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.
Abstract (translated)
监测大型空间范围内的珊瑚礁仍然是一个开放性的挑战，这对于评估生态系统健康和指导保护工作至关重要。虽然基于无人机的空中影像提供了广阔的地理覆盖范围，但其有限的分辨率使得难以可靠地区分如珊瑚形态等细小尺度类别。同时，在大规模范围内获取像素级标注既昂贵又耗时，这限制了深度学习分割方法的大规模应用。 我们提出了一种多尺度弱监督语义分割框架，通过将水下影像中的精细生态信息转移到空中数据中来解决这一挑战。我们的方法能够利用无人机影像对珊瑚礁进行大范围测绘，并且只需要少量的人工标注，结合分类驱动的监督、空间插值和自我蒸馏技术。我们展示了这种方法的有效性，使其能够在大面积范围内分割出珊瑚形态类型，并证明了其在整合新类别方面的灵活性。 本研究提供了一种可扩展且成本效益高的方法来实现高分辨率礁石监测，该方法结合了低成本的数据收集、弱监督的深度学习和多尺度遥感技术。
URL
https://arxiv.org/abs/2508.18958
