Semantic Segmentation Based Building Extraction Method Using Multi-Source GIS mAP Datasets and Satellite Imagery

Weijia Li, Conghui He, Jiarui Fang, Haohuan Fu

CVPRW 2018 pp. 238-241

doi:10.1109/CVPRW.2018.00043 /cvprw/2018/li2018cvprw-semantic/

Abstract

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.

CVPRW Semantic Scholar

Cite

Text

Li et al. "Semantic Segmentation Based Building Extraction Method Using Multi-Source GIS mAP Datasets and Satellite Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00043

Markdown

[Li et al. "Semantic Segmentation Based Building Extraction Method Using Multi-Source GIS mAP Datasets and Satellite Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/li2018cvprw-semantic/) doi:10.1109/CVPRW.2018.00043

BibTeX

@inproceedings{li2018cvprw-semantic,
  title     = {{Semantic Segmentation Based Building Extraction Method Using Multi-Source GIS mAP Datasets and Satellite Imagery}},
  author    = {Li, Weijia and He, Conghui and Fang, Jiarui and Fu, Haohuan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {238-241},
  doi       = {10.1109/CVPRW.2018.00043},
  url       = {https://mlanthology.org/cvprw/2018/li2018cvprw-semantic/}
}