A UNIFIED FRAMEWORK FOR WATER SURFACE EXTRACTION AND CHANGE PREDICTION IN IMAGERY DATA STREAMS
Keywords:Deep Learning, Satellite Imagery Mining, Spatio-temporal Change Prediction, WaterSurface Extraction
Changes in surface water might result in natural disasters such as floods, water shortages, landslides, waterborne diseases, which lead to loss of lives. Timely extracting for surface water and predicting its movement is essential for planning activities and decision-making processes. Most existing works on extracting water surface using satellite images focus on static spectral images and ignore the temporal evolution of data in streams, leading to less accuracy and lack of prediction power. Although some works realize that modeling temporal information of satellite signals could boost the forecasting capability on environmental changes, most of them only focus on prediction tasks independently and separately from the extraction task. In this paper, we propose a unified framework for water extraction and change prediction (WECP) built on top of imagery data streams, which are free to access from orbiting satellites, to locate water surface and predict its changes over time. Our framework is evaluated on Landsat 8 data due to its high spatial resolution. Empirical evaluations on real imagery datasets of different landscapes reveal that our framework is robust in extracting and capturing spatio-temporal changes in the water surface.
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