Towards Adaptive Benthic Habitat Mapping

TL;DR

This paper presents an adaptive approach for benthic habitat mapping using AUV-collected imagery and bathymetric data, leveraging Bayesian neural networks to optimize sampling and reduce survey costs.

Contribution

It introduces a Bayesian neural network model that predicts habitat classes and estimates uncertainty, enabling adaptive sampling to improve habitat maps efficiently.

Findings

Uncertainty estimates guide targeted sampling to enhance habitat models.

Adaptive sampling reduces the number of samples needed for accurate mapping.

The approach is demonstrated on real AUV data from Tasmania, Australia.

Abstract

Autonomous Underwater Vehicles (AUVs) are increasingly being used to support scientific research and monitoring studies. One such application is in benthic habitat mapping where these vehicles collect seafloor imagery that complements broadscale bathymetric data collected using sonar. Using these two data sources, the relationship between remotely-sensed acoustic data and the sampled imagery can be learned, creating a habitat model. As the areas to be mapped are often very large and AUV systems collecting seafloor imagery can only sample from a small portion of the survey area, the information gathered should be maximised for each deployment. This paper illustrates how the habitat models themselves can be used to plan more efficient AUV surveys by identifying where to collect further samples in order to most improve the habitat model. A Bayesian neural network is used to predict visually-derived habitat classes when given broad-scale bathymetric data. This network can also estimate the uncertainty associated with a prediction, which can be deconstructed into its aleatoric (data) and epistemic (model) components. We demonstrate how these structured uncertainty estimates can be utilised to improve the model with fewer samples. Such adaptive approaches to benthic surveys have the potential to reduce costs by prioritizing further sampling efforts. We illustrate the effectiveness of the proposed approach using data collected by an AUV on offshore reefs in Tasmania, Australia.