Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation
Cloud to Street；Rutgers University（罗格斯大学）；European Space Agency（欧洲空间局）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
