Application of deep learning to heterogeneous open data for measuring urban environment and health

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In today's world, there is a great deal of information from sources like Google Street View and mobile phone usages about our environment and movement. The promise of big data, in many research fields, business, policy-making alike, that it will transform and improve the way we do things. Researchers in environmental health are also increasingly interested to use of information generated by mobile phones, satellites, wearable devices, social media, etc. The main challenge, however, is that making sense of diverse data at such big scales is not easy. Further, these new datasets are characteristically very different from conventional survey based data, hence their analysis require new methods. To this end, promising advances in deep learning over the past five years have achieved close-to-human performance on visual tasks such as recognizing objects and classifying them (e.g., telling a chair apart from a bird). In this project, I aim to apply advanced machine learning techniques to answer important questions in environmental health. Specifically, the focus will be on analysing images captured by satellites and street view cameras, and data generated from mobile phones, and investigating what they can reveal about health outcomes and its environmental and social determinants.