Integrating remote sensing and social sensing for flood mapping

Flood events cause substantial damage to infrastructure and disrupt livelihoods. Timely monitoring of flood extent helps authorities identify severe impacts and plan relief operations. Remote sensing through satellite imagery is an effective method to identify flooded areas. However, critical contextual information about the severity of structural damage or urgent needs of affected population cannot be obtained from remote sensing alone. On the other hand, social sensing through microblogging sites can potentially provide useful information directly from eyewitnesses and affected people. Therefore, this paper explores the integration of remote sensing and social sensing data to derive informed flood extent maps. For this purpose, we employ state-ofthe-art deep learning methods to process heterogeneous data obtained from four case-study areas, including two urban regions from Somalia and India and two coastal regions from Italy and The Bahamas. On the remote sensing side, we observe that deep learning models perform generally better than Otsu in flood water prediction. For example, for highly urban areas from Somalia and India, U-Net achieves better F1-scores (0.471 and 0.310, respectively) than Otsu (0.297 and 0.251, respectively). Similarly, for coastal areas, FCN yields a better F1-score for Italy (0.128) than Otsu (0.083) while FCN and Otsu perform on par for The Bahamas (0.102 and 0.105, respectively). Then, on the social sensing side, we add two data layers representing relevant tweet text and images posted from the case-study regions to highlight different ways these heterogeneous data sources complement each other. Our extensive analyses reveal several valuable insights. In particular, we identify three types of signals: (i) confirmatory signals from both sources, which puts greater confidence that a specific region is flooded, (ii) complementary signals that provide different contextual information including needs and requests, disaster impact or damage reports and situational information, and (iii) novel signals when both data sources do not overlap and provide unique information.