描绘一切 v2：用于田地描绘的全球基础模型
Delineate Anything v2: A Global Foundation Model for Field Delineation
- European Space Agency（欧洲航天局）
- Space Research Institute NASU-SSAU（乌克兰国家科学院-乌克兰国家空间局空间研究所）
- University of Maryland（马里兰大学）
- National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“ Igor Sikorsky基辅理工学院”）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对大规模农业田地边界描绘难题，提出全球可扩展基础模型描绘一切 v2，构建 FBIS - 73M 数据集，引入特定数据处理管道，建立评估基准，该模型超越现有技术，速度适合大规模部署，相关资源公开。
AI中文摘要：
大规模精确的农业田地边界描绘是食品安全、供应链透明度和碳核算的基础任务。像 SAM 这样的视觉基础模型在零样本能力方面表现出色，但在地理空间领域常因拓扑复杂性、农田纹理模式和缺乏物理尺度意识而失败。本文介绍了专门为广域田地边界映射设计的全球可扩展基础模型描绘一切 v2。构建了包含 61 个国家 7300 万个实例的多分辨率数据集 FBIS - 73M。引入了特定分辨率的数据处理管道来解决多田地行政地块合并问题。建立了覆盖 100 个国家的评估基准。结果表明描绘一切 v2 超越了当前最先进技术，在乌克兰全国范围（60.3 万平方公里）映射中，在消费级工作站上 5.4 小时完成，且保持适合快速国家和全球规模部署的执行速度。代码、预训练权重、数据集和矢量边界产品均公开可用。
英文摘要：
Accurate agricultural field boundary delineation at large scale is a foundational task for food security, supply chain transparency, and carbon accounting. While vision foundation models like SAM show remarkable zero-shot capabilities, they frequently fail in geospatial domains due to topological complexity, cropland texturing patterns, and a lack of physical scale awareness. In this work, we introduce Delineate Anything v2, a globally scalable foundation model designed specifically for wide-area field boundary mapping. We construct FBIS-73M, a 73-million-instance multi-resolution dataset spanning 61 countries. To address the pervasive issue of multi-field administrative parcel merging, we introduce a resolution-specific data curation pipeline that leverages topological image-space adaptation to homogenize merged parcels and strengthen weak physical boundaries. Furthermore, we establish a novel, manually curated evaluation benchmark covering 100 countries to assess independent zero-shot generalization. Our results show that Delineate Anything v2 surpasses the current state-of-the-art, including the Delineate Anything framework, by 0.284 mAP@0.5 (+103.3% relative gain), while maintaining execution speeds suitable for rapid national- and global-scale deployment, as demonstrated by nationwide mapping of Ukraine (603,000 km^2) in 5.4 hours on a consumer-grade workstation. Code, pre-trained weights, the FBIS-73M dataset, and ready-to-use national-scale vector boundary products are publicly available at https://lavreniuk.github.io/Delineate-Anything/.
