Exploiting ConvNet Diversity for Flooding Identification

TL;DR

This paper introduces novel deep learning methods leveraging network diversity for flood detection in high-resolution remote sensing images, significantly improving accuracy over existing approaches.

Contribution

It presents new flood identification techniques using diverse ConvNet architectures and ensemble strategies, enhancing detection performance in remote sensing data.

Findings

Outperformed state-of-the-art baselines by 1-4% in Jaccard Index

Demonstrated effectiveness of diverse network architectures for flood detection

Validated methods on high-resolution remote sensing datasets

Abstract

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.