Abstract
The identification of urban locations with a high risk of diseases infections is a central aspect of public policies aiming at controlling these diseases. The presence of diseases, such as dengue fever, can be attributed to environmental factors in the urban scenario. Previous works have leveraged street-level imagery to provide estimates of dengue rates in an urban setting. In this paper, we apply Dense Deep Convolutional Neural Networks to both street-level and aerial imagery, providing evidence that aerial photography can provide better results than street-level images alone, while combining both leads to further improvements.
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