  Environmental damage has been of much concern, particularly in coastal areas
and the oceans, given climate change and the drastic effects of pollution and
extreme climate events. Our present-day analytical capabilities, along with
advancements in information acquisition techniques such as remote sensing, can
be utilised 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 habitat
mapping. Our framework compares different clustering methods for reef habitat
mapping using remote sensing data. We evaluate four major clustering approaches
based on qualitative and visual assessments which include k-means, hierarchical
clustering, Gaussian mixture model, and density-based clustering. We utilise
remote sensing data featuring the One Tree Island reef in Australia's Southern
Great Barrier Reef. Our results indicate that clustering methods using remote
sensing data can well identify benthic and geomorphic clusters in reefs when
compared with other studies. Our results indicate that Reef-Insight can
generate detailed reef habitat maps outlining distinct reef habitats and has
the potential to enable further insights for reef restoration projects.
