# Reef-insight: A framework for reef habitat mapping with clustering methods via remote sensing

arXiv: https://arxiv.org/abs/2301.10876 · HTML: https://mlsanity.com/paper/2301.10876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 79 references that are in the map.
- First posted on arXiv 26 Jan 2023.

## Builds on (most cited first, 4 of 4)

- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [A review of machine learning in processing remote sensing data for mineral exploration](https://mlsanity.com/paper/2103.07678/) (2021)
- [GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data](https://mlsanity.com/paper/1712.04778/) (2017)
- [Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics](https://mlsanity.com/paper/1808.02763/) (2018)

## Built on by (most cited first, 1 of 1)

- [Remote sensing framework for geological mapping via stacked autoencoders and clustering](https://mlsanity.com/paper/2404.02180/) (2024)

## Abstract (arXiv)

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.