  Flooding is the world's most costly type of natural disaster in terms of both
economic losses and human causalities. A first and essential procedure towards
flood monitoring is based on identifying the area most vulnerable to flooding,
which gives authorities relevant regions to focus. In this work, we propose
several methods to perform flooding identification in high-resolution remote
sensing images using deep learning. Specifically, some proposed techniques are
based upon unique networks, such as dilated and deconvolutional ones, while
other was conceived to exploit diversity of distinct networks in order to
extract the maximum performance of each classifier. Evaluation of the proposed
algorithms were conducted in a high-resolution remote sensing dataset. Results
show that the proposed algorithms outperformed several state-of-the-art
baselines, providing improvements ranging from 1 to 4% in terms of the Jaccard
Index.
