任意描绘：卫星影像上分辨率无关的田地边界划分

Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery

April 3, 2025

作者: Mykola Lavreniuk, Nataliia Kussul, Andrii Shelestov, Bohdan Yailymov, Yevhenii Salii, Volodymyr Kuzin, Zoltan Szantoi

cs.AI

摘要

从卫星影像中精确划定农田边界对于土地管理和作物监测至关重要。然而，现有方法因数据集规模有限、分辨率差异及多样化的环境条件而面临挑战。为此，我们将该任务重新定义为实例分割，并引入了农田边界实例分割-22M数据集（FBIS-22M），这是一个大规模、多分辨率的数据集，包含672,909个高分辨率卫星图像块（分辨率范围从0.25米到10米）和22,926,427个独立农田的实例掩码，显著缩小了农业数据集与其他计算机视觉领域数据集之间的差距。此外，我们提出了“Delineate Anything”模型，这是一个基于我们新FBIS-22M数据集训练的实例分割模型。该模型确立了新的技术标杆，在[email protected]和[email protected]:0.95指标上分别实现了88.5%和103%的显著提升，同时展现出更快的推理速度以及在多种图像分辨率和未见地理区域上的强大零样本泛化能力。代码、预训练模型及FBIS-22M数据集可在https://lavreniuk.github.io/Delineate-Anything获取。

English

The accurate delineation of agricultural field boundaries from satellite imagery is vital for land management and crop monitoring. However, current methods face challenges due to limited dataset sizes, resolution discrepancies, and diverse environmental conditions. We address this by reformulating the task as instance segmentation and introducing the Field Boundary Instance Segmentation - 22M dataset (FBIS-22M), a large-scale, multi-resolution dataset comprising 672,909 high-resolution satellite image patches (ranging from 0.25 m to 10 m) and 22,926,427 instance masks of individual fields, significantly narrowing the gap between agricultural datasets and those in other computer vision domains. We further propose Delineate Anything, an instance segmentation model trained on our new FBIS-22M dataset. Our proposed model sets a new state-of-the-art, achieving a substantial improvement of 88.5% in [email protected] and 103% in [email protected]:0.95 over existing methods, while also demonstrating significantly faster inference and strong zero-shot generalization across diverse image resolutions and unseen geographic regions. Code, pre-trained models, and the FBIS-22M dataset are available at https://lavreniuk.github.io/Delineate-Anything.