# Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth Mapping

arXiv: https://arxiv.org/abs/2209.09200 · HTML: https://mlsanity.com/paper/2209.09200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [DSSD : Deconvolutional Single Shot Detector](https://mlsanity.com/paper/1701.06659/) (2017)
- [A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing](https://mlsanity.com/paper/1708.03474/) (2017)
- [Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition](https://mlsanity.com/paper/2110.11479/) (2021)
- [IoU-uniform R-CNN: Breaking Through the Limitations of RPN](https://mlsanity.com/paper/1912.05190/) (2019)
- [Hiding Data in Images Using Cryptography and Deep Neural Network](https://mlsanity.com/paper/1912.10413/) (2019)
- [Application of Deep Learning in Generating Desired Design Options: Experiments Using Synthetic Training Dataset](https://mlsanity.com/paper/2001.05849/) (2019)
- [Feasibility study of urban flood mapping using traffic signs for route optimization](https://mlsanity.com/paper/2109.11712/) (2021)

## Abstract (arXiv)

Successful flood recovery and evacuation require access to reliable flood depth information. Most existing flood mapping tools do not provide real-time flood maps of inundated streets in and around residential areas. In this paper, a deep convolutional network is used to determine flood depth with high spatial resolution by analyzing crowdsourced images of submerged traffic signs. Testing the model on photos from a recent flood in the U.S. and Canada yields a mean absolute error of 6.978 in., which is on par with previous studies, thus demonstrating the applicability of this approach to low-cost, accurate, and real-time flood risk mapping.