Spatial Bias Correction of Social Media Data by Exploiting Remote Sensing Knowledge in Data-Deficient Regions

Social media data have shown great potential for disaster response. However, the inherent limitations associated to these data (particu-larly, the spatial bias) restrict its precise application. In this work, we present a new spatial bias correction method based on remote sensing knowledge and spatio-temporal fusion, named locally optimal transport (LOT). Our method is first tested using a case study (2013 Boulder, Colorado flood event). Then, we apply our method to a 2016 Wuhan flood event to test its accuracy in a data deficient region. Our results show that combining remote sensing features and spatio-temporal fusion can help to address problems with a lack of prior data and limited disaster period data. According to the random ground verification points collected from news, pictures and videos, our new LOT method is able to accurately relocate spatially biased social media data to inundated areas, which are dangerous for users.