arXiv:2409.13568v3 Announce Type: replace Abstract: Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing method...
arXiv:2606. 11793v1 Announce Type: cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models.
arXiv:2606. 11793v2 Announce Type: replace-cross Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models.
arXiv:2609.37784v1 Announce Type: cross Abstract: Satellite observations, precomputed embeddings, and map products describe the same evolving Earth, yet are stored as independent, petabyte-scale data...
arXiv:2608.30392v1 Announce Type: new Abstract: Global food security and sustainable climate action increasingly rely on robust, scalable agricultural monitoring. Earth observation foundation models...
arXiv:2604. 10311v2 Announce Type: replace Abstract: Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications.
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.
AgroBench is a reproducible benchmark that converts U.S. county-level crop yield statistics into weakly supervised pixel‑level crop time series. The data generation pipeline fuses USDA yield data with land cover masks, Sentinel‑2 and Sentinel‑1 imagery, climatic variables, and terrain information to produce multimodal sequences for individual crop pixels across the growing season. The benchmark includes over 13 million observations from 788,654 crop pixels, covering 5,107 county‑year combinations for five major U.S. crops from 2017 to 2024, and establishes a Leave‑One‑Year‑Out evaluation protocol with baseline machine learning results.
arXiv:2609.24787v1 Announce Type: new Abstract: Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current model...
The review discusses how two‑phase heat transfer—critical for boiling, condensation, and thermal management—poses challenges for data reuse due to its complex interfacial physics. It surveys open datasets, machine‑learning techniques, and reusable software, organizing them with a spatial‑plus‑temporal dimensionality taxonomy (S+TD) that links data types to AI tasks such as regression, sequence learning, and image/video analysis. The paper proposes a roadmap for physics‑aware open data, including metadata standards, maturity labels, benchmark splits, and community databanks, emphasizing that progress in two‑phase AI relies as much on robust data infrastructure as on model design.
arXiv:2608. 04792v1 Announce Type: new Abstract: Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task at global scale.
arXiv:2609.13868v1 Announce Type: cross Abstract: Earth-observation (EO) foundation models have become exceptionally effective at learning se mantic, high-dimensional geospatial embeddings, while mod...