Environmental damage has been of much concern, particularly coastal areas and the oceans given climate change and drastic effects of pollution and extreme climate events. Our present day analytical capabilities along with the advancements in information acquisition techniques such as remote sensing can be utilized for the management and study of coral reef ecosystems. In this paper, we present Reef-insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef community mapping. Our framework compares different clustering methods to evaluate them for reef community mapping using remote sensing data. We evaluate four major clustering approaches such as k- means, hierarchical clustering, Gaussian mixture model, and density-based clustering based on qualitative and visual assessment. We utilise remote sensing data featuring Heron reef island region in the Great Barrier Reef of Australia. Our results indicate that clustering methods using remote sensing data can well identify benthic and geomorphic clusters that are found in reefs when compared to other studies. Our results indicate that Reef-insight can generate detailed reef community maps outlining distinct reef habitats and has the potential to enable further insights for reef restoration projects. We release our framework as open source software to enable its extension to different parts of the world

翻译：摘要：环境损害已引起广泛关注，尤其是气候变化、污染及极端气候事件的剧烈影响下的沿海区域和海洋。我们当前的分析能力，结合遥感等信息获取技术的进步，可用于珊瑚礁生态系统的管理和研究。本文提出Reef-insight，这是一个无监督机器学习框架，融合了先进聚类方法与遥感技术，用于礁群落制图。该框架通过遥感数据比较不同聚类方法，评估其在礁群落制图中的表现。我们基于定性和视觉评估，对四种主要聚类方法（如k-means、层次聚类、高斯混合模型和基于密度的聚类）进行了评价。研究采用澳大利亚大堡礁英雄礁岛区域的遥感数据。结果表明，与其他研究相比，使用遥感数据的聚类方法能有效识别礁区中的底栖和地貌聚类。研究进一步指出，Reef-insight可生成详尽的礁群落地图，勾勒出不同的礁栖息地，并有望为礁修复项目提供更深入的洞见。我们将该框架作为开源软件发布，以支持其在全球不同地区的推广和应用。