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Towards Equitable Access to Information and Opportunity for All: Mapping Schools with High-Resolution Satellite Imagery and Machine Learning

Zhuangfang Yi, Naroa Zurutuza, Drew Bollinger, Manuel García-Herranz, Dohyung Kim
CVPRW 2019 pp. 60-66
/cvprw/2019/yi2019cvprw-equitable/

Abstract

Having accurate data about schools is key for organizations to provide quality education and promote lifelong learning, listed as UN sustainable development goal 4 (SDG4), ensure equal access to opportunity (SDG10) and eventually, reduce poverty (SDG1). However, this is a challenging task since educational facilities' records are often inaccurate, incomplete or non-existent. By leveraging machine learning and high-resolution imagery, we are able to determine school detection at the national scale. Infant-Prints: Fingerprints for Reducing Infant Mortality

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Cite

Text

Yi et al. "Towards Equitable Access to Information and Opportunity for All: Mapping Schools with High-Resolution Satellite Imagery and Machine Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.

Markdown

[Yi et al. "Towards Equitable Access to Information and Opportunity for All: Mapping Schools with High-Resolution Satellite Imagery and Machine Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/yi2019cvprw-equitable/)

BibTeX

@inproceedings{yi2019cvprw-equitable,
  title     = {{Towards Equitable Access to Information and Opportunity for All: Mapping Schools with High-Resolution Satellite Imagery and Machine Learning}},
  author    = {Yi, Zhuangfang and Zurutuza, Naroa and Bollinger, Drew and García-Herranz, Manuel and Kim, Dohyung},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {60-66},
  url       = {https://mlanthology.org/cvprw/2019/yi2019cvprw-equitable/}
}