A multi-source spatiotemporal dataset of floods in Chengdu (2018)

With the continuous development of disaster prevention, mitigation and relief means, disaster-related data are becoming increasingly effective and extensive. In flood events, traditional remote sensing images, terrestrial GIS data, and socio-economic development data are widely recognized as the major sources of disaster data. In the meantime, social media data emerged as a "mobile sensor" of disasters, which have been widely used for its extensive participation and multi-source dissemination. Based on the concept of multi-source spatiotemporal data fusion, this paper collects and collates river situation data, remote sensing images, Sina Weibo data, terrain condition and socio-economic development data of the region in different periods in the flood event. On the basis of traditional disaster data, this study engages a public perspective of the disaster area to show the impact of flood disaster and the progress of the disaster, which provides complete and effective data support for subsequent flood risk zoning, flooding range modeling and disaster assessment.