Abstract/Summary

We investigate a deep transfer learning methodology to perform water segmentation and water level prediction on river camera images. Starting from pre-trained segmentation networks that provided state-of-the-art results on general purpose semantic image segmentation datasets ADE20k and COCO-stuff, we show that we can apply transfer learning methods for semantic water segmentation. Our transfer learning approach improves the current segmentation results of two water segmentation datasets available in the literature. We also investigate the usage of the water segmentation networks in combination with on-site ground surveys to automate the process of water level estimation on river camera images. Our methodology has the potential to impact the study and modelling of flood-related events.