Virtual scenes can present rich and clear disaster information, which can significantly improve the level of public disaster perception. However, existing methods for constructing scenes of debris flow disasters have some deficiencies. First, the construction process does not consider public knowledge, which makes it difficult for the constructed scenes to meet the requirements of the public. Second, the scene representation emphasizes visual effects but lacks augmented visualization, leading to scarcity of semantic information and inefficient public perception. In this paper, the optimal selection of scene objects, semantic augmentation through the combination of various visual variables and dynamic augmented representation are discussed in detail. Finally, a debris flow that occurred Shuimo town is selected for experiment analysis. The experimental results show that most people are unaware of the risks posed by debris flow disasters. The public is more concerned about the consequences of a disaster than its spatiotemporal process, especially when the consequences are related to their own interests. Furthermore, an augmented representation can increase the amount of semantic information of scene objects, which is essential for enhancing public understanding of the causes, processes and effects of debris flows and thereby changing people’s attitudes and enhancing their risk perception.

The automatic extraction of building outlines from high-resolution images is an important and challenging task. Convolutional neural networks have shown excellent results compared with traditional building extraction methods because of their ability to extract high-level abstract features from images. However, it is difficult to fully utilize the multiple features of current building extraction methods; consequently, the resulting building boundaries are irregular. To overcome these limitations, we propose a method for extracting buildings from high-resolution images using a multifeature convolutional neural network (MFCNN) and morphological filtering. Our method consists of two steps. First, the MFCNN, which consists of residual connected unit (RCU), dilated perception unit (DPU), and pyramid aggregation unit (PAU), is used to achieve pixel-level segmentation of the buildings. Second, morphological filtering is used to optimize the building boundaries, improve the boundary regularity, and obtain refined building boundaries. The Massachusetts and Inria datasets are selected for experimental analysis. Under the same experimental conditions, the extraction results achieved with the proposed MFCNN are compared with the results of other deep learning models that have been commonly used in recent years: FCN-8s, SegNet, and U-Net. The results on both datasets reveal that the proposed model improves the F1-score by 3.31%-5.99%, increases the overall accuracy (OA) by 1.85%-3.07%, and increases the intersection over union (IOU) by 3.47%-8.82%. These findings illustrate that the proposed method is effective at extracting buildings from complex scenes.

To improve the efficiency of planning and designing silt dam systems, this article employs theories and technologies of collaboration and distributed virtual geographic environments (VGEs) to construct a collaborative virtual geographic environment (CVGE) system. The CVGE system provides geographically distributed users with a shared virtual space and a collaborative platform to implement collaborative planning. Many difficulties have been found in integrating data resources and model procedures for the planning of silt dam systems because of their diversity in heterogeneous environments. Unlike most of the current distributed system applications, the proposed CVGE system not only supports multi-platform and multi-program-language interoperability in the dynamically changing network environment, but also shares programs, data and software in the collaborative environment. Based on creating a shared 3D space by virtual reality technology, agent and grid technologies were tightly coupled to develop the CVGE system. A grid-based multi-agent system service framework was designed to implement this new paradigm for the CVGE system, which efficiently integrates and shares geographically distributed resources as well as having the ability to build modelling procedures on different platforms. At the same time, mobile agent computing services were implemented to reduce the network load, process parallel tasks, enhance communication efficiency and adapt dynamically to the changing network environment. Using Java, JMF (Java Media Framework API), Globus Toolkits (GT) core, Voyager, C++, and the OpenGL development package, a prototype system was developed to support silt dam systems planning in the case study area, the Jiu-Yuan-Gou watershed of the Loess Plateau, China. Compared with the traditional workflow, the CVGE system can reduce the workload by between one third and a half.

Simulation and subsequent visualization in a network environment are important to glean insights into spatiotemporal processes. As computing systems become increasingly diverse in hardware architectures, operating systems, screen sizes, human–computer interactions and network capabilities, effective simulation and visualization must become adaptive to a wide range of diverse devices. This paper focuses on the optimization of simulation and visualization analysis of the dam-failure flood spatiotemporal process for diverse computing systems. First, an adaptive browser/server architecture of the dam-failure simulation application was designed to fill the hardware performance and visualization context gap that exists within diverse computing systems. Second, a data flow and an optimization method for multilevel time-series flood data were given to provide more support to network simulation, visualization and analysis on diversified terminals. Finally, a user interaction friendly and plugin-free prototype system was developed. The experiment results demonstrate that the methods addressed in this paper can cope with the challenge in simulation, visualization and interaction of a dam-failure simulation application on diversified terminals.

Constructing 3D railway scenes helps improve the railway information construction ability and the comprehensive railway management level. However, the existing modeling methods have disadvantages such as low flexibility in scene configuration, weak extensibility of scene objects and poor reusability of modeling knowledge. To enable multitype 3D railway scenes to be automatically constructed, a template-based knowledge reuse method was proposed in this paper. First, based on parsing of modeling operation characteristics and modeling knowledge expression, a modeling knowledge template was designed to store modeling knowledge and modeling operations in a parameterized way. The contents of this template were described in terms of semantic information, geometric information, topologic information and reference system information, and the method for instantiating the modeling knowledge template was proposed. Second, a scene configuration template was designed to organize the instantiated modeling knowledge files and scene objects. By the use of a flexible tree structure, the scene configuration template enables extensibility of scene objects. Finally, the two types of templates were parsed in terms of scene organization, linear referencing information, geometric and topologic relationships, and semantic descriptions. In this way, different types of 3D railway scenes were generated automatically. After a prototype system was developed, experiments on automatic modeling of two 3D railway scenes were carried out to verify the template-based knowledge reuse method. The experimental results show that the proposed method can be used to generate multitype 3D railway scenes automatically. With this method, model combination can be configured, model types can be extended and modeling knowledge can be reused.