Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies
This work addresses the need for accurate and timely flood detection to aid emergency response, but it is incremental as it focuses on optimizing existing methods rather than introducing new ones.
The paper tackled the problem of improving flood detection in remote sensing RGB imagery by exploring various data augmentation strategies, including optical distortion, and reported that these strategies refined the training of state-of-the-art deep learning segmentation networks on the BlessemFlood21 dataset.
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.