A Holistic Framework for Addressing the World Using Machine Learning
Abstract
Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human perception, and universal to be used as a unified geocoding system. Our algorithm starts with extracting road segments using deep learning and partitions the road network into regions. Then regions, streets, and address cells are named using proximity computations. We also extend our addressing scheme to cover inaccessible areas, to be flexible for changes, and to lead as a pioneer for a unified geodatabase.
Cite
Text
Demir. "A Holistic Framework for Addressing the World Using Machine Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00245Markdown
[Demir. "A Holistic Framework for Addressing the World Using Machine Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/demir2018cvprw-holistic/) doi:10.1109/CVPRW.2018.00245BibTeX
@inproceedings{demir2018cvprw-holistic,
title = {{A Holistic Framework for Addressing the World Using Machine Learning}},
author = {Demir, Ilke},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2018},
pages = {1875-1877},
doi = {10.1109/CVPRW.2018.00245},
url = {https://mlanthology.org/cvprw/2018/demir2018cvprw-holistic/}
}