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.