Abstract
With the design and development of smart cities, opportunities as well as challenges arise at the moment. For this purpose, lots of data need to be obtained. Nevertheless, circumstances vary in different cities due to the variant infrastructures and populations, which leads to the data sparsity. In this paper, we propose a transfer learning method for urban waterlogging disaster analysis, which provides the basis for traffic management agencies to generate proactive traffic operation strategies in order to alleviate congestion. Existing work on urban waterlogging mostly relies on past and current conditions, as well as sensors and cameras, while there may not be a sufficient number of sensors to cover the relevant areas of a city. To this end, it would be helpful if we could transfer waterlogging. We examine whether it is possible to use the copious amounts of information from social media and satellite data to improve urban waterlogging analysis. Moreover, we analyze the correlation between severity, road networks, terrain, and precipitation. Moreover, we use a multiview discriminant transfer learning method to transfer knowledge to small cities. Experimental results involving cities in China and India show that our proposed framework is effective.
Highlights
With the design and development of smart cities, opportunities and challenges arise at the moment
Circumstance vary in different cities due to the variant infrastructures and populations, which leads to the data sparsity, Bassoli et al [1]
This paper’s major contributions can be described in three aspects: (1) We propose a multiview discriminant transfer learning method between cities for urban waterlogging disaster analysis
Summary
With the design and development of smart cities, opportunities and challenges arise at the moment For this purpose, a huge amount of physical sensor and social media data need to be obtained. Motivated by the uniqueness of the information available on social media and through satellites and the close relationship between this information and the severity of urban waterlogging, we set ourselves the task of determining whether we can retrieve the relevant Twitter and satellite data and transfer the knowledge conveyed by these to small cities to analyze urban waterlogging, Wu et al [8]. (1) We propose a multiview discriminant transfer learning method between cities for urban waterlogging disaster analysis.
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