Multi³Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

Jakub Fil, Tim G. J. Rudner, Marc Russwurm, Benjamin Bischke, Ramona Pelich, Veronika Kopackova, Piotr Bilinski

Sep 30, 2018 NIPS 2018 Workshop Spatiotemporal Blind Submission readers: everyone
  • Keywords: image segmentation, deep learning, computer vision, flood detection, disaster response, spatiotemporal data, satellite imagery
  • TL;DR: We present a novel approach to performing rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network.
  • Abstract: We present a novel approach to performing rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our method significantly expedites the generation of satellite imagery-based flood maps, which are crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our approach allows for a rapid and accurate post-disaster damage assessment, helping governments to better coordinate medium- and long-term financial assistance programs for affected areas. Our model consists of multiple streams of encoder-decoder architectures that extract temporal information from mediumresolution images and spatial information from high-resolution images before fusing the resulting representations into a single medium resolution segmentation map of flooded buildings. We demonstrate that our model produces highly accurate segmentation of flooded buildings using only freely available medium-resolution imagery and can be improved through very high-resolution (VHR) data.
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