Assessing the health of fish populations relies on determining the length of fish in sample species subsets, in conjunction with other key ecosystem markers; thereby, inferring overall health of communities. Despite attempts to use artificial intelligence (AI) to measure fish, most measurement remains a manual process, often necessitating fish being removed from the water. Overcoming this limitation and potentially harmful intervention by measuring fish without disturbance in their natural habitat would greatly enhance and expedite the process. Stereo baited remote underwater video systems (stereo-BRUVS) are widely used as a non-invasive, stressless method for manually counting and measuring fish in aquaculture, fisheries and conservation management. However, the application of deep learning (DL) to stereo-BRUVS image processing is showing encouraging progress towards replacing the manual and labour-intensive task of precisely locating the heads and tails of fish with computer-vision-based algorithms. Here, we present a generalised, semi-automated method for measuring the length of fish using DL with near-human accuracy for numerous species of fish. Additionally, we combine the DL method with a highly precise stereo-BRUVS calibration method, which uses calibration cubes to ensure precision within a few millimetres in calculated lengths. In a human versus DL comparison of accuracy, we show that, although DL commonly slightly over-estimates or under-estimates length, with enough repeated measurements, the two values average and converge to the same length, demonstrated by a Pearson correlation coefficient (r) of 0.99 for n=3954 measurement in 'out-of-sample' test data. We demonstrate, through the inclusion of visual examples of stereo-BRUVS scenes, the accuracy of this approach. The head-to-tail measurement method presented here builds on, and advances, previously published object detection for stereo-BRUVS. Furthermore, by replacing the manual process of four careful mouse clicks on the screen to precisely locate the head and tail of a fish in two images, with two fast clicks anywhere on that fish in those two images, a significant reduction in image processing and analysis time is expected. By reducing analysis times, more images can be processed; thereby, increasing the amount of data available for environmental reporting and decision making.

Machine-assisted object detection and classification of fish species from Baited Remote Underwater Video Station (BRUVS) surveys using deep learning algorithms presents an opportunity for optimising analysis time and rapid reporting of marine ecosystem statuses. Training object detection algorithms for BRUVS analysis presents significant challenges: the model requires training datasets with bounding boxes already applied identifying the location of all fish individuals in a scene, and it requires training datasets identifying species with labels. In both cases, substantial volumes of data are required and this is currently a manual, labour-intensive process, resulting in a paucity of the labelled data currently required for training object detection models for species detection. Here, we present a “machine-assisted” approach for i) a generalised model to automate the application of bounding boxes to any underwater environment containing fish and ii) fish detection and classification to species identification level, up to 12 target species. A catch-all “ fish ” classification is applied to fish individuals that remain unidentified due to a lack of available training and validation data. Machine-assisted bounding box annotation was shown to detect and label fish on out-of-sample datasets with a recall between 0.70 and 0.89 and automated labelling of 12 targeted species with an F 1 score of 0.79. On average, 12% of fish were given a bounding box with species labels and 88% of fish were located and given a fish label and identified for manual labelling. Taking a combined, machine-assisted approach presents a significant advancement towards the applied use of deep learning for fish species detection in fish analysis and workflows and has potential for future fish ecologist uptake if integrated into video analysis software. Manual labelling and classification effort is still required, and a community effort to address the limitation presented by a severe paucity of training data would improve automation accuracy and encourage increased uptake.

The increasing amount of distributed power generation from rooftop solar panels allows new electricity markets to emerge in which pmsumers and consumers can trade locally produced energy. The use of blockchain technology has increasingly emerged in energy markets and shows great potential to facilitate Peer-to-Peer energy trading. However, blockchain technology is still in its infancy meaning it is not yet being used to its' full potential. In this paper, blockchain technology for Peer-to-Peer energy trading and its implications are explored, especially in view of the 'trilemma': scalability, security, and decentralisation. Peer-to-Peer energy trading is the focus of this paper, which ultimately proposes a blockchain scalability solution. This solution is empirically modelled using data collected in a trial case study. The proposed solution increases scalability without compromising security and decentralisation when compared to base layer models.

Currently, Martian water-ice cloud properties, such as wind direction and angular wind velocity, are determined through manual analysis of atmospheric movies taken by the Mars Science Laboratory (MSL, Curiosity). These atmospheric movies, known as Zenith Movies (ZM), have a vertical elevational pointing which allows a direct measurement of wind properties from overhead clouds. However, acquiring this observation requires a significant amount of downlinked data volume which impedes on how often it can be taken. To combat this, an algorithm using Computer Vision (CV) and machine learning has been developed to calculate cloud parameters directly. To determine how well the algorithm performs, it has been tested on a previous data set from Campbell et al. (2020) that manually measured the wind direction and angular distance in ZMs. This data set had a variety of movies with different cloud features. When ZMs had strong features, the algorithm matched well with manual results which shows promising results. However, movies that had either lighting changes, multiple cloud decks or camera artifacts caused the algorithm to perform less well. Therefore the algorithm needs improving to more accurately measure these parameters over an assortment of conditions.

