Floods cause serious problems around the world. Responding quickly and effectively requires accurate and timely information about the affected areas. The effective use of Remote Sensing images for accurate flood detection requires specific detection methods. Typically, Deep Neural Networks are employed, which are trained on specific datasets. For the purpose of river flood detection in RGB imagery, we use the BlessemFlood21 dataset. We here explore the use of different augmentation strategies, ranging from basic approaches to more complex techniques, including optical distortion. By identifying effective strategies, we aim to refine the training process of state-of-the-art Deep Learning segmentation networks.
View on arXiv@article{polushko2025_2504.20203,
title={ Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies },
author={ Vladyslav Polushko and Damjan Hatic and Ronald Rösch and Thomas März and Markus Rauhut and Andreas Weinmann },
journal={arXiv preprint arXiv:2504.20203},
year={ 2025 }
}