arXiv:2609.13206v1 Announce Type: cross Abstract: Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such...
arXiv:2509. 04682v2 Announce Type: replace-cross Abstract: Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant failure modes, gaps largely unaddressed in current UPAM practice.
arXiv:2607. 07758v1 Announce Type: new Abstract: Foundation models (FMs) have transformed machine learning from isolated task-specific model development toward general-purpose models pretrained on broad data and adapted to multiple downstream tasks.
OceanDepths is the first open, global, AI‑ready dataset that pairs satellite‑derived sea surface temperature, salinity, and height with co‑located EN4 subsurface temperature and salinity profiles, complemented by GLORYS12 reanalysis data. It covers 2000–2024 at 0.1°×0.1° spatial resolution and weekly temporal resolution, providing over 9.5 million paired profiles interpolated to 50 depth levels. The dataset’s 4‑D multivariate structure, high resolution, long temporal extent, and extreme sparsity of subsurface observations make it a challenging testbed for novel AI methods, with demonstrated use in subsurface state reconstruction and potential for observation‑based forecasting.
arXiv:2607. 05207v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task.
arXiv:2607. 22205v2 Announce Type: replace-cross Abstract: Remote sensing multimodal large language models (RS-MLLMs) have improved general aerial-image understanding.
arXiv:2606. 23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.
arXiv:2604. 26051v2 Announce Type: replace-cross Abstract: The increasing number of satellites has improved the temporal resolution of Earth observation, making satellite-based flood mapping a promising approach for operational flood monitoring.
arXiv:2606. 13302v1 Announce Type: new Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience.
arXiv:2607. 24532v1 Announce Type: new Abstract: Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by advances in machine learning (ML) and large computing infrastructure.
arXiv:2608.24594v1 Announce Type: new Abstract: Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation...
arXiv:2607. 11998v1 Announce Type: cross Abstract: High deployment cost, poor spatial coverage and susceptibility to storm conditions are all challenges faced by traditional in-situ methods.