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

arXiv: https://arxiv.org/abs/2011.08010 · HTML: https://mlsanity.com/paper/2011.08010/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Enabling Fast and Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mapping](https://mlsanity.com/paper/2408.05350/) (2024)

## Abstract (arXiv)

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.