arXiv Machine Learning
VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 26
Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates
arXiv:2602. 17683v3 Announce Type: replace Abstract: Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture.
arXiv AI
Jun 26
EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting
arXiv:2606. 27277v1 Announce Type: new Abstract: Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions.
arXiv Machine Learning
Sep 1
Emulating the Forced Response of Climate Models with Generative Machine Learning
arXiv:2605.16929v2 Announce Type: replace Abstract: Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts....
arXiv Machine Learning
Aug 4
A Sequence-to-Sequence ConvLSTM Approach for Leaf Area Index Forecasting over the South-Central United States
arXiv:2608. 00879v1 Announce Type: cross Abstract: Leaf Area Index (LAI) is a fundamental biophysical variable governing land-atmosphere interactions; however, LAI forecasting at high spatial resolution remains an unsolved challenge.
arXiv Machine Learning
Aug 13
Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling
arXiv:2608. 12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties.
arXiv Machine Learning
Jun 9
Temporal Coverage over Density: Parsimonious Training-Set Design for ML Climate Downscaling
arXiv:2606. 07898v1 Announce Type: new Abstract: High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivating the development of machine-learning downscalers and emulators.