农村乱占耕地建房图斑自动提取的调查体系研究
Research on the Investigation System of Automatic Extraction of Building Patches in Rural Illegally Occupied Cultivated Land
马庆伟 1姚超峰 1郭长恩1
作者信息
- 1. 山东省地质矿产勘查开发局 八○一水文地质工程地质大队(山东省地矿工程勘察院),山东 济南 250000;山东省地下水环境保护与修复工程技术研究中心,山东 济南 250000
- 折叠
摘要
传统的土地调查摸排类工作通常是基层调查人员采用区域巡查或基于卫星遥感影像人工提取变化图斑下发的方式开展,周期长且成本高.为缩短工期、降低成本,采用深度学习的方法训练房屋建筑样本,自动提取耕地范围内房屋建筑,并以人工检核的方式进行补充,建立以月度为周期的农村乱占耕地建房调查体系.试验结果表明,深度提取耕地范围内房屋建筑物的正确率为 76.2%,召回率为 82.4%,可极大地提高工作效率,为后续土地摸排类工作提供借鉴.
Abstract
The traditional land survey and inventory work is usually carried out by the grassroots investigators in the form of regional pa-trolling or manual extraction of change patches based on satellite remote sensing images,which has a long cycle and high labor cost.The method of deep learning is used to train the sample of housing construction,automatically extract the housing construction within the scope of cultivated land,and supplemented by manual inspection,so as to establish a monthly survey system of housing construc-tion in rural illegally occupied cultivated land.The experimental results show that the accuracy rate of deep learning extraction of hou-ses and buildings within the scope of cultivated land is 76.2%,and the recall rate is 82.4%,which can greatly improve the work effi-ciency and provide a reference for the follow-up land inventory work.
关键词
农村乱占耕地建房/深度学习/卫星遥感影像/常态化调查Key words
building houses in rural illegally occupied cultivated land/deep learning/satellite remote sensing images/normalization survey引用本文复制引用
出版年
2024
NETL
NSTL
万方数据