ML Anthology

Predicting City Poverty Using Satellite Imagery

Simone Piaggesi, Laetitia Gauvin, Michele Tizzoni, Ciro Cattuto, Natalia Adler, Stefaan Verhulst, Andrew Young, Rhiannan Price, Leo Ferres, André Panisson
CVPRW 2019 pp. 90-96
/cvprw/2019/piaggesi2019cvprw-predicting/

Abstract

Reliable data about socio-economic conditions of individuals, such as health indexes, consumption expenditures and wealth assets, remain scarce for most countries. Traditional methods to collect such data include on site surveys that can be expensive and labour intensive. On the other hand, remote sensing data, such as high-resolution satellite imagery, are becoming largely available. To circumvent the lack of socio-economic data at high granularity, computer vision has already been applied successfully to raw satellite imagery sampled from resource poor countries.

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Cite

Text

Piaggesi et al. "Predicting City Poverty Using Satellite Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.

Markdown

[Piaggesi et al. "Predicting City Poverty Using Satellite Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/piaggesi2019cvprw-predicting/)

BibTeX

@inproceedings{piaggesi2019cvprw-predicting,
  title     = {{Predicting City Poverty Using Satellite Imagery}},
  author    = {Piaggesi, Simone and Gauvin, Laetitia and Tizzoni, Michele and Cattuto, Ciro and Adler, Natalia and Verhulst, Stefaan and Young, Andrew and Price, Rhiannan and Ferres, Leo and Panisson, André},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {90-96},
  url       = {https://mlanthology.org/cvprw/2019/piaggesi2019cvprw-predicting/}
}