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arXiv · 2011.08010

Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation

Veda Sunkara·Matthew Purri·Bertrand Le Saux·Jennifer Adams
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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.

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Veda Sunkara, Matthew Purri, Bertrand Le Saux, Jennifer Adams. 2020-11-05. Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation. https://arxiv.org/abs/2011.08010

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