Wenhang Li

A visualization-oriented 3D method for efficient computation of urban solar radiation based on 3D–2D surface mapping

The temporal and spatial distribution of solar energy in urban areas is highly variable because of the complex building structures present. Traditional GIS-based solar radiation models rely on two-dimensional (2D) digital elevation models to calculate insolation, without considering building facades and complicated three-dimensional (3D) shading effects. Inspired by the ‘texture baking’ technique used in computer graphics, we propose a full 3D method for computing and visualizing urban solar radiation based on image-space data representation. First, a surface mapping approach is employed to project each 3D triangular mesh onto a 2D raster surface whose cell size determines the calculation accuracy. Second, the positions and surface normal vectors of each 3D triangular mesh are rasterized onto the associated 2D raster using barycentric interpolation techniques. An efficient compute unified device architecture -accelerated shadow-casting algorithm is presented to accurately capture shading effects for large-scale 3D urban models. Solar radiation is calculated for each raster cell based on the input raster layers containing such information as slope, aspect, and shadow masks. Finally, a resulting insolation raster layer is produced for each triangular mesh and is represented as an RGB texture map using a color ramp. Because a virtual city can be composed of tens of thousands of triangular meshes and texture maps, a texture atlas technique is presented to merge thousands of small images into a single large image to batch draw calls and thereby efficiently render a large number of textured meshes on the graphics processing unit.

Visualizing 3D atmospheric data with spherical volume texture on virtual globes

Abstract Volumetric ray-casting is a widely used algorithm in volume visualization, but implementing this algorithm to render atmospheric volume data that cover a large area on virtual globes constitutes a challenging problem. Because atmospheric data are usually georeferenced to a spherical coordinate system described by longitude, latitude and altitude, adaptations to the conventional volumetric ray-casting method are needed to accommodate spherical volume texture sampling. In this paper, we present a volumetric ray-casting framework to visualize atmospheric data that cover a broad but thin geographic area (because of the thinness of Earth׳s atmosphere). Volume texture conforming to the spherical coordinate system of a virtual globe can be created directly from the spherical volume data to avoid oversampling, undersampling or a loss of accuracy due to reprojecting and resampling such data into a Cartesian coordinate system. Considering the insignificant physical thickness of the atmosphere of the Earth, the ray-casting method presented in this paper also allows for real-time vertical scaling (exaggeration of the altitudinal range) without the need to re-process the volume texture, enabling convenient visual observation of the altitudinal variations. The spherical volume ray-casting method is implemented in a deferred rendering framework to integrate the volume effects into a virtual globe composed of a variety of background geospatial data objects, such as terrain, imagery, vector shapes and 3D geometric models.

Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach

Hemispherical (fisheye) photography is a well-established approach for estimating the sky view factor (SVF). High-resolution urban models from LiDAR and oblique airborne photogrammetry can provide continuous SVF estimates over a large urban area, but such data are not always available and are difficult to acquire. Street view panoramas have become widely available in urban areas worldwide: Google Street View (GSV) maintains a global network of panoramas excluding China and several other countries; Baidu Street View (BSV) and Tencent Street View (TSV) focus their panorama acquisition efforts within China, and have covered hundreds of cities therein. In this paper, we approach this issue from a big data perspective by presenting and validating a method for automatic estimation of SVF from massive amounts of street view photographs. Comparisons were made with SVF estimates derived from two independent sources: a LiDAR-based Digital Surface Model (DSM) and an oblique airborne photogrammetry-based 3D city model (OAP3D), resulting in a correlation coefficient of 0.863 and 0.987, respectively. The comparisons demonstrated the capacity of the proposed method to provide reliable SVF estimates. Additionally, we present an application of the proposed method with about 12,000 GSV panoramas to characterize the spatial distribution of SVF over Manhattan Island in New York City. Although this is a proof-of-concept study, it has shown the potential of the proposed approach to assist urban climate and urban planning research. However, further development is needed before this approach can be finally delivered to the urban climate and urban planning communities for practical applications.

Social Force Model-Based Group Behavior Simulation in Virtual Geographic Environments

Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals, but it has not sufficiently accounted for inter-group and intra-group behaviors which are important components of crowd dynamics. We present the social group force model (SGFM), based on an extended SFM, to simulate group behaviors in VGEs with focuses on the avoiding behaviors among different social groups and the coordinate behaviors among subgroups that belong to one social group. In our model, psychological repulsions between social groups make them avoid with the whole group and group members can stick together as much as possible; while social groups are separated into several subgroups, the rear subgroups try to catch up and keep the whole group cohesive. We compare the simulation results of the SGFM with the extended SFM and the phenomena in videos. Then we discuss the function of Virtual Reality (VR) in crowd simulation visualization. The results indicate that the SGFM can enhance social group behaviors in crowd dynamics.

A Heterogeneous Distributed Virtual Geographic Environment—Potential Application in Spatiotemporal Behavior Experiments

Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR) devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough, and will continue to coexist with personal computer (PC)-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to the heterogeneous problems caused by various types of clients, and support the implementation of spatiotemporal crowd behavior experiments with large numbers of concurrent participants. In this study, we developed an HDVGE framework, and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture, and proposed an abstract interaction layer, a protocol-based interaction algorithm, and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system: PC, and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency local area network (LAN) environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE provides a feasible solution for studying not only spatiotemporal crowd behaviors in normal conditions, but also evacuation behaviors in emergency conditions such as fires and earthquakes. HDVGE could also serve as a new means of obtaining observational data about individual and group behavior in support of human geography research.

Improved Cubemap model for 3D navigation in geo-virtual reality

Due to advances in rendering techniques and hardware capability, stereoscopic 3D (s3D) visualization is becoming increasingly common in daily life. However, this does not change the fact that stereo effects and visual comfort depend greatly on how the related parameters are controlled during the production of the s3D images. In geo-virtual reality systems, which are important browsers for Digital Earth, the maintenance of these parameters is deeply related to the navigation process. Therefore, the navigation method in such systems requires special care. This paper presents a new flying method based on a Cubemap structure. The method defines a Vehicle model and modifies the original Cubemap structure by adding a front view camera during the navigation; it allows the users to fly through a virtual geographic environment with automatic speed control, smooth collision resolution, and dynamic adjustment of the s3D-related parameters. A user test was conducted to compare this new method with the original method based on the Cubemap structure. The results show that the new method performs better than the former one for it provides a convenient interaction experience with improved stereoscopic effect, and diminishes visual discomfort.