Building Detection from Satellite Imagery Using Ensemble of Size-Specific Detectors
Abstract
In recent years, convolutional neural networks (CNNs) show remarkably high performance in building detection tasks. While much progress has been made, there are two aspects that have not been considered well in the past: how to address a wide variation in building size, and how to well incorporate with context information such as roads. To answer these questions, we propose a simple, but effective multi-task model. The model learns multiple detectors each of which is dedicated to a specific size of buildings. Moreover, the model implicitly utilizes context information by simultaneously training road extraction task along with building detection task. The road extractor is trained by distilling knowledge from another pre-trained CNN, requiring no labels for roads in its training. Our experiments show that the proposed model significantly improves the building detection accuracy.
Cite
Text
Hamaguchi and Hikosaka. "Building Detection from Satellite Imagery Using Ensemble of Size-Specific Detectors." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00041Markdown
[Hamaguchi and Hikosaka. "Building Detection from Satellite Imagery Using Ensemble of Size-Specific Detectors." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/hamaguchi2018cvprw-building/) doi:10.1109/CVPRW.2018.00041BibTeX
@inproceedings{hamaguchi2018cvprw-building,
title = {{Building Detection from Satellite Imagery Using Ensemble of Size-Specific Detectors}},
author = {Hamaguchi, Ryuhei and Hikosaka, Shuhei},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2018},
pages = {187-191},
doi = {10.1109/CVPRW.2018.00041},
url = {https://mlanthology.org/cvprw/2018/hamaguchi2018cvprw-building/}
}