Guiding Labelling Effort for Efficient Learning With Georeferenced Images.

We describe a novel semi-supervised learning method that reduces the labelling effort needed to train convolutional neural networks (CNNs) when processing georeferenced imagery. This allows deep learning CNNs to be trained on a per-dataset basis, which is useful in domains where there is limited learning transferability across datasets. The method identifies representative subsets of images from an unlabelled dataset based on the latent representation of a location guided autoencoder.

Convolutional neural network for single-sensor acoustic localization of a transiting broadband source in very shallow water.

When a broadband source of radiated noise transits past a fixed hydrophone, a Lloyd's mirror constructive/destructive interference pattern can be observed in the output spectrogram. By taking the spectrum of a (log) spectrum, the power cepstrum detects the periodic structure of the Lloyd's mirror fringe pattern by generating a sequence of pulses located at the fundamental quefrency and its multiples. The fundamental quefrency, which is the reciprocal of the frequency difference between adjacent destructive interference fringes, equates to the multipath delay time.

BioTIME: A database of biodiversity time series for the Anthropocene.

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series.

Large-scale assessment of benthic communities across multiple marine protected areas using an autonomous underwater vehicle.

Marine protected areas (MPAs) are designed to reduce threats to biodiversity and ecosystem functioning from anthropogenic activities. Assessment of MPAs effectiveness requires synchronous sampling of protected and non-protected areas at multiple spatial and temporal scales. We used an autonomous underwater vehicle to map benthic communities in replicate 'no-take' and 'general-use' (fishing allowed) zones within three MPAs along 7o of latitude.

A simple, fast, and repeatable survey method for underwater visual 3D benthic mapping and monitoring.

Visual 3D reconstruction techniques provide rich ecological and habitat structural information from underwater imagery. However, an unaided swimmer or diver struggles to navigate precisely over larger extents with consistent image overlap needed for visual reconstruction. While underwater robots have demonstrated systematic coverage of areas much larger than the footprint of a single image, access to suitable robotic systems is limited and requires specialized operators.

Quantifying the response of structural complexity and community composition to environmental change in marine communities.

Habitat structural complexity is a key factor shaping marine communities. However, accurate methods for quantifying structural complexity underwater are currently lacking. Loss of structural complexity is linked to ecosystem declines in biodiversity and resilience.

Australian sea-floor survey data, with images and expert annotations.

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps.

Large-scale geographic variation in distribution and abundance of Australian deep-water kelp forests.

Despite the significance of marine habitat-forming organisms, little is known about their large-scale distribution and abundance in deeper waters, where they are difficult to access. Such information is necessary to develop sound conservation and management strategies. Kelps are main habitat-formers in temperate reefs worldwide; however, these habitats are highly sensitive to environmental change.

Variable responses of benthic communities to anomalously warm sea temperatures on a high-latitude coral reef.

High-latitude reefs support unique ecological communities occurring at the biogeographic boundaries between tropical and temperate marine ecosystems. Due to their lower ambient temperatures, they are regarded as potential refugia for tropical species shifting poleward due to rising sea temperatures. However, acute warming events can cause rapid shifts in the composition of high-latitude reef communities, including range contractions of temperate macroalgae and bleaching-induced mortality in corals.

Estimation of clustering parameters using gaussian process regression.

We propose a method for estimating the clustering parameters in a Neyman-Scott Poisson process using Gaussian process regression. It is assumed that the underlying process has been observed within a number of quadrats, and from this sparse information the distribution is modelled as a Gaussian process. The clustering parameters are then estimated numerically by fitting to the covariance structure of the model.

Multi-scale measures of rugosity, slope and aspect from benthic stereo image reconstructions.

This paper demonstrates how multi-scale measures of rugosity, slope and aspect can be derived from fine-scale bathymetric reconstructions created from geo-referenced stereo imagery. We generate three-dimensional reconstructions over large spatial scales using data collected by Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), manned submersibles and diver-held imaging systems. We propose a new method for calculating rugosity in a Delaunay triangulated surface mesh by projecting areas onto the plane of best fit using Principal Component Analysis (PCA).