使用残差U-Net和文本提示的SAM 3细化从1米分辨率的NAIP图像中绘制农田范围和可见边界
Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement
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中文总结 AI 辅助
研究利用残差U-Net和文本提示的SAM 3细化从1米分辨率的NAIP图像绘制农田范围和可见边界，通过特定标注、训练和融合方法，提升了模型在困难补丁上的表现，生成语义农田范围层，为农业监测提供支持。
中文摘要 AI 辅助
农业田地图往往具有专有性、不完整性或过时性，但它们为作物监测、产量核算和土地转换分析提供了空间框架。本研究提出了一种可重复的工作流程，用于从1米分辨率的NAIP RGB图像中绘制农田范围和可见边界。在CVAT中对37个场景进行注释并转换为二进制掩码，通过非重叠的256x256补丁获得5698个样本，分为训练、验证和测试补丁。使用骰子主导损失训练的残差U-Net（ResUNet）取得了一定测试精度等指标。通过逻辑或运算将由“农业农田”提示的冻结SAM 3分支与ResUNet融合，在选定困难补丁上骰子系数提升，滑动窗口拼接产生连贯区域掩码。该产品是语义农田范围层，支持当前田地图层不可用时的农业监测。
英文摘要
Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis. This study presents a reproducible workflow for mapping farmland extent and visible boundaries from 1 m NAIP RGB imagery. Thirty-seven scenes spanning open cropland, peri-urban interfaces, semi-arid irrigation geometries, and fragmented mosaics were annotated in CVAT and converted to binary masks. Non-overlapping 256 x 256 patches yielded 5,698 samples, split by source scene into 3,850 training, 770 validation, and 1,078 test patches. A residual U-Net (ResUNet) trained with a Dice-dominant loss, L = 2.5(1 - Dice) + BCE, achieved test accuracy 0.8808, IoU 0.8605, Dice 0.9234, precision 0.8766, and recall 0.9794. A frozen SAM 3 branch prompted with "agricultural farmland field" was fused with ResUNet by logical OR. On selected difficult patches, Dice improved from 0.858 to 0.955 (orchard rows) and from 0.804 to 0.903 (fragmented parcels). Sliding-window stitching produced coherent regional masks (example tile Dice 0.898 and 0.919). The product is a semantic farmland-extent layer, not a cadastral parcel map, and supports agricultural monitoring where current field layers are unavailable.
发表机构
- Department of Biological and Agricultural Engineering, University of California, Davis（加利福尼亚大学戴维斯分校生物与农业工程系）
- Del Norte High School（德尔诺特高中）
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