基于空间大数据的补充耕地合规性自动判别技术研究
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自然资源部部门预算项目(121101000000200001)


Research on Automatic Identification Technology for Compliance of Supplementary Cultivated Land Based on Spatial Big Data
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    摘要:

    为提升补充耕地项目监管效率和准确率,保证补充耕地数量准确、地类符合、区位合理,开展基于空间大数据的补充耕地合规性自动判别技术研究。本研究基于大数据框架、并行计算技术和GIS空间分析方法,设计了补充耕地合规性自动判别规则和指标体系,研制了补充耕地合规性内业自动判别的技术流程、算法和软件,开展了日常和专项补充耕地监管和核查,作为全国补充耕地项目监管的重要技术手段。经实际运行验证,项目平均分析时长2~4min,平均每月阻止5700余个问题项目入库。该研究能够为补充耕地项目立项、实施、验收提供合规性判别技术手段,提升补充耕地项目立项的合理性,提升补充耕地信息核实、监管、监督的技术水平,对确保我国耕地占补平衡制度的落实具有技术支撑作用。

    Abstract:

    In order to improve the supervision efficiency and accuracy of the supplementary cultivated land projects, and ensure that the quantity of the supplementary cultivated land is accurate, the land type meets the requirements, and the location is reasonable. It was based on spatial big data to research on the automatic discriminant technology of supplementary cultivated land compliance. The automatic identification rules and indicator system for the compliance of supplementary cultivated land was designed based on the big data framework, parallel computing technology, GIS spatial analysis, and the technical process, algorithm and software for the automatic identification of the compliance of supplementary cultivated land were developed. Daily and special supervision and verification of supplementary cultivated land were carried out, which became an increasingly important technical means for supervision and verfication of supplementary cultivated land projects. Practical operations demonstrated that the average analysis time for the project was 2~4 minutes, and the average of over 5700 problematic projects were prevented from being included in the database each month. This research provided technical means for compliance identification in the initiation, inplementation, and acceptance of supplementary cultivated land projects, enhancing the rationality of project initiation and improving the technical level of information verification, supervision of supplementary cultivated land. It played a crucial technical support role in ensuring the implementation of the system of the cultivated land requisition compensation balance in China.

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郭一珂,姚敏,周俊杰,孟凡荣,于海跃.基于空间大数据的补充耕地合规性自动判别技术研究[J].农业机械学报,2024,55(6):159-167. GUO Yike, YAO Min, ZHOU Junjie, MENG Fanrong, YU Haiyue. Research on Automatic Identification Technology for Compliance of Supplementary Cultivated Land Based on Spatial Big Data[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(6):159-167.

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  • 收稿日期:2024-01-22
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  • 在线发布日期: 2024-06-10
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