Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a threat environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common threat model is constructed for threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.

Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) systems can potentially generate high precision Digital Terrain Models (DTM), especially for railway surveys. However, traditional feature points do not exist in LiDAR systems due to discrete sampling. Artificial control targets (ACTs) can be used as ground control points (GCPs) in a LiDAR system. In this paper, we present a new type of ACT suitable for a UAV-LiDAR system. The design of X-type ACTs and railway survey experiments with a multi-rotor UAV-LiDAR system are illustrated. Six ACTs are settled in the study area. The root mean square errors (RMSE) in the x, y direction are 0.15 m and 0.13 m, respectively, from collected multi-rotor UAV-LiDAR data. Planimetric errors in the LiDAR data were corrected with the six ACTs. With 22 horizontal and 32 elevation checkpoints, the accuracy estimation results demonstrate that RMSE values in the x, y, z directions are 0.06 m, 0.07 m and 0.030 m, respectively. The design of X-type ACTs, accuracy estimations and improvements for railway surveys with the multi-rotor UAV-LiDAR system presented in this paper will provide a reference for 3D measurements along other railways.

Most simulation-based noise maps are important for official noise assessment but lack local noise characteristics. The main reasons for this lack of information are that official noise simulations only provide information about expected noise levels, which is limited by the use of large-scale monitoring of noise sources, and are updated infrequently. With the emergence of smart cities and ubiquitous sensing, the possible improvements enabled by sensing technologies provide the possibility to resolve this problem. This study proposed an integrated methodology to propel participatory sensing from its current random and distributed sampling origins to professional noise simulation. The aims of this study were to effectively organize the participatory noise data, to dynamically refine the granularity of the noise features on road segments (e.g., different portions of a road segment), and then to provide a reasonable spatio-temporal data foundation to support noise simulations, which can be of help to researchers in understanding how participatory sensing can play a role in smart cities. This study first discusses the potential limitations of the current participatory sensing and simulation-based official noise maps. Next, we explain how participatory noise data can contribute to a simulation-based noise map by providing (1) spatial matching of the participatory noise data to the virtual partitions at a more microscopic level of road networks; (2) multi-temporal scale noise estimations at the spatial level of virtual partitions; and (3) dynamic aggregation of virtual partitions by comparing the noise values at the relevant temporal scale to form a dynamic segmentation of each road segment to support multiple spatio-temporal noise simulations. In this case study, we demonstrate how this method could play a significant role in a simulation-based noise map. Together, these results demonstrate the potential benefits of participatory noise data as dynamic input sources for noise simulations on multiple spatio-temporal scales.

GIS is now faced with the challenge how to represent and understand the fast-paced, constantly changing world, given increasingly real-time data including readings from large-scale distributed sensors and large quantities of simulation data generated by computation models. Traditional static GIS pays more attention to representing historic data and Temporal GIS(TGIS) only treats time as a occasional but not critical factor and can't support the explicit change representation. In this context, Real-time GIS(RGIS) is put forward and its data model becomes a key problem how to make an appropriate abstraction over the rapid changes implied in real-time data stream and establish the general interaction relationship between them. The paper proposes a spatiotemporal changeoriented three-domain model with the emphasis on the semantic interaction relationship of object, event and process. Based on this model, a semantic enrichment method for multi-scale spatiotemporal change is put forward. Finally, as a RGIS application, indoor fire disaster simulation is illustrated and proves the validity of the proposed model.

With the two cores of database and model-base, Virtual Geographic Environment (VGE) breaks the limitations of the traditional Geographic Information System (GIS), which provides new support to the study and understanding of our world.