arXiv:2607. 21881v1 Announce Type: cross Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis.
arXiv:2411. 05359v2 Announce Type: replace-cross Abstract: Comprehensive agricultural landscape understanding is critical for addressing global challenges in food security, climate change, and resource management.
The study evaluates the use of frozen geospatial foundation embeddings (AlphaEarth) for mapping cultivated versus non‑cultivated land in Maine. Using 192 spatially separated patches and USDA Cropland Data Layer labels, a lightweight classifier achieved 93.7% overall accuracy without fine‑tuning, and a nearest‑class‑centroid rule reached 90.2%. A balanced sample of 60,000 labeled pixels was nearly as effective as the full 8.6 million‑pixel pool, and classifiers trained in one year remained accurate across 2018‑2023. In a blind human validation of 385 points, the AlphaEarth‑plus‑random‑forest map matched 95.3% of the consensus, outperforming the CDL reference (91.7%).
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:2608. 06404v1 Announce Type: cross Abstract: Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response.
arXiv:2607. 23024v1 Announce Type: cross Abstract: High-resolution satellite imagery is the backbone of good land-cover classification, and without that, environmental monitoring, urban planning, and sustainable resource management all fall short.
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.
The study presents a new approach to detect Christmas tree plantations in high‑resolution aerial imagery, treating the task as a rare‑target semantic segmentation problem. It introduces a Hard Negative Mining strategy that significantly improves precision‑recall performance, achieving an IoU of 0.733 and an F1‑score of 0.846 on a 2020 test set. Temporal transfer experiments demonstrate the model’s ability to generalize across years, while large‑scale validation highlights the challenge posed by the plantations’ small spatial footprint.
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. This paper presents a controlled benchmark that evaluates three models, Prithvi, SpectralGPT, and SatMAE, on multi-temporal crop segmentation and change detection across four U.
TRNet is a multimodal segmentation network that maps paddy rice in mountainous and hilly regions using 0.5 m RGB imagery, a 5 m DEM, and slope data. It introduces a Topographic Energy Spectral Rectification module to suppress steep‑slope clutter and a Topography Guided Paddy Structure Decoder to refine predictions with topographic context. On two test areas, TRNet achieves Rice IoU scores of 85.10 % and 80.68 %, outperforming a Dual Encoder U‑Net by 9.15 and 18.83 percentage points, and maintains strong performance on new August 2024 imagery.
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: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. 00548v1 Announce Type: cross Abstract: Concentrated Animal Feeding Operations (CAFOs) play an important role in agricultural production but are also associated with environmental, public health, and disease surveillance concerns.