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.
By August Posch, Jitendra Kumar, Forrest M. Hoffman, Auroop R. Ganguly
arXiv:2608. 11996v1 Announce Type: cross Abstract: Cashew production is a widespread economic activity in Guinea-Bissau, as well as other countries in West Africa.
By Miguel, Sofia, Maria, Patr\'icia, Luke, Jo\~ao
arXiv:2607. 12748v1 Announce Type: cross Abstract: Farm site discovery from satellite imagery is a spatiotemporal candidate ranking problem because farm evidence is distributed across pasture, field boundaries, roads, buildings, and seasonal vegetation patterns.
By Usman Haider, Fatima Khalid, Karl Mason
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.
By Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar, Dharmendra Saraswat
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.
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.
By Zhuocheng Shang, Sanmay Das, Ahmed Eldawy