  To address the mounting destruction caused by floods in climate-vulnerable
regions, we propose Street to Cloud, a machine learning pipeline for
incorporating crowdsourced ground truth data into the segmentation of satellite
imagery of floods. We propose this approach as a solution to the
labor-intensive task of generating high-quality, hand-labeled training data,
and demonstrate successes and failures of different plausible crowdsourcing
approaches in our model. Street to Cloud leverages community reporting and
machine learning to generate novel, near-real time insights into the extent of
floods to be used for emergency response.
