**4. Experiments**

The 3D pipe network model was implemented and the performance of the visualization is verified. The open source GIS projects are developed to verify the efficiency for the hybrid method of modeling the 3D pipe network. 3D pipe network model can be shown in desktop GIS, mobile GIS, and web GIS. The iDesktop-cross [44] is an open source desktop GIS project [45], which includes 3D pipe network models. The SuperMap iClient GIS (iClient-JavaScript [46]) is a visual analytics framework for WebGIS based application [7]. The pipe network data is shown in the outdoor GIS (Figure 12a) and the indoor GIS (Figure 12b).

**a)** Pipe network model in the outdoor GIS. 

**b)** Pipe network model in the indoor GIS. 

**Figure 12.** Pipeline network model in iDesktop Cross.

Based on the API provided by iClient3D for WebGL [47], the pipe network visualization is shown in Figure 13 and is developed by SuperMap iEarth for WebGL [48].

To test the performance using the methods above, we use the instantiation and non-instantiation techniques to test the performance of the applications of 3D pipeline networks. The experimental environment is in the 64-bit Windows 7 operating system with 4 GB memory, the graphics card uses GTX650 with 2 G memory, and the CPU is an Intel i5-3340 with 3.1 GHz quad-core processor. The test scenario is pipeline data of a certain plant area, the area is about 5 km2, and the pipeline is densely distributed, as shown in Figure 14. We specify the flight route in the scene, then let the camera automatically move along the flight path, recording the frame rate, CPU usage, and memory usage at each moment along the way.

**Figure 13.** Pipeline network in the SuperMap iEarth.

**Figure 14.** Pipeline network in the experiment.

The experimental results are shown in Table 4. By using the instantiation method, the frame rate is increased by about 100%, the CPU usage is reduced to 33%, the memory usage is reduced to 25%, and the memory usage is slightly reduced. Proving that instantiation technology improves pipeline rendering performance is significant.



#### **5. Conclusions and Discussion**

In summary, we propose a hybrid framework for high-performance modeling of 3D pipe network, including pipe network data model and high-performance modeling. The pipe network is decomposed into two sections described in Section 2, and the multi-pass pipe point is decomposed into a set of pipe segments described by two half sections, through the topological relationship between the pipe point and the pipeline. Through this decomposition, complex pipe points can be split into simple units. In this way, a well-matched pipe point pipeline model can be quickly constructed, saving a lot of manual operations and improving the e fficiency of building pipeline scenarios. In addition, the split pipe segmen<sup>t</sup> unit can be easily combined with the instantiation technique for rendering.

The results of the experiments have shown that the use of instantiation technology significantly improves the rendering performance of the 3D pipe networks. 3D pipe network design needs more time. The hybrid method reduces the cost for constructing 3D pipe network scene and improves the rendering performance of 3D pipelines and 3D pipe points. The geospatial application of 3D pipe network is complex. Spatial 3D model (S3M) is proposed for spatial data transmission, exchange, and visualization of massive and multi-source 3D spatial data. Rendering large-scale 3D pipe networks requires intensive computational resources. The combination parallel computing framework with GPU and OpenMP significantly reduces the processing time for large-scale 3D pipe networks. The results of the experiments showed that the hybrid framework achieves a high e fficiency and the hardware resource occupation is reduced.

The hybrid framework for high-performance modeling of 3D pipe network that integrates geospatial applications makes GIS integrate into the high-performance based smart city with unprecedented opportunities. There are however some limitations to this study. (1) This study did not consider spatial cloud computing and edge computing, which is more powerful than hybrid parallel computing. Spatial cloud computing can address the high-performance based challenges for large-scale geospatial applications because spatial cloud computing has the ability to process large-scale 3D GIS models with high performance. Edge computing enables spatial analytics and geospatial data gathering to process near the source of the spatial data. Edge computing has the opportunity to perform real-time 3D pipe network modeling. (2) Our models did not consider artificial intelligence (AI) properties. 3D pipe network data production takes a long time with manual work. It is essential to extract the pipe network data from LiDAR data and remote sensing images using AI-based methods. In future work, we will focus on the integration of spatial cloud computing, edge computing, and machine learning to high-performance based smart city applications.

**Author Contributions:** Conceptualization, Shaohua Wang; data curation, Yinle Sun, Wenwen Cai and Liang Long; funding acquisition, Shaohua Wang and Liang Long; investigation, Shaohua Wang and Yeran Sun; methodology, Shaohua Wang and Hao Lu; project administration, Zhenhua Feng; software, Yinle Sun, Zhenhua Feng and Hao Lu; supervision, Shaohua Wang and Zhenhua Feng; validation, Wenwen Cai and Yong Guan; visualization, Yinle Sun and Yong Guan; writing—original draft, Shaohua Wang and Yeran Sun.

**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities of China (Grant No. 37000-18841208), the National Key R&D Plan (2016YFB0502004), National Postdoctoral International Exchange Program (Grant Number 20150081), Project of Beijing Excellent Talents (201500002685XG242), Independent Research Project of State Key Laboratory of Resources and Environmental Information Systems, Chinese Academy of Sciences (088RAC00YA).

**Acknowledgments:** We would like to acknowledge the Institute of Geographic Sciences and Natural Resources Research of the Chinese Academy of Science for providing a research gran<sup>t</sup> to conduct this work.

**Conflicts of Interest:** The authors declare no conflict of interest.