Increasing sea surface temperature and extreme heat events pose the greatest threat to coral reefs globally, with trends exceeding previous norms. The resultant mass bleaching events, such as those evidenced on the Great Barrier Reef in 2016, 2017, and 2020 have substantial ecological costs in addition to economic and social costs. Advancing remote (nanosatellites, rapid revisit traditional satellites) and in-field (drones) technological capabilities, cloud data processing, and analysis, coupled with existing infrastructure and in-field monitoring programs, have the potential to provide cost-effective and timely information to managers allowing them to better understand changes on reefs and apply effective remediation. Within a risk management framework for monitoring coral bleaching, we present an overview of how remote sensing can be used throughout the whole risk management cycle and highlight the role technological advancement has in earth observations of coral reefs for bleaching events.

There is an immense, internationally significant collection of radio astronomy data in Australia, generated by organisations such as CSIRO and ICRAR, which are also playing an active role in building the Square Kilometre Array (SKA). Australia has constructed two of the three official SKA pathfinders: the Australian SKA Pathfinder (ASKAP) and the Murchison Widefield Array (MWA), so the collection of data will grow in the near future. Commonwealth (Super Science) has made a considerable infrastructure investment to support Data Intensive Sciences within the Pawsey Supercomputing Centre (Pawsey), MWA and ASKAP. The scientists use the co-located high performance compute and data stores to facilitate the research. Research Data Services (RDS) is an investment to support Data Intensive Sciences, such as e.g., MWA GLEAM survey, by providing an infrastructure to store large datasets. RDS already hosts many PBs of MWA data. The Astronomy Data Services (ADS) project has developed a solution to provide public access to astronomy data stored on RDS infrastructure. Together with IVOA services, such as TAP, SIAP and ADQL, JPEG2000 encoding for imagery data, and the consecutive streaming client-server visualisation using PIP protocol have been enabled.

Technological advancements in remote sensing and GIS have improved natural resource managers' abilities to monitor large-scale disturbances. In a time where many processes are heading towards automation, this study has regressed to simple techniques to bridge a gap found in the advancement of technology. The near-daily monitoring of dredge plume extent is common practice using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and associated algorithms to predict the total suspended solids (TSS) concentration in the surface waters originating from floods and dredge plumes. Unfortunately, these methods cannot determine the difference between dredge plume and benthic features in shallow, clear water. This case study at Barrow Island, Western Australia, uses hand digitising to demonstrate the ability of human interpretation to determine this difference with a level of confidence and compares the method to contemporary TSS methods. Hand digitising was quick, cheap and required very little training of staff to complete. Results of ANOSIM R statistics show remote sensing derived TSS provided similar spatial results if they were thresholded to at least 3 mg L(-1). However, remote sensing derived TSS consistently provided false-positive readings of shallow benthic features as Plume with a threshold up to TSS of 6 mg L(-1), and began providing false-negatives (excluding actual plume) at a threshold as low as 4 mg L(-1). Semi-automated processes that estimate plume concentration and distinguish between plumes and shallow benthic features without the arbitrary nature of human interpretation would be preferred as a plume monitoring method. However, at this stage, the hand digitising method is very useful and is more accurate at determining plume boundaries over shallow benthic features and is accessible to all levels of management with basic training.

It is unlikely that we will be able to remotely sense coastal or oceanic water chemistry comprehensively from onorbit passive or active sensors. Inferences currently are made on nitrates using sea surface temperature as a surrogate. Passive microwave sensors monitor conductivity as a surrogate for surface salinity. Suspended sediments, of course, are detectable by scattering. While Raman laser spectroscopy can diagnose constituent chemicals, issues of detection sensitivity and also eye safety are concerns especially in coastal regions. However, it might be that we don’t have to pursue this challenging objective. We are primarily concerned from a marine management perspective with situations where coastal waters become degraded and the biology is disrupted. In such circumstances, the forcing on the relative mix of phytoplankton functional groups [PFGs] may well provide the important signature that identifies the impact that coastal water chemistry [or water temperature] is having on the biological systems. A number of case studies have shown that it is feasible to identify PFGs using both multi-spectral and hyperspectral remote sensing based primarily on the spectral absorption of the individual plankton species. As a general observation, it appears that as water quality degrades with an increase in concentration of pollutants, in particular, excess nutrients such as phosphates and nitrates from agricultural run-off and sewer outfalls, the diatom population decreases and flagellate population increases. If it is possible to demonstrate that the technology does deliver with acceptable accuracy the important trends over time in PFG composition then it certainly should be feasible to go back in time for at least a decade to examine temporal trends in the mix of PFGs.