Model overview
by Mykola Lavreniuk, Nataliia Kussul, Andrii Shelestov, Yevhenii Salii, Volodymyr Kuzin, Charlotte Julia Li-Xing Wang, Zoltan Szantoi **Delineate Anything v2** extends Delineate Anything into a globally representative, resolution-agnostic foundation model that scales agricultural field boundary detection to a planetary level from any imagery source. Trained on **FBIS-73M**, a massive 73-million-instance dataset spanning 61 countries with diverse imagery sources ranging from 0.25m to 10m resolution, built through a resolution-specific curation pipeline that solves the parcel-versus-field mismatch, Delineate Anything v2 sets a new state-of-the-art in global zero-shot delineation. It delivers a **+103.3% relative gain in mAP@0.5** over Delineate Anything while maintaining extreme efficiency, mapping all of Ukraine (603,000 km²) in 5.4 hours on a regular PC with 1 GPU NVIDIA RTX 5070 Ti 16 GB. ```bibtex @inproceedings{lavreniuk2026delanyv2, title={Delineate Anything v2: A Global Foundation Model for Field Delineation}, author={Mykola Lavreniuk and Nataliia Kussul and Andrii Shelestov and Yevhenii Salii and Volodymyr Kuzin and Charlotte Julia Li-Xing Wang and Zoltan Szantoi}, year={2026}, booktitle={European Conference on Computer Vision Workshops (ECCVW)}, }
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：agpl-3.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。