北京测绘2026，Vol.40Issue(3)：282-288,7.DOI:10.19580/j.cnki.1007-3000.2026.03.003
基于深度学习的乱占耕地违法行为监管方法
Supervision of illegal occupation of arable land based on deep learning
摘要
Abstract
In response to the drawbacks of conventional methods for the routine monitoring of illegal occupation of arable land,such as high workload and poor timeliness,this study was based on dynamically updated high-resolution remote sens-ing images combined with unmanned aerial vehicle(UAV)intelligent inspection imagery.A high-quality sample library was constructed using manual labeling methods.Several deep learning models were trained,including the pyramid scene parsing network(PSPNet),deep semantic segmentation algorithm(DeepLabV3+),real-time object detection algorithm(YOLOv8),and the convolutional networks plus for biomedical image segmentation(U-net++),to conduct dynamic super-vision of illegal occupation of arable land.The results show that the PSPNet model has relatively low detection accuracy and poor completeness in object extraction.Although DeepLabV3+and YOLOv8 models exhibit higher accuracy,they struggle with the completeness of complex object extraction and severe deformation of contour boundaries.The U-Net++model shows significant advantages in both detection accuracy and object extraction completeness,achieving a detection accuracy of up to 94.7%in practical applications,thereby providing accurate and reliable data for the enforcement of regulations against illegal occupation of arable land.关键词
深度学习/乱占耕地违法行为/无人机智能巡检/改进U形深度卷积神经网络(U-Net++)/金字塔场景解析网络(PSPNet)/深度语义分割算法(DeepLabV3+)/实时目标检测算法(YOLOv8)分类
天文与地球科学引用本文复制引用
李娟,刘小嘉..基于深度学习的乱占耕地违法行为监管方法[J].北京测绘,2026,40(3):282-288,7.基金项目
广东省科技计划(2021B1111610001) （2021B1111610001）
