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 Garcia-Herranz, Dohyung Kim; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 60-66

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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[bibtex]
@InProceedings{Yi_2019_CVPR_Workshops,
author = {Yi, Zhuangfang and Zurutuza, Naroa and Bollinger, Drew and Garcia-Herranz, Manuel and Kim, Dohyung},
title = {Towards equitable access to information and opportunity for all: mapping schools with high-resolution Satellite Imagery and Machine Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}