深度学习网络支持下的农房侵占耕地自动化监测

doi:10.13474/j.cnki.11-2246.2022.0076

测绘通报 ›› 2022, Vol. 0 ›› Issue (3): 47-53.doi: 10.13474/j.cnki.11-2246.2022.0076

深度学习网络支持下的农房侵占耕地自动化监测

高鸣^1,2, 周鑫鑫^1,2, 刘琦^1,2, 杨光迪^1,2, 吴长彬^1,2

1. 南京师范大学地理科学学院, 江苏南京 210046;
2. 南京师范大学虚拟地理环境教育部重点实验室, 江苏南京 210046

收稿日期:2021-04-08 发布日期:2022-04-01
通讯作者: 吴长彬。E-mail:wuchangbin@njnu.edu.cn
作者简介:高鸣(1997-),男,硕士生,主要从事土地监测监管与城市规划评价研究。E-mail:gaomingnnu@163.com
基金资助:
国家自然科学基金(41471318);江苏省自然资源科技计划(2021013)

Automatic monitoring of farmland occupation by farm house based on deep learning network

GAO Ming^1,2, ZHOU Xinxin^1,2, LIU Qi^1,2, YANG Guangdi^1,2, WU Changbin^1,2

1. School of Geographical Sciences, Nanjing Normal University, Nanjing 210046, China;
2. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China

Received:2021-04-08 Published:2022-04-01

摘要/Abstract

摘要： 近年来,违法占用耕地现象屡禁不止,如何利用人工智能等新一代信息技术,快速摸清农村乱占耕地建房底数,做到"早发现、早制止、严查处",是当前整治农村乱占耕地建房工作的研究难点之一。本文通过对高分辨率自然资源影像数据进行预处理,构建基于深度学习网络的自动化监测模型,应用模型进行预测并对输出结果进行GIS优化和空间叠加。试验结果表明,该方法可以快速监测出疑似侵占耕地的违法房屋,为坚守"耕地红线不突破"的底线提供了智能化技术选择,可服务于整治农村乱占耕地建房工作。

关键词: 自然资源监测;基本农田;深度学习;U-Net网络;高分遥感影像

Abstract: In recent years,illegal occupation of arable land has been repeatedly prohibited.How to use artificial intelligence and other new-generation information technology to quickly figure out the number of illegal occupation of arable land and build houses in rural areas,and achieve"early detection,early stop,strict investigation and punishment ",is one of the current research difficulties in the work of rectifying the illegal occupation of farmland in rural areas.This paper preprocesses high-resolution natural resource image data,and then builds an automated monitoring model based on a deep learning network.Thirdly,it applies model to predict and GIS optimization and spatial overlay of output results.Experimental results show that this method can quickly detect illegal houses that are suspected of occupying cultivated land,and provides intelligent technology options for sticking to the bottom line of" not breaking through the red line of cultivated land",and can serve the work of rectifying building houses on the cultivated land in rural areas.

Key words: natural resource monitoring;basic farmland;deep learning;U-Net network;high-resolution remote sensing image

中图分类号:

P237

高鸣, 周鑫鑫, 刘琦, 杨光迪, 吴长彬. 深度学习网络支持下的农房侵占耕地自动化监测[J]. 测绘通报, 2022, 0(3): 47-53.

GAO Ming, ZHOU Xinxin, LIU Qi, YANG Guangdi, WU Changbin. Automatic monitoring of farmland occupation by farm house based on deep learning network[J]. Bulletin of Surveying and Mapping, 2022, 0(3): 47-53.

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深度学习网络支持下的农房侵占耕地自动化监测

Automatic monitoring of farmland occupation by farm house based on deep learning network

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