Research on the Investigation System of Automatic Extraction of Building Patches in Rural Illegally Occupied Cultivated Land
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.
building houses in rural illegally occupied cultivated landdeep learningsatellite remote sensing imagesnormalization survey
NETL
NSTL
万方数据
