FloodLens: A Semi-Supervised Segmentation Framework for Post-Flood Assessment using UAV Imagery

Abstract

One of the worst natural disasters, floods seriously harm infrastructure, economy, and society. The manual interpretation of satellite or aerial imagery is a major component of traditional post-disaster assessment methods, but is time-consuming, labor-intensive, and prone to human error. Automated and precise flood mapping systems that facilitate quick emergency response is becoming more necessary as deep-learning advances and high-resolution UAV datasets become available. This paper focuses on semantic segmentation of flood-affected regions from aerial images by developing a hybrid light weighted and semi-supervised deep learning framework. In this research, from floodNet dataset both unlabeled and labeled UAV imagery, with 10 semantic classes is used and a multi-class segmentation using UNet++ model with ResNet34 and EfficientNet-B4 as backbone encoders is performed. The model is initially trained using annotated images in a supervised learning manner and later is enhanced using a semi-supervised learning approach of pseudo labeling. This training approach is applied to leverage more than 1000 unlabeled images, improving the generalization gradually. With a mean Dice score of 0.84 and mean IoU score of 0.77 for the validation set, EfficientNetB4 performs better while ResNet34 is faster by 3ms of inference time. In effect, the results show that preprocessing and segmentation using proposed models improve further processing of the results in disaster management systems. In addition, pseudo-labeling not only improves performance of minority classes but also offers a scalable method, to manage unlabelled data in substantial amounts, in real-world deployments. Overall, this work testifies the potential of combining the robust encoder-decoder architectures, with semi-supervised learning approach in order to deliver a reliable and efficient preprocessing for automated flood assessment technique. It also provides insights by highlighting the potential for extending the model for preprocessing in real-world applications, such as damage quantification, risk prioritization, etc.