CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge

CVPRW 2018 pp. 242-246

doi:10.1109/CVPRW.2018.00044 /cvprw/2018/delassus2018cvprw-cnns/

Abstract

This paper presents our contribution to the DeepGlobe Building Detection Challenge. We enhanced the SpaceNet Challenge winning solution by proposing a new fusion strategy based on a deep combiner using segmentation both results of different CNN and input data to segment. Segmentation results for all cities have been significantly improved (between 1% improvement over the baseline for the smallest one to more than 7% for the biggest one). The separation of adjacent buildings should be the next enhancement made to the solution.

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Cite

Text

Delassus and Giot. "CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00044

Markdown

[Delassus and Giot. "CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/delassus2018cvprw-cnns/) doi:10.1109/CVPRW.2018.00044

BibTeX

@inproceedings{delassus2018cvprw-cnns,
  title     = {{CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge}},
  author    = {Delassus, Rémi and Giot, Romain},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {242-246},
  doi       = {10.1109/CVPRW.2018.00044},
  url       = {https://mlanthology.org/cvprw/2018/delassus2018cvprw-cnns/}
}