Measuring social, environmental and health inequalities using deep learning and street imagery.

ABSTRACT: Cities are home to an increasing majority of the world's population. Currently, it is difficult to track social, economic, environmental and health outcomes in cities with high spatial and temporal resolution, needed to evaluate policies regarding urban inequalities. We applied a deep learning approach to street images for measuring spatial distributions of income, education, unemployment, housing, living environment, health and crime. Our model predicts different outcomes directly from raw images without extracting intermediate user-defined features. To evaluate the performance of the approach, we first trained neural networks on a subset of images from London using ground truth data at high spatial resolution from official statistics. We then compared how trained networks separated the b

SUBMITTER: Suel E 

PROVIDER: S-EPMC6473002 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature