Abstract
Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent years. However, social media also has certain flaws, such as insufficient location information, etc. This affects the efficiency of combining these data with remote sensing data. For flood disasters in particular, extensively flooded areas limit the distribution of social media data, which makes it difficult for these data to function as they should. In this paper, we propose a disaster reduction framework to solve these problems. We first used an approach that was based on search engine and lexical rules to automatically extract disaster-related location information from social media texts. Then, we combined the extracted information with the upload location of social media itself to construct location-pointing relationships. These relationships were used to build a new social network, which can be used in combination with remote sensing images for disaster analysis. The analysis integrated the advantages of social media and remote sensing. It can not only provide macro disaster information in the study area but can also assist in evaluating the disaster situation in different flooded areas from the perspective of public observation. In addition, the timeliness of social media data also improved the continuity and situational awareness of flood monitoring. A case study of the flood disaster in the Yangtze River Basin in China in 2020 was used to verify the effectiveness of the method described in this paper.
Highlights
IntroductionWith the intensification of global climate change, meteorological disasters such as heavy rains and floods frequently occur [1,2]
We propose a framework that aims to improve the efficiency of combining social media data with remote sensing data in order to mine more disaster information from disaster-affected areas
For social media, based on the different location information of social media, we constructed a new social network that can describe pointing relationships between spatial locations
Summary
With the intensification of global climate change, meteorological disasters such as heavy rains and floods frequently occur [1,2]. This has caused a large number of casualties and property losses, which seriously affect the sustainable development of society [3]. Due to the development of science and technology, Earth observation methods represented by remote sensing have played an important role in disaster reduction [4,5]. They provide detailed snapshots of conditions that cover a wide range of disaster areas, which are convenient for disaster assessment and auxiliary rescue [6].
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