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

Source: [Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation](<https://www.sota2.com/research/paper/68fa68bb1e209131cea1ddeb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:45.312Z

Authors: Veda Sunkara, Matthew Purri, Bertrand Le Saux, Jennifer Adams

Published: 2020-11-05T16:36:58.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.08010>)

[PDF](<https://arxiv.org/pdf/2011.08010.pdf>)