How machine learning can help mapping schools – Ericsson

At the end of 2019, almost a third of young people – 369 million globally – had no access to the internet. This lack of connectivity isn’t just a matter of inconvenience, it’s a matter of life-defining opportunities. Digital inclusion has an enormous role to play …….

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At the end of 2019, almost a third of young people – 369 million globally – had no access to the internet. This lack of connectivity isn’t just a matter of inconvenience, it’s a matter of life-defining opportunities. Digital inclusion has an enormous role to play in terms of equal access to prospects and resources, from services and information to education, employment and even – as we’ve seen in the past two years with the COVID-19 pandemic – social interaction.

UNICEF and the ITU launched the Giga initiative in 2019, aiming to connect every school to the Internet by 2030, and in doing so, empower every young person with access to information, opportunity and choice. As a Global UNICEF Partner for School Connectivity Mapping, Ericsson has been working closely with UNICEF on a key element of the initiative – the mapping of every school’s connectivity across the globe, contributing vital data for an ambitious live mapping platform known as Project Connect.


As Giga passes the significant milestone of 1 million schools mapped, it’s a perfect time to celebrate all we’ve achieved so far, explore the amazing technology and data behind this ambitious project, and take a look forward at how it will help address school connectivity and support access to education across the globe.

Cutting-edge technology ensuring no school is left behind

There are approximately six million schools in the world, yet information about them is surprisingly lacking – we don’t know where they all are, let alone whether they have internet access, or the quality of those connections. The reality of achieving connectivity for all schools is an enormous and incredibly complex challenge. But data has a crucial role to play in breaking down this enormous task, to build a strong foundation from which we can identify what needs to be done, how resources can best be utilized and where the most critical needs lie.

While much of the initial school location data comes from partnering with governments, many lack comprehensive, up-to-date locations for all the schools within their borders. Faced with this challenge, we’re turning to data science and cutting-edge technology such as machine learning (ML) to potentially help close the gaps.

The initial test results are extremely promising. The first test pilot was developed for Colombia, and it identified 7000 schools that were not in the National Registry of the Ministry of Education – a staggering number of schools, and students, that might otherwise have been overlooked. While the ML algorithms are still in …….

Source: https://www.ericsson.com/en/blog/2021/11/how-machine-learning-can-help-mapping-schools

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