arXiv Machine Learning
Foundation Models Meet Agriculture: Challenges Beyond Pretraining
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Computer Vision
Sep 24
AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations
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 Machine Learning
Jul 31
Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States
arXiv:2607. 27217v1 Announce Type: cross Abstract: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements.
arXiv Machine Learning
Oct 16
Multi-Modal, Multi-Temporal Geospatial Foundation Models Improve Global Above-Ground Biomass Estimation
The study evaluates 13 Geospatial Foundation Models (GFMs) for estimating above‑ground biomass (AGB) from satellite imagery, comparing them to a fully supervised state‑of‑the‑art baseline trained on the AGBD dataset. Most GFMs perform worse than the baseline, but TESSERA and AlphaEarth Foundations (AEF) features combined with specific prediction heads achieve superior results, with the best model attaining an RMSE of 50.79 Mg/ha. This AEF‑based model also generalizes better to unseen regions, reduces RMSE by up to 25% in excluded areas, and matches the ESA CCI biomass product when trained on limited regional labels.
arXiv Machine Learning
Aug 6
Above-ground Biomass Estimation with Geospatial Foundation Models
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 Machine Learning
Jun 30
Benchmarking Geospatial Foundation Models for Agriculture Applications
arXiv:2606. 29664v1 Announce Type: cross Abstract: Geospatial foundation models pretrained on satellite imagery promise broad generalization across remote sensing tasks and regions, but their geographic transferability has not been systematically tested, especially in agriculture applications.
arXiv Machine Learning
Jun 5
Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping
arXiv:2606. 05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops.