Abstract
Abstract. Our country had increasingly high requirements for land management, especially for arable land, which was the most precious resource. Strict policies for protecting arable land are being implemented. The enforcement of satellite imagery in mega cities had certain limitations in terms of timeliness and accuracy. To meet the requirements of refined management, a high-altitude camera was constructed to form a near ground monitoring network covering the cultivated land area. Multi temporal image groups were obtained through video frame extraction. A monitoring sample library for typical behaviors of natural resources was established. Land objects closely related to illegal activities such as construction machinery and bulldozing areas were identified based on deep learning algorithms. By using the technology of bidirectional conversion between video spatial location and geographic coordinates, a fusion of spatial information video monitoring patterns was formed. Taking the 20000 acre permanent basic farmland area as an example, the recognition rate of illegal land related behaviors was about 81%. By combining approval information and conducting spatial analysis on the identified patterns, real-time warning information could be pushed. This application can effectively improve the timeliness of supervision and enhance the level of supervision.
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