Monitor living conditions in cities using deep learning and street imagery

Brief Project Information
Researchers have applied deep learning to street imagery for measuring spatial distributions of income, education, unemployment, housing, living environment, health, and crime. It has the potential to complement traditional survey-based and administrative data sources for high-resolution urban surveillance to measure inequalities and monitor the impacts of related policies.

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Sustainability Review

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Editor's comment for this project's sustainability
Deep learning and street imagery based methods serve as a very good complement to the traditional survey-based and administrative data sources for more comprehensive urban surveillance, thus helping to achieve a more balanced development with finer granularity.
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