The latest progress of data fusion for integrated disaster reduction intelligence service

Looking back over the past decade, superstorms, wildfires, floods, geological hazards, and monster earthquakes have taken unimaginable tolls all over the planet. In 2020, nearly 138 million people suffered from various natural disasters throughout China, where 591 people died and disappeared, and 5.89 million people were relocated for emergency resettlement. This led to direct economic losses of 370.15 billion CNY. With the advances of data acquisition technologies, i.e. remote sensing and Internet of Things, disasterrelated data can be collected rapidly and easily. However, disaster-related data vary in the acquiring methodology and, as such, vary in geographic scope and resolution; thus, how to fuse various disaster-related data is of significance for emergency disaster reduction (Liu et al. 2020). Disaster-related data are essential in understanding the impacts and costs of disasters, and data fusion plays an essential role in disaster prediction, reduction, assessment, and intelligent services. Using multisource data can improve the information availability and quality derived at various levels (Liu et al. 2018, Liu et al. 2020). Especially, for the emergency response, it is particularly imperative to integrate multisource data to provide the latest, accurate and timely information with various scales for disaster reduction services. For example, a large-scale landslide occurred in the Jinsha River Basin at the border of Sichuan and Tibet on 10 October 2018 and formed the barrier lake, which posed a great threat to the lives and property of people in the downstream Jinsha River region (Qiu et al. 2017, Li et al. 2020a). Using disaster multisource data fusion (Gamba 2014), spatiotemporal process simulation (Wang et al. 2020), visual analysis and risk assessment (Li et al. 2020), and disaster information intelligent service, decision-making information were generated to support disaster emergency management (Liu et al. 2018). This special issue on Data Fusion for Integrated Disaster Reduction Intelligence Service focuses on the latest theoretical and technical issues related to disaster-related data fusion, which aims at clarifying the current research progress to provide an opportunity to learn and communicate with each other in this field. This special issue is supported by the National Key Research and Development Program of China under Grant No. 2016YFC0803101 and includes six articles spanning various topics. Specifically, an improved frequency domain integration approach is proposed by combining GNSS and Accelerometers using GNSS to gain an accurate initial position to reconstruct dynamic displacements. An online emergency mapping framework based on a disaster scenario model is introduced for mapping, knowledge rules, mapping templates, map symbol engines, and a simple wizard to shorten the mapping cycle in emergencies. A suitability visualisation method is realised for flood fusion 3D scene guided by disaster information through the fusion of the basic geography scene, the flood time-space process, and the disaster object models, thus helping users quickly obtain flood disaster information. An unsupervised method of Chinese addresses INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION 2021, VOL. 12, NO. 4, 265–267 https://doi.org/10.1080/19479832.2021.1970931