# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 3 Methodology  
The proposed methodology defines the way to develop the point-to-point routing algorithm using LiDAR data. LiDAR data consists of an X, Y, Z point cloud of terrain features of buildings, ground, trees, and other objects [31, 32]. Authors initially tried to establish the routing algorithm over simulated point cloud data of terrain. The simulated site for developing routing algorithm consisted of several buildings, ground, etc. (of varying height, orientation, texture, etc.). This simulated area was designed keeping different terrain complexities in mind. A MATLAB point cloud simulation program was used to generate an unlabeled 3D point cloud of building, ground, etc. Different terrain features were extracted from the point cloud data for further processing. These features were building, ground, vegetation, etc. The edges and corners of buildings were then identified and isolated separately [33].  
The point-to-point routing algorithm was developed over these dense terrain data. The algorithm tried to determine all the possible routes between any pair of source and destination. These routes can be a direct route, routes over the top, routes around the sides, and the reflected routes (elaborated in the Section 4). As there is no such algorithm to determine the route, authors tried to come up with a novel cutting-plane technique [34]. The simulated terrain data were used for route determination. It was also used to determine the efficacy of route determination algorithm. The flowchart for routing algorithm, which can be used for noise prediction, is shown in Figure 1. The point cloud-based routing algorithm was also tested for its accuracy. Simulated terrain point cloud data were used to test various routes. The algorithm was also tried over the LiDAR data of RGIPT campus for noise data prediction.  
Figure 1: Methodology for determination of routes extracting terrain parameter and utilization of same for noise prediction.  
**Route Determination and efficacy for RGIPT campus:** The routing algorithm was also tried to be trained and tested over real LiDAR terrain data and used later for noise prediction using noise modeling. The terrain data were generated for RGIPT campus using Terrestrial Laser Scanner, point cloud data has an accuracy of up to \(\pm 3\) cm (mentioned in Supplementary Section S2). RGIPT campus lies in between (latitude (26.265788), longitude (81.504372)) and (latitude (26.263355), longitude (81.515723)), as shown in Figure 2. The project site has an area of 0.33 km\({}^{2}\). Adjoining the campus, there is a railway line, which is the primary source of noise pollution for the campus [35]. The locations for noise sources and receivers (destinations) in the RGIPT campus were ascertained. All the routes were determined between every pair or source and destination. Next, the terrain parameters were determined for every route and incorporated inside a noise propagation model for noise prediction. Efficacy of noise prediction was also tested with ground noise data for the campus.  
The detailed data processing worked on various stages such as LiDAR data acquisition, building extraction, building corner estimation, and the route determination (Route over the top, routes around the side, and reflections). All these routes were used to determine the terrain parameters and noise mapping. Stepwise processing and results were given below in Figure 3.  
Figure 3: Summary flow chart of stepwise processing of LiDAR data for deriving a point-to-point routing algorithm for noise mapping.  
Figure 2: RGIPT project site LiDAR data were used for noise prediction leveraging point to point routing algorithm.