Data on livestock farm locations and demographics are essential for disease monitoring, risk assessment, and developing spatially explicit epidemiological models. Our semantic segmentation model achieved an F2 score of 92 % and a mean Intersection over Union of 76 %. An initial total of 194,474 swine barn candidates were identified in the Southeast (North Carolina = 111,135, South Carolina = 37,264 Virginia = 46,075) and 524,962 in the Midwest (Iowa = 168,866 Minnesota = 165,714 Ohio = 190,382). The post processing Random Forest classifier reduced false positives by 82 % in the Southeast and 88 % in the Midwest, resulting in 45,580 confirmed barn polygons. These were grouped into 16,976 predicted farms and classified into one of the four production types. Population sizes were then estimated using the Random Forest regression model, with prediction accuracy varying by production type. Across all farms, 87 % of predictions for operations with 1,000 2,000 pigs were within 500 pigs of the reference value, with nursery farms showing the highest agreement (R2= 0.82), followed by finisher farms (R2 = 0.77) and sow farms (R2 = 0.56). Our results revealed substantial gaps in the existing spatial and demographic data on U.S. swine production.

翻译：关于畜牧养殖场位置与种群结构的数据对于疾病监测、风险评估以及开发空间显式流行病学模型至关重要。本研究提出了一种基于深度学习的框架，通过高分辨率卫星图像自动检测美国商业养猪场，并估算其种群规模。我们的语义分割模型取得了F2分数92%和平均交并比76%的性能。初步在东南部地区识别出194,474个候选猪舍（北卡罗来纳州=111,135，南卡罗来纳州=37,264，弗吉尼亚州=46,075），在中西部地区识别出524,962个候选猪舍（爱荷华州=168,866，明尼苏达州=165,714，俄亥俄州=190,382）。后处理的随机森林分类器将东南部地区的误报率降低了82%，中西部地区降低了88%，最终得到45,580个确认的猪舍多边形。这些猪舍被归类为16,976个预测农场，并划分为四种生产类型之一。随后使用随机森林回归模型估算种群规模，预测精度因生产类型而异。在所有农场中，对于存栏量在1,000至2,000头猪的养殖场，87%的预测值与参考值的偏差在500头以内，其中保育猪场的预测一致性最高（R²=0.82），其次是育肥猪场（R²=0.77）和母猪场（R²=0.56）。我们的研究结果揭示了美国生猪养殖现有空间与种群数据存在显著缺口。