Building Detection from Satellite Imagery Using a Composite Loss Function

Sergey Golovanov, Rauf Kurbanov, Aleksey Artamonov, Alex Davydow, Sergey I. Nikolenko

CVPRW 2018 pp. 229-232

doi:10.1109/CVPRW.2018.00040 /cvprw/2018/golovanov2018cvprw-building/

Abstract

In this paper, we present a LinkNet-based architecture with SE-ResNeXt-50 encoder and a novel training strategy that strongly relies on image preprocessing and incorporating distorted network outputs. The architecture combines a pre-trained convolutional encoder and a symmetric expanding path that enables precise localization. We show that such a network can be trained on plain RGB images with a composite loss function and achieves competitive results on the DeepGlobe challenge on building extraction from satellite images

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Cite

Text

Golovanov et al. "Building Detection from Satellite Imagery Using a Composite Loss Function." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00040

Markdown

[Golovanov et al. "Building Detection from Satellite Imagery Using a Composite Loss Function." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/golovanov2018cvprw-building/) doi:10.1109/CVPRW.2018.00040

BibTeX

@inproceedings{golovanov2018cvprw-building,
  title     = {{Building Detection from Satellite Imagery Using a Composite Loss Function}},
  author    = {Golovanov, Sergey and Kurbanov, Rauf and Artamonov, Aleksey and Davydow, Alex and Nikolenko, Sergey I.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {229-232},
  doi       = {10.1109/CVPRW.2018.00040},
  url       = {https://mlanthology.org/cvprw/2018/golovanov2018cvprw-building/}
}