Seaweed biomass offers significant potential for climate mitigation, but large-scale, autonomous open-ocean farms are required to fully exploit it. Such farms typically have low propulsion and are heavily influenced by ocean currents. We want to design a controller that maximizes seaweed growth over months by taking advantage of the non-linear time-varying ocean currents for reaching high-growth regions. The complex dynamics and underactuation make this challenging even when the currents are known. This is even harder when only short-term imperfect forecasts with increasing uncertainty are available. We propose a dynamic programming-based method to efficiently solve for the optimal growth value function when true currents are known. We additionally present three extensions when as in reality only forecasts are known: (1) our methods resulting value function can be used as feedback policy to obtain the growth-optimal control for all states and times, allowing closed-loop control equivalent to re-planning at every time step hence mitigating forecast errors, (2) a feedback policy for long-term optimal growth beyond forecast horizons using seasonal average current data as terminal reward, and (3) a discounted finite-time Dynamic Programming (DP) formulation to account for increasing ocean current estimate uncertainty. We evaluate our approach through 30-day simulations of floating seaweed farms in realistic Pacific Ocean current scenarios. Our method demonstrates an achievement of 95.8% of the best possible growth using only 5-day forecasts. This confirms the feasibility of using low-power propulsion and optimal control for enhanced seaweed growth on floating farms under real-world conditions.

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated but with unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models on ST ocean data. To the best of our knowledge, a comprehensive survey of existing studies remains missing in the literature, which hinders not only computer scientists from identifying the research issues in ocean data mining but also ocean scientists to apply advanced STDM techniques. In this paper, we provide a comprehensive survey of existing STDM studies for ocean science. Concretely, we first review the widely-used ST ocean datasets and highlight their unique characteristics. Then, typical ST ocean data quality enhancement techniques are explored. Next, we classify existing STDM studies in ocean science into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate on the techniques for these tasks. Finally, promising research opportunities are discussed. This survey can help scientists from both computer science and ocean science better understand the fundamental concepts, key techniques, and open challenges of STDM for ocean science.

Nvidia's Jetson modules also help the USVs handle significant amounts of data processing while running on mostly solar and wind power. You may be familiar with self-driving autonomous vehicles, but did you know that there are also autonomous vehicles for the ocean? The US-based startup Saildrone makes autonomous, uncrewed surface vehicles (USVs) with nautical data collection technology that can be used to better explore marine life, weather, ocean floor mapping, and more. USVs' data collection technology has been used to track hurricanes in the North Atlantic, discover a 3,200-foot underwater mountain in the Pacific Ocean, and start mapping the ocean floor, according to Saildrone. Also: Bing Chat's enterprise solution is here.

Global warming is an urgent issue that is generating catastrophic environmental changes, such as the melting of sea ice and glaciers, particularly in the polar regions. The melting pattern and retreat of polar sea ice cover is an essential indicator of global warming. The Sentinel-2 satellite (S2) captures high-resolution optical imagery over the polar regions. This research aims at developing a robust and effective system for classifying polar sea ice as thick or snow-covered, young or thin, or open water using S2 images. A key challenge is the lack of labeled S2 training data to serve as the ground truth. We demonstrate a method with high precision to segment and automatically label the S2 images based on suitably determined color thresholds and employ these auto-labeled data to train a U-Net machine model (a fully convolutional neural network), yielding good classification accuracy. Evaluation results over S2 data from the polar summer season in the Ross Sea region of the Antarctic show that the U-Net model trained on auto-labeled data has an accuracy of 90.18% over the original S2 images, whereas the U-Net model trained on manually labeled data has an accuracy of 91.39%. Filtering out the thin clouds and shadows from the S2 images further improves U-Net's accuracy, respectively, to 98.97% for auto-labeled and 98.40% for manually labeled training datasets.

Coral reef conservation is a steppingstone to protect marine biodiversity and life in the ocean as we know it. The health of coral also has huge societal implications: reef ecosystems provide sustenance and livelihoods for millions of people around the world. Conserving biodiversity in reef areas is both a social issue and a marine biodiversity priority. In the face of climate change, Annalisa Bracco, professor in the School of Earth and Atmospheric Sciences at Georgia Institute of Technology, and Lyuba Novi, a postdoctoral researcher, offer a new methodology that could revolutionize how conservationists monitor coral. The researchers applied machine learning tools to study how climate impacts connectivity and biodiversity in the Pacific Ocean's Coral Triangle--the most diverse and biologically complex marine ecosystem on the planet.

Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Hyperspectral remote sensing images provide rich spectral information which is beneficial for the monitoring of oil spills in complex ocean scenarios. However, most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs), which require a huge amount of effort to annotate a certain number of high-quality training sets. In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs. First, considering that the noise level varies among different bands, a noise variance estimation method is exploited to evaluate the noise level of different bands, and the bands corrupted by severe noise are removed. Second, kernel principal component analysis (KPCA) is employed to reduce the high dimensionality of the HSIs. Then, the probability of each pixel belonging to one of the classes of seawater and oil spills is estimated with the isolation forest, and a set of pseudo-labeled training samples is automatically produced using the clustering algorithm on the detected probability. Finally, an initial detection map can be obtained by performing the support vector machine (SVM) on the dimension-reduced data, and then, the initial detection result is further optimized with the extended random walker (ERW) model so as to improve the detection accuracy of oil spills. Experiments on airborne hyperspectral oil spill data (HOSD) created by ourselves demonstrate that the proposed method obtains superior detection performance with respect to other state-of-the-art detection approaches.

If it looks like a penguin and swims like a penguin – but it's actually a robot – then it must be the latest advance in marine sensory equipment. The Quadroin is an autonomous underwater vehicle (AUV): a 3D-printed self-propelled machine designed to mimic a penguin in order to measure the properties of oceanic eddies. It was developed by Burkard Baschek while head of Germany's Institute of Coastal Ocean Dynamics at the Helmholtz Centre Hereon in Geesthacht after he watched more than $20,000 of his equipment sink to the bottom of the Pacific Ocean. Eddies are small ocean currents that other research methods have struggled to capture. They influence all the animals and plants in the seas as well as the Earth's climate, driving roughly 50% of all phytoplankton production.

Killer whales – also known as orcas – have complex social structures including close'friendships', a new study reveals. Scientists at the University of Exeter used drones to film the animals – one of the world's most powerful predators – in the Pacific Ocean. The team found killer whales (Orcinus orca) spend more time interacting with certain individuals in their pod, and tend to favour those of the same sex and similar age. Results from the new study are based on 651 minutes of video filmed over 10 days. Orcas are the largest member of the dolphin family.

A new study led by the University of Exeter and the Center for Whale Research suggests killer whales may socialise with each other based on age and gender, with younger whales and females more sociable than other groups. The research used drone cameras to study one pod of southern resident killer whales off the US coast of Washington State, in the Pacific Ocean. Around 10 hours of footage was captured over 10 days.

The deepest regions of the oceans still remain one of the least explored areas on Earth, despite their considerable scientific interest and the richness of lifeforms inhabiting them. Two reasons for this are the low temperatures and enormous pressures exerted at such depths, which require the exploration equipment be carefully shielded inside high-strength metal or ceramic chambers to withstand them. This makes deep-sea exploration vessels bulky, expensive and unwieldy, as well as difficult to design, manufacture and transport. But a new small self-powered underwater robotic fish appears to offer an alternative. According to a recent paper, the robot was able to reach the deepest part of the Pacific Ocean – the Mariana Trench – at a depth of almost 11 km (6.8 miles).