BlessemFlood21: Advancing Flood Analysis with a High-Resolution Georeferenced Dataset for Humanitarian Aid Support

TL;DR

The paper introduces BlessemFlood21, a high-resolution, georeferenced dataset with water masks from flood imagery, to advance computer vision tools for rapid flood detection and humanitarian aid support.

Contribution

It provides a new high-resolution RGB-NIR dataset with water masks for flood analysis, enabling development of more effective flood detection algorithms.

Findings

Deep learning models achieved promising segmentation accuracy.

The dataset facilitates research in efficient flood detection tools.

Water masks improve model training and evaluation.

Abstract

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. Computer Vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep Learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery.