Tracking Advances in Access to Electricity Using Satellite-Based Data and Machine Learning to Complement Surveys
Access to electricity is widely considered a major determinant of socioeconomic development. But despite long-standing efforts to expand access, 789 million people remained without electricity in 2018. Accurate and reliable data to keep track of el...
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World Bank, Washington, DC
2021
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| Online Access: | http://documents.worldbank.org/curated/en/178851618463838983/Tracking-Advances-in-Access-to-Electricity-Using-Satellite-Based-Data-and-Machine-Learning-to-Complement-Surveys http://hdl.handle.net/10986/35473 |
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okr-10986-354732021-06-14T09:54:54Z Tracking Advances in Access to Electricity Using Satellite-Based Data and Machine Learning to Complement Surveys Dhorne, Milien Nicolas, Claire Arderne, Christopher Besnard, Juliette ELECTRIFICATION ENERGY EFFICIENCY SATELLITE IMAGERY MACHINE LEARNING SDG 7.1.1 ACCESS TO ELECTRICITY Access to electricity is widely considered a major determinant of socioeconomic development. But despite long-standing efforts to expand access, 789 million people remained without electricity in 2018. Accurate and reliable data to keep track of electrification efforts must be the first step toward achieving universal access. Monitoring access with the finest granularity and taking into account local socioeconomic characteristics enable a realistic depiction of electrification progress. Such data can be used to plan efficient and robust energy access policies and programs, to raise public awareness of the urgency of action, to sustain the pace of electrification, and ultimately to connect the hardest-to-reach populations. In addition to identifying where efforts should be targeted, high-resolution data are needed to show which electricity supply options are most relevant. Remote sensing techniques and geographic information systems have revolutionized data collection by providing a range of location-specific information that was not previously accessible. The use of standardized geospatial tools and methods has made it possible to offer countries technical assistance and operational support for the development of national electrification strategies, least-cost electrification plans, and country-based investment prospectuses that combine grid, mini-grid, and off-grid technologies. 2021-04-21T13:05:06Z 2021-04-21T13:05:06Z 2021-04-15 Brief http://documents.worldbank.org/curated/en/178851618463838983/Tracking-Advances-in-Access-to-Electricity-Using-Satellite-Based-Data-and-Machine-Learning-to-Complement-Surveys http://hdl.handle.net/10986/35473 English Live Wire;2021/113 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Brief |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English |
| topic |
ELECTRIFICATION ENERGY EFFICIENCY SATELLITE IMAGERY MACHINE LEARNING SDG 7.1.1 ACCESS TO ELECTRICITY |
| spellingShingle |
ELECTRIFICATION ENERGY EFFICIENCY SATELLITE IMAGERY MACHINE LEARNING SDG 7.1.1 ACCESS TO ELECTRICITY Dhorne, Milien Nicolas, Claire Arderne, Christopher Besnard, Juliette Tracking Advances in Access to Electricity Using Satellite-Based Data and Machine Learning to Complement Surveys |
| relation |
Live Wire;2021/113 |
| description |
Access to electricity is widely
considered a major determinant of socioeconomic development.
But despite long-standing efforts to expand access, 789
million people remained without electricity in 2018.
Accurate and reliable data to keep track of electrification
efforts must be the first step toward achieving universal
access. Monitoring access with the finest granularity and
taking into account local socioeconomic characteristics
enable a realistic depiction of electrification progress.
Such data can be used to plan efficient and robust energy
access policies and programs, to raise public awareness of
the urgency of action, to sustain the pace of
electrification, and ultimately to connect the
hardest-to-reach populations. In addition to identifying
where efforts should be targeted, high-resolution data are
needed to show which electricity supply options are most
relevant. Remote sensing techniques and geographic
information systems have revolutionized data collection by
providing a range of location-specific information that was
not previously accessible. The use of standardized
geospatial tools and methods has made it possible to offer
countries technical assistance and operational support for
the development of national electrification strategies,
least-cost electrification plans, and country-based
investment prospectuses that combine grid, mini-grid, and
off-grid technologies. |
| format |
Brief |
| author |
Dhorne, Milien Nicolas, Claire Arderne, Christopher Besnard, Juliette |
| author_facet |
Dhorne, Milien Nicolas, Claire Arderne, Christopher Besnard, Juliette |
| author_sort |
Dhorne, Milien |
| title |
Tracking Advances in Access to Electricity Using Satellite-Based Data and Machine Learning to Complement Surveys |
| title_short |
Tracking Advances in Access to Electricity Using Satellite-Based Data and Machine Learning to Complement Surveys |
| title_full |
Tracking Advances in Access to Electricity Using Satellite-Based Data and Machine Learning to Complement Surveys |
| title_fullStr |
Tracking Advances in Access to Electricity Using Satellite-Based Data and Machine Learning to Complement Surveys |
| title_full_unstemmed |
Tracking Advances in Access to Electricity Using Satellite-Based Data and Machine Learning to Complement Surveys |
| title_sort |
tracking advances in access to electricity using satellite-based data and machine learning to complement surveys |
| publisher |
World Bank, Washington, DC |
| publishDate |
2021 |
| url |
http://documents.worldbank.org/curated/en/178851618463838983/Tracking-Advances-in-Access-to-Electricity-Using-Satellite-Based-Data-and-Machine-Learning-to-Complement-Surveys http://hdl.handle.net/10986/35473 |
| _version_ |
1764483110624296960 |