Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation
This addresses the need for efficient flood mapping in climate-vulnerable regions, though it appears incremental as it builds on existing segmentation methods with crowdsourcing.
The paper tackles the problem of generating high-quality flood maps by proposing Street to Cloud, a machine learning pipeline that uses crowdsourced ground truth data to segment satellite imagery of floods, resulting in near-real time insights for emergency response.
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.