Comprehensive agricultural landscape understanding is critical for addressing global challenges in food security, climate change, and resource management. This requires mapping not just crop fields, but also vital features like trees and water bodies which form an intricate mosaic in complex \textit{smallholder} systems dominating the Global South. Previous efforts to develop such land use maps have been limited by a narrow focus on methods for field delineation only, and also do not develop robust post-processing steps essential for real-world deployment. Further, to our knowledge, no prior system for smallholder farms has been deployed and evaluated at a national scale. This work addresses these limitations by presenting the first national-scale agricultural mapping system that moves beyond simple field delineation to enable segmentation of agricultural instances like fields, trees and water bodies. Our system is refined for real-world application using novel post-processing heuristics to ensure map consistency and accuracy, and is validated through a rigorous, multi-faceted evaluation process. Fine-grained land use maps generated by our system are publicly accessible via an API at \textit{\href{http://agri.withgoogle.com}{http://agri.withgoogle.com}}, enabling a wide range of applications from precision agriculture and policy-making to advancing global sustainability development goals.

翻译：全面的农业景观理解对于应对粮食安全、气候变化和资源管理等全球性挑战至关重要。这要求不仅对农田地块进行制图，还需识别树木、水体等关键特征——这些要素在全球南方主导的复杂《小农户》系统中构成了精密的镶嵌格局。以往的土地利用地图开发工作存在双重局限：一方面仅聚焦于田块边界的划定方法，另一方面未能构建实际部署所需的稳健后处理流程。此外，据我们所知，尚无针对小农户农场的系统能在国家尺度上进行部署与评估。本研究通过提出首个超越简单田块划定的国家尺度农业制图系统突破上述局限，该系统可实现对农田、树木及水体等农业实体的分割。我们通过新型后处理启发式算法优化系统在实际场景中的地图一致性与准确性，并经过严格的多维评估验证。系统生成的高分辨率土地利用地图可通过API在《\href{http://agri.withgoogle.com}{http://agri.withgoogle.com}》公开获取，支持从精准农业、政策制定到推动全球可持续发展目标等广泛的应用场景。