用卫星图快速生成全国农田边界，精度远超现有方法。
Delineate Anything Flow: Fast, Country-Level Field Boundary Detection from Any Source
- 基于YOLOv11和2290万标注样本，构建可跨分辨率的农田分割模型。
- 6小时内完成乌克兰全境60万平方公里边界绘制，比现有方案多识300多万块地。
- 适合无数字地籍数据的国家，输出矢量边界直接用于农业管理。
从卫星影像中精准提取农田边界对土地管理和作物监测至关重要，但现有方法常出现边界不完整、相邻地块合并且难以扩展。本文提出Delineate Anything Flow（DelAnyFlow）方法，一种与分辨率无关的大规模农田边界映射技术。该方法结合基于YOLOv11骨干网络的DelAny实例分割模型，以及结构化后处理、合并、矢量化与简化流程，生成矢量边界。模型在包含672,909个多分辨率图像块（0.25–10m）和2290万验证田块的全球最大同类数据集FBIS 22M上训练。DelAny模型在准确率上超过SAM2的100%以上，推理速度提升400倍。其零样本泛化能力强，支持国家级应用：利用2024年哨兵2号数据，在单个工作站上6小时内完成了乌克兰（60.3万平方公里）的完整边界图层生成。与Sinergise Solutions和NASA Harvest的现成产品相比，本方法在小农户和碎片化系统中（0.25–1公顷）显著提升边界完整性。乌克兰案例中，分别识别出375万块（5米分辨率）和515万块（2.5米分辨率）农田，远超Sinergise的266万块和NASA Harvest的169万块。该工作提供了一种可扩展、低成本的农田边界提取方法，适用于缺乏数字地籍数据的地区。项目主页含模型权重、代码、国家尺度矢量输出及数据集链接：https://lavreniuk.github.io/Delineate-Anything/
原文摘要 · Abstract (English)
Accurate delineation of agricultural field boundaries from satellite imagery is essential for land management and crop monitoring, yet existing methods often produce incomplete boundaries, merge adjacent fields, and struggle to scale. We present the Delineate Anything Flow (DelAnyFlow) methodology, a resolution-agnostic approach for large-scale field boundary mapping. DelAnyFlow combines the DelAny instance segmentation model, based on a YOLOv11 backbone and trained on the large-scale Field Boundary Instance Segmentation-22M (FBIS 22M) dataset, with a structured post-processing, merging, vectorization, and simplification to generate vector boundaries. FBIS 22M, the largest dataset of its kind, contains 672,909 multi-resolution image patches (0.25-10m) and 22.9million validated field instances. The DelAny model delivers state-of-the-art accuracy with over 100% higher mAP and 400x faster inference than SAM2. DelAny demonstrates strong zero-shot generalization and supports national-scale applications: using Sentinel 2 data for 2024, DelAnyFlow generated a complete field boundary layer for Ukraine (603,000km2) in under six hours on a single workstation. DelAnyFlow outputs significantly improve boundary completeness relative to operational products from Sinergise Solutions and NASA Harvest, particularly in smallholder and fragmented systems (0.25-1ha). For Ukraine, DelAnyFlow delineated 3.75M fields at 5m and 5.15M at 2.5m, compared to 2.66M detected by Sinergise Solutions and 1.69M by NASA Harvest. This work delivers a scalable, cost-effective methodology for field delineation in regions lacking digital cadastral data. A project landing page with links to model weights, code, national-scale vector outputs, and dataset is available at https://lavreniuk.github.io/Delineate-Anything/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。