Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation (Papers Track)
Veda Sunkara (Cloud to Street); Matthew Purri (Rutgers University); Bertrand Le Saux (European Space Agency (ESA)); Jennifer Adams (European Space Agency (ESA))
Abstract
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.