Enhancing USDA NASS Cropland Data Layer with Segment Anything Model
Enhancing USDA NASS Cropland Data Layer with Segment Anything Model
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DOI：
10.1109/agro-geoinformatics59224.2023.10233404
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发表时间：
2023-07
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通讯作者：
Chen Zhang;Purva Marfatia;Hamza Farhan;L. Di;Li Lin;Haoteng Zhao;Hui Li;Md Didarul Islam;Zhengwei Yang
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作者：
Chen Zhang;Purva Marfatia;Hamza Farhan;L. Di;Li Lin;Haoteng Zhao;Hui Li;Md Didarul Islam;Zhengwei Yang
随着遥感数据和机器学习技术的普及，特定作物的土地覆盖绘图是农业地理信息学的重要应用。本文提出了一种新方法，使用 Meta 的分段任意模型 (SAM) 来增强美国农业部 (USDA) 国家农业统计局 (NASS) 著名的农田数据层 (CDL) 产品。该研究利用 SAM 的零样本泛化能力，从 Sentinel-2 图像中自动描绘农田。通过对每个划定的土地单元内的主要作物类型进行投票，可以消除 CDL 中的大量噪声像素，从而显着提高绘图精度。美国加州中央山谷和玉米带等主要农业地区的初步实验结果表明，SAM 可以显着提高原始 CDL 数据的质量。这种细化特定作物土地覆盖数据（如 CDL）的能力证明了 SAM 在农业监测系统中的实际适用性。此外，结果展示了将 SAM 集成到现有作物类型分类工作流程中的巨大潜力，可以以最小的努力在全国范围内创建高质量的早季和反季作物类型地图。
Crop-specific land cover mapping is a vital application in agro-geoinformatics with the proliferation of remote sensing data and machine learning techniques. This paper presents a novel approach to enhance the well-known Cropland Data Layer (CDL) product by U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) using Meta’s Segment Anything Model (SAM). The study leverages SAM’s zero-shot generalization capability to automatically delineate cropland fields from Sentinel-2 images. By voting for the major crop types within each delineated land unit, a substantial number of noisy pixels is CDL can be eliminated, leading to notable improvements in mapping accuracy. Preliminary experimental results across key agricultural regions in the U.S., such as California’s Central Valley and Corn Belt, suggest that SAM can significantly enhance the quality of the original CDL data. This ability to refine crop-specific land cover data, like CDL, demonstrates SAM’s practical applicability within agricultural monitoring systems. Moreover, the result showcases the promising potential of integrating SAM into existing crop type classification workflows to create high-quality early- and in-season crop type maps on a national scale with minimal effort.