The traditional mine remote sensing change monitoring requires human-computer interaction interpretation, and the timeliness cannot meet the demand. This paper studies the application of deep learning in remote sensing stope change monitoring in open pit mines, and analyzes the accuracy and applicability of automatic extraction results. Combined with information such as roads, mining rights, and urban zoning, the target area for open stope changes is provided. It provides a new technical idea for mine remote sensing monitoring.
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