Building extraction based on random forest and superpixel segmentation

doi:10.13474/j.cnki.11-2246.2021.0042

Abstract

Abstract: Buildings are an important part of urban space. The extraction of building information is of great significance for basic geospatial database updates, urban planning, urban dynamic monitoring. Building information extraction based on remote sensing images data has a very wide range of applications. This study proposes a method based on random forest and super pixel segmentation algorithm, and automatically extracting buildings from airborne laser point cloud and digital aerial image data. The experiment selects a certain area in Haizhu district, Guangzhou as the research area. The results show that:In a general urban area, more than 90% of buildings can be extracted accurately and quickly, with an average accuracy and completeness of about 90%. The method proposed in this paper has a good application prospect.

Key words: building boundary extraction, high-resolution optical images, LiDAR, random forest, superpixel segmentation

CLC Number:

P237

CHEN Liyan, LIN Hong, WU Jianhua. Building extraction based on random forest and superpixel segmentation[J]. Bulletin of Surveying and Mapping, 2021, 0(2): 49-53.

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