MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING

Geohazards related to water quality have become critical especially due to the harmful impacts of climate change and human activities. This constitutes the timely, efficient and accurate enough water quality monitoring as a significant component in the emergency management cycle, which can be realised by satellite remote sensing. In this paper, the first image-based benchmark dataset dedicated to mapping muddy waters is presented, named MUDDAT. The dataset is based on Sentinel-2 10m resolution images depicting various muddy water incidents across nine European countries and can essentially be used for semantic segmentation. The annotation procedure is based on an ensemble of three independent methods which reduces biases, namely, NDTI-MNDWI, SID and k-means clustering. Finally, a U-Net deep learning model which is considered state-of-the-art in semantic segmentation tasks is trained and fine-tuned. Both qualitative and quantitative results in unseen regions reach high values in classification metrics, constituting MUDDAT a considerable option in water quality monitoring for operational use.