A new preprint describes an approach that moves from identifying ecological marine provinces, called eco-provinces, toward predicting them using learning methods. The authors frame the work as a step toward trustworthy models that can support spatial habitat analysis as climate change affects marine ecosystems. The paper appears on arXiv under the cs.LG category as a cross-listing.
A revised arXiv paper presents a deep evidential regression approach for estimating forest height using multimodal satellite imagery. The method targets applications including carbon accounting, biodiversity monitoring, and ecosystem management. It aims to give accurate predictions while quantifying uncertainty in sparse-data settings.
Researchers analyzed 1,744 co-registered Landsat 4-9 and Sentinel-2 image chips to study how Robert Smithson's 1970 land artwork Spiral Jetty has repeatedly been submerged and exposed as the Great Salt Lake shrank. The study treats the artwork as a large-scale visual indicator of changing water levels in the lake's north arm. It suggests long-running satellite archives can document climate-driven hydrological shifts at a specific site.
Researchers present ClimateAgent, a system that coordinates multiple LLM-based agents to carry out climate data science tasks. The work argues that general-purpose agents and fixed script pipelines lack the domain context needed for large, varied climate datasets, and proposes an orchestration approach tailored to that setting. It is a revised arXiv preprint (v2) in machine learning.
Researchers have introduced a conditional diffusion framework that refines coarse ERA5 reanalysis fields into fine-scale air temperature estimates by incorporating sparse ground-station measurements. The approach is designed to capture terrain and land-surface contrasts that coarse products miss, with the goal of improving local heatwave hazard assessment in areas with limited sensor coverage.
Researchers describe a machine learning approach that classifies sea ice by its stage of development, using labels drawn from several operational ice charts compiled by human analysts. The method also estimates uncertainty, which is relevant for navigation and ice monitoring where chart interpretations can vary.
A new arXiv paper presents multilinear operator methods that make it practical to train a machine-learning model translating wildfire conditions into smoke forecasts in real time. Wildfire smoke is a significant source of fine particulate pollution, threatening public health and power grid reliability, and long-range prediction must also factor in fuel management choices and natural fuel evolution. The work aims to speed up training so smoke-impact models can support operational forecasting.
A new arXiv paper introduces MethaneFuse, a method that learns from multiple public satellite sources to detect methane plumes. Because real-world plume events seldom have fully paired measurements across sensors, the approach is designed to work with complementary spatial, spectral, and atmospheric observations rather than complete multi-sensor coverage. The work targets gaps left by incomplete satellite data in methane monitoring.