TY  - CONF
AU  - Laura Lopez-Fuentes
AU  - Claudio Rossi
AU  - Harald Skinnemoen
PY  - 2017//
TI  - River segmentation for flood monitoring
BT  - Data Science for Emergency Management at Big Data 2017
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/LRS2017.pdf
UR  - http://dx.doi.org/10.1109/BigData.2017.8258373
N1  - LAMP; 600.084; 600.120
ID  - Laura Lopez-Fuentes2017
ER  -