Flood level estimation from news articles and flood detection from satellite image sequences

This paper presents the solutions of team EVUS-ikg for the Multimedia Satellite Task at MediaEval 2019. We addressed two of the subtasks, namely multimodal flood level estimation (MFLE) and city-centered satellite sequences (CCSS). For MFLE, a two-step approach was proposed, which retrieves flood relevant images based on global deep features and then detects severe flood images based on self-defined distance features, which can be extracted from human body keypoints and semantic segments. For CCSS, a neural network, which combines CNN and LSTM, was used to detect floods in satellite image sequences. Both methods have achieved a good performance on the test set, which shows a great potential to improve the current flood monitoring applications.