A Method of Building Layering Based on Oblique Photogrammetry 3D Data
The attribute linking method that takes the single building as the object can no longer meet the demand of attri-bute information linking for the multi-layer difference in 3D space of urban buildings.Therefore,we propose an intelli-gent method to extract building floors based on oblique photo-grammetry 3D data.This method uses the existing 2D vector boundary data of the buildings to extract the facade texture of the buildings,and adds a feature enhancement structure,re-verse feature pyramid network(FPN)to Mask R-CNN model to make full use of the feature information of high and low floors to improve the window recognition and detection rate.According to the rules of window arrangement,regular completion is performed,and the floor height is calculated and the floors are divided.The experiment verifies the pro-posed method has better fault tolerance.Even when the win-dows are not fully identified or blocked,the floor layering can be achieved through simple post-processing.
building layeringattribute linkingdeep learningwindow extractionrule completion
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