High-precision Batch Automatic Extraction of Buildings Based on DeepLabV3+
Due to the problems of low efficiency,insufficient precision,and unsatisfactory recognition effect of building extraction in real-scene 3D models,a new batch automatic extraction method of buildings is proposed based on 3D model of oblique photography in this paper.On the basis of the classic semantic segmentation frame-work DeepLabV3+,a DEEPROOF model is trained,which can automatically extract building features from mas-sive and diverse data.Further,the extraction results are improved by combining threshold clustering,noise reduc-tion processing,and relevant image processing methods in computer vision.The experimental results show that,compared with the other four semantic segmentation methods,the proposed method performs better in precision,recall,and mean IoU,which are 2～35 percentage points higher than those of the other methods.Moreover,the segmentation area is more reasonable and scientific,the contour lines are clearer and more accurate,and the effec-tiveness of automatic batch-building extractions is better,which can meet the accuracy requirements of production.
semantic segmentationreal scene 3D modelbuilding extractionDeepLabV3+monomer modelob-lique photography
万方数据
维普
