Can AI fix the census?
, satellite imagery and machine learning were used to automatically identify housing plots and predict population, age, and sex in five provinces in the western half of the Democratic Republic of Congo . The project brought Grid3 participants like the University of Southampton in the UK together with groups like the DRC’s National Bureau for Statistics.
In the US, he explains, applying an AI model trained using images of roofs from the western US is problematic if it's applied to homes on the East Coast because the western expansion of the country follows a grid-based system, while cities like Boston developed with less uniformity. Equally, a roof in South Africa looks different from one in Zambia.
Deep learning models that make population predictions from satellite imagery tend to overestimate the size of rural populations and underestimate the size of urban populations, likely due to an inability to recognize the height of buildings, according to a 2021The issue of building detection is being tackled by a number of other companies and organizations.
To test the limits of that model, last summer researchers tried to recreate parts of the American Community Survey, which the US Census Bureau conducts every year. The model predicted population density and household income with an accuracy above 50 percent. That might not sound impressive, but the results took a single graduate student one week to produce, while the last US census cost $14 billion dollars and required the labor of hundreds of thousands of people.
But the lofty dream of the generalizable model, Hsiang says, is to make it possible to address any environmental and socioeconomic problem on a global scale and empower any policymaker with a computer to use satellite imagery and machine learning to make informed decisions. “Ours is not as good, the quality is a lot lower,” he says of the AI model he worked on.
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