Journal
GEO-SPATIAL INFORMATION SCIENCE
Volume 24, Issue 2, Pages 279-301Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10095020.2020.1812445
Keywords
Remote sensing; social media; urban waterlogging; data fusion
Categories
Funding
- China Postdoctoral Science Foundation [2017M622522]
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This study proposes a waterlogging probability assessment framework based on multisource data, integrating social media data and remote sensing images to generate more accurate waterlogging probability maps for disaster monitoring and inundated areas identification.
Urban waterlogging probability assessment is critical to emergency response and policymaking. Remote Sensing (RS) is a rich and reliable data source for waterlogging monitoring and evaluation through water body extraction derived from the pre- and post-disaster RS images. However, RS images are usually limited to the revisit cycle and cloud cover. To solve this issue, social media data have been considered as another data source which are immune to the weather such as clouds and can reflect the real-time public response for disaster, which leads itself a compensation for RS images. In this paper, we propose a coarse-to-fine waterlogging probability assessment framework based on multisource data including real-time social media data, near real-time RS image and historical geographic information, in which a coarse waterlogging probability map is refined by using the real-time information extracted from social media data to acquire a more accurate waterlogging probability. Firstly, to generate a coarse waterlogging probability map, the historical inundated areas are derived from Digital Elevation Model (DEM) and historical waterlogging points, then the geographic features are extracted from DEM and RS image, which will be input to a Random Forest (RF) classifier to estimate the likelihood of hazards. Secondly, the real-time waterlogging-related information is extracted from social media data, where the Convolutional Neural Network (CNN) model is applied to exploit the semantic information of sentences by capturing the local and position-invariant features using convolution kernel. Finally, fine waterlogging probability map scan be generated based on morphological method, in which real-time waterlogging-related social media data are taken as isolated highlight point and used to refine the coarse waterlogging probability map by a gray dilation pattern considering the distance-decay effect. The 2016 Wuhan waterlogging and 2018 Chengdu waterlogging are taken as case studies to demonstrate the effectiveness of the proposed framework. It can be concluded from the results that by integrating RS image and social media data, more accurate waterlogging probability maps can be generated, which can be further applied for inundated areas identification and disaster monitoring.
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