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

doi:10.13474/j.cnki.11-2246.2022.0076

Abstract

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

CLC Number:

P237

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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