The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks

Virtual Landslide Disaster environments are important for multilevel simulation,analysis and decision-making about Landslide Disasters.However,in the existing related studies,complex disaster scene objects and relationships are not deeply analyzed,and the scene contents are fixed,which is not conducive to meeting multilevel visualization task requirements for diverse users.To resolve the above issues,a construction method for Personalized Virtual Landslide Disaster Environments Based on Knowledge Graphs and Deep Neural networks is proposed in this paper.The characteristics of relationships among users,scenes and data were first discussed in detail;then,a knowledge graph of virtual Landslide Disaster environments was established to clarify the complex relationships among disaster scene objects,and a Deep Neural network was introduced to mine the user history information and the relationships among object entities in the knowledge graph.Therefore,a personalized Landslide Disaster scene data recommendation mechanism was proposed.Finally,a prototype system was developed,and an experimental analysis was conducted.The experimental results show that the method can be used to recommend intelligently appropriate disaster information and scene data to diverse users.The recommendation accuracy stabilizes above 80%–a level able to effectively support The Construction of Personalized Virtual Landslide Disaster environments.

Damage and geological assessment of the 18 September 2011 M_w 6.9 earthquake in Sikkim,India using very high resolution satellite data

Post-disaster very high resolution（VHR） satellite data are potential sources to provide detailed information on damage and geological changes for a large area in a short time.In this paper,we studied landslides triggered by the M_w 6.9 earthquake in Sikkim,India which occurred on 18 September 2011 using VHR data from Cartosat-1,GeoEye-1,QuickBird-2 and WorldView-2 satellites.Since the earthquake-affected area is located in mostly inaccessible Himalayan terrain,VHR data from these satellites provided a unique opportunity for quick and synoptic assessment of the damage.Using visual change analysis technique through comparison of pre- and post-earthquake images,we assessed the damage caused by the event.A total of 123 images acquired from eight satellites,covering an area of4105 km2 were analysed and 1196 new landslides triggered by the earthquake were mapped.Road blockages and severely affected villages were also identified.Geological assessment of the terrain highlighted linear disposition of landslides along existing fault scarps,suggesting a reactivation of fault.The landslide inventory map prepared from VHR images also showed a good correlation with the earthquake shake map.Results showed that several parts of north Sikkim,particularly Mangan and Chungthang,which are close to the epicentre,were severely affected by the earthquake,and that the event-based landslide inventory map can be used in future earthquake-triggered landslide susceptibility assessment studies.