Last updated on September 20, 2020
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  • 6200+ Downloads
    Updated April 8, 2019 | Dataset date: Oct 1, 2018
    This dataset updates: As needed
    This zip file contains 28 cloud optimized tiff files that cover the continent of Africa. Each of the 28 files represents a region or area - these are not divided by country. Notes: The country-by-country files that were previously hosted here have been moved into separate datasets. You can find all of them here. South Sudan, Sudan, Somalia and Ethiopia are intentionally omitted from this dataset. However, a country-level dataset for Ethiopia can be found here. These 28 tiff files represent 2015 population estimates. However, please note that many of the country-level files include 2020 population estimates including: Angola, Benin, Botswana, Burundi, Cameroon, Cabo Verde, Cote d'Ivoire, Djibouti, Eritrea, Eswatini, The Gambia, Ghana, Lesotho, Liberia, Mozambique, Namibia, Sao Tome & Principe, Sierra Leone, South Africa, Togo, Zambia, and Zimbabwe.
  • 1700+ Downloads
    Updated March 19, 2019 | Dataset date: Jan 24, 2019
    This dataset updates: Never
    Facebook has produced a model to help map global medium voltage (MV) grid infrastructure, i.e. the distribution lines which connect high-voltage transmission infrastructure to consumer-serving low-voltage distribution. The data found here are model outputs for six select African countries: Malawi, Nigeria, Uganda, DRC, Cote D’Ivoire, and Zambia. The grid maps are produced using a new methodology that employs various publicly-available datasets (night time satellite imagery, roads, political boundaries, etc) to predict the location of existing MV grid infrastructure. The model documentation and code are also available , so data scientists and planners globally can replicate the model to expand model coverage to other countries where this data is not already available. You can find the model code and documentation here: https://github.com/facebookresearch/many-to-many-dijkstra Note: current model accuracy is approximately 70% when compared to existing ground-truthed data. Accuracy can be further improved by integrating other locally-relevant information into the model and running it again. Resolution: geotiff is provided at Bing Tile Level 20