Mapping the World Population One Building at a Time
High resolution datasets of population density which accurately map sparsely distributed human populations do not exist at a global scale. Typically, population data is obtained using censuses and statistical modeling. More recently, methods using...
| Main Authors: | , , , , , , , , , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2020
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/439381588065763562/Mapping-the-World-Population-One-Building-at-a-Time http://hdl.handle.net/10986/33700 |
| Summary: | High resolution datasets of population
density which accurately map sparsely distributed human
populations do not exist at a global scale. Typically,
population data is obtained using censuses and statistical
modeling. More recently, methods using remotely-sensed data
have emerged, capable of effectively identifying urbanized
areas. Obtaining high accuracy in estimation of population
distribution in rural areas remains a very challenging task
due to the simultaneous requirements of sufficient
sensitivity and resolution to detect very sparse populations
through remote sensing as well as reliable performance at a
global scale. Here, the authors present a computer vision
method based on machine learning to create population maps
from satellite imagery at a global scale, with a spatial
sensitivity corresponding to individual buildings and
suitable for global deployment. |
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