@InProceedings{LauraLopez-Fuentes2017,
author="Laura Lopez-Fuentes
and Claudio Rossi
and Harald Skinnemoen",
title="River segmentation for flood monitoring",
booktitle="Data Science for Emergency Management at Big Data 2017",
year="2017",
abstract="Floods are major natural disasters which cause deaths and material damages every year. Monitoring these events is crucial in order to reduce both the affected people and the economic losses. In this work we train and test three different Deep Learning segmentation algorithms to estimate the water area from river images, and compare their performances. We discuss the implementation of a novel data chain aimed to monitor river water levels by automatically process data collected from surveillance cameras, and to give alerts in case of high increases of the water level or flooding. We also create and openly publish the first image dataset for river water segmentation.",
optnote="LAMP; 600.084; 600.120",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3078), last updated on Mon, 24 Jan 2022 11:03:09 +0100",
doi="10.1109/BigData.2017.8258373",
file=":http://refbase.cvc.uab.es/files/LRS2017.pdf:PDF"
}