Related papers: Ocean Eddy Identification and Tracking using Neura…

Underwater environments pose significant challenges due to the selective absorption and scattering of light by water, which affects image clarity, contrast, and color fidelity. To overcome these, we introduce OceanLens, a method that models…

Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the…

Increasing interest in the acquisition of biotic and abiotic resources from within the deep sea (e.g. fisheries, oil-gas extraction, and mining) urgently imposes the development of novel monitoring technologies, beyond the traditional…

Ocean eddies play an important role in the transport and mixing processes of the ocean due to their ability to transport material, heat, salt, and other tracers across large distances. They exhibit at least two timescales; an Eulerian…

Extended target/object tracking (ETT) problem involves tracking objects which potentially generate multiple measurements at a single sensor scan. State-of-the-art ETT algorithms can efficiently exploit the available information in these…

The ocean front has an important impact in many areas, it is meaningful to obtain accurate ocean front positioning, therefore, ocean front detection is a very important task. However, the traditional edge detection algorithm does not detect…

Atmosphere and ocean are coupled via air-sea interactions. The atmospheric conditions fuel the ocean circulation and its variability, but the extent to which ocean processes can affect the atmosphere at decadal time scales remains unclear.…

Landslides pose severe threats to infrastructure, economies, and human lives, necessitating accurate detection and predictive mapping across diverse geographic regions. With advancements in deep learning and remote sensing, automated…

This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Ni\~no events, crucial for mitigating their global climatic, economic, and societal impacts. Traditional prediction models often rely on oceanic…

As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle…

Underwater object detection (UOD), aiming to identify and localise the objects in underwater images or videos, presents significant challenges due to the optical distortion, water turbidity, and changing illumination in underwater scenes.…

Rainy years and dry years in SE Australia are known to be correlated with sea surface temperatures in the specific areas of the Indian Ocean. While over the past 100 years the correlation had been both positive and negative, it…

Event cameras are ideal for object tracking applications due to their ability to capture fast-moving objects while mitigating latency and data redundancy. Existing event-based clustering and feature tracking approaches for surveillance and…

Lagrangian ocean drifters provide highly accurate approximations of ocean surface currents but are sparsely located across the globe. As drifters passively follow ocean currents, there is minimal control on where they will be making…

The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSH), thus allowing for a better characterization of the mesoscale and…

Human activities accelerate consumption of fossil fuels and produce greenhouse gases, resulting in urgent issues today: global warming and the climate change. These indirectly cause severe natural disasters, plenty of lives suffering and…

Oceanographic forecasting impacts various sectors of society by supporting environmental conservation and economic activities. Based on global circulation models, traditional forecasting methods are computationally expensive and slow,…

For over 40 years, remote sensing observations of the Earth's oceans have yielded global measurements of sea surface temperature (SST). With a resolution of approximately 1km, these data trace physical processes like western boundary…

A key challenge for computationally intensive state-of-the-art Earth System models is to distinguish global warming signals from interannual variability. Here we introduce DLESyM, a parsimonious deep learning model that accurately simulates…

Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the…