Semantic Segmentation based Building Extraction Method using Multi-source GIS Map Datasets and Satellite Imagery

被引：24

作者：

Li, Weijia ^{[1
]}

He, Conghui ^{[2
]}

Fang, Jiarui ^{[2
]}

Fu, Haohuan ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Earth Syst Sci, Beijing, Peoples R China

[2] Tsinghua Univ, Dept Comp Sci, Beijing, Peoples R China

来源：

PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) | 2018年

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

D O I：

10.1109/CVPRW.2018.00043

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes our proposed building extraction method in DeepGlobe - CVPR 2018 Satellite Challenge. We proposed a semantic segmentation and ensemble learning based building extraction method for high resolution satellite images. Several public GIS map datasets were utilized through combining with the multispectral WorldView-3 satellite image datasets for improving the building extraction results. Our proposed method achieves the overall prediction score of 0.701 on the test dataset in DeepGlobe Building Extraction Challenge.

引用

页码：233 / 236

页数：4

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