Satellite imagery, UAV imagery, and geo-referenced underwater photo transects (from the surface) are different methods used in marine monitoring and benthic habitat mapping applications to collect observations at different spatial scales. There are however challenges in linking them all together to provide fine-grained mapping and analysis for underwater, benthic habitats with complex geometric and ecological properties. We propose a novel framework called SkySea that offers users access to aligned observational data at multiple spatial scales. SkySea can integrate satellite images (e.g., from SENTINEL-2 at 10m resolution), UAV images (<5cm ground sampling distance), detailed underwater images, 3D reconstruction of the seafloor/benthos from underwater images, and make the data available through a commonly used user interface, such as QGIS. Initial evaluation indicates that the spatial overlay achieves sub-meter-level accuracy, while the underwater 3D reconstruction reaches an average relative error of less than 10% for size estimation with reference objects. We believe that this is a novel and innovative framework to achieve a seamless connection across an enormous gap of scales from satellite images, regional UAV images, local underwater images and local 3D reconstruction of the underwater environment, for benthic habitat mapping. It enables marine biologists to perform survey planning, species mapping, and model validation tasks in an integrated pipeline.

We investigated whether daily behavior summaries derived from a behavior classifier applied to ear-tag accelerometer data could support calving-oriented monitoring under field conditions. Data came from a seedstock operation in subtropical Australia with grazing and feedlot components. Because exact birth times were unavailable, we used weak supervision over two candidate days: the farm-recorded calf-observation date and the previous day. A neural network model using an exactly-one formulation over this candidate window generated an uncalibrated compatibility score from daily behavior durations and cow-specific baseline-deviation features only after each 06:00–06:00 interval was complete. Cow-grouped nested cross-validation used 134 calving cows and 4891 cow-days. At the selected operating point, the model achieved 47.0% candidate-window coverage, an F1 score of 0.277, an AUPRC of 0.231 with a candidate-day prevalence reference of 0.055, and a 2.94% pooled false-alert rate on non-candidate days. Random forest and an at-least-one neural network formulation achieved higher candidate-window coverage at their selected thresholds but greater false-alert burden outside the candidate window. We also characterized out-of-domain output burden in an independent cohort containing no calving events: 8 of 1547 scored cow-days were Alert and 7 of 31 cows received at least one Alert. These findings indicate useful but modest calving-associated information in daily behavior summaries and support prospective evaluation for management triage. However, the present study did not establish intervention lead time. More precise event timing and external calving cohorts are needed to establish advance-warning value and transferability.

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels. In this paper, we present a comprehensive real-time machine learning-based underwater data collection and curation system on edge devices for COTS monitoring. In particular, we leverage the power of deep learning-based object detection techniques, and propose a resource-efficient COTS detector that performs detection inferences on the edge device to assist marine experts with COTS identification during the data collection phase. The preliminary results show that several strategies for improving computational efficiency (e.g., batch-wise processing, frame skipping, model input size) can be combined to run the proposed detection model on edge hardware with low resource consumption and low information loss.

Prioritizing cows for closer observation around calving is important for livestock management, but reliable same-day calving detection from behavior alone remains challenging. We examine whether daily 06:00–06:00 behavior summaries derived from ear-tag accelerometers can support strictly causal, calving-oriented triage in grazing cattle. Because birth annotations provide only daily-resolution supervision, we frame the task as weakly supervised day-level risk assessment rather than precise event timing. Therefore, we train a compact weakly supervised multilayer-perceptron daily-risk model using an exactly-one likelihood over a two-day candidate window comprising the day before birth and the recorded day of birth. The model uses current behavior summaries, deviations from cow-specific causal behavior baselines for the current and previous retained days, a history-gap variable, and missingness indicators. We evaluate the model using cow-grouped nested cross-validation on data from 134 calving cows and 4,891 cow-days. At the primary base operating point, pooled out-of-fold predictions show a measurable but modest signal, with F1 score 0.277, MCC 0.244, AUPRC 0.231, and candidate-window detection 47.0%. Comparator methods recover similar signals with different detection–burden trade-offs: a supervised positive-bag random forest improves F1 score and candidate-window detection, but increases non-candidate false-alert burden. A higher-threshold Alert state reduces the retained non-candidate false-alert rate to 0.0121, but lowers recall to 0.194. Applying the frozen deployment model to an independent non-calving cohort yields low daily Watch/Alert rates but non-negligible cow-level exposure. Overall, daily behavior summaries are better suited to lightweight herd-level risk stratification and human-in-the-loop monitoring than to reliable standalone calving detection.