Abstract
Flooding is an increasingly common global threat, causing emergencies and severe damage to infrastructure.
During crises, organisations like the World Food Programme use remotely sensed images, 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. We provide with BlessemFlood21 labelled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to floodwater detection in RGB imagery.
