英格兰农业部门的现场级资产映射数据集
Hassan Aftab Sheikh1, Alok Singh2, Neetu Kushwaha1
1Smith School of Enterprise and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.
Scientific data
|July 15, 2025
概括
我们使用NLP创建了一个开源的农场数据集,用于绘制农场所有权和土地使用的地图. 这些数据对于可持续农业融资和农业部门脱碳努力至关重要.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 农业部门对温室气体排放做出了重大贡献.
- 缺乏详细的农场级数据阻碍了有效的过渡融资和脱碳.
- 关于所有权,土地使用和生产的资产级准确信息缺失.
研究的目的:
- 开发一个开源的农场级数据集,以解决农业数据缺口.
- 促进农业部门的过渡融资和脱碳努力.
- 支持可持续农业的金融工具.
主要方法:
- 利用自然语言处理 (NLP) 和无监督学习技术.
- 将农场名称映射到空间多边形,以识别所有权和实体信息.
- 提取了关键属性,包括地址,土地面积,作物类型,产量和地理空间坐标.
主要成果:
- 成功创建了英格兰的综合农场级数据集.
- 确定了117,116个不同的农业实体,并提供了详细的属性.
- 该数据集为财务和环境评估提供了必要的数据.
结论:
- 新兴的农场级数据集对于可持续农业融资至关重要.
- 这一数据集可以加强对碳信用和与可持续性相关的贷款的验证.
- 改进数据质量将加强对农业气候融资的风险评估.
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