# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 6 Future Scope  
The current study was focused on determination of a point-to-point routing algorithm taking LiDAR data as terrain input. The routing algorithm worked in two stages, i.e., extraction of building, ground and other terrain points from LiDAR data in the first stage, and then extraction of detailed routes between a source point and a receiving point in the second stage. The approach determined all the possible routes for propagation in 3D for every pair of source and receiver points and utilized it for sound propagation modelling integrating terrain and noise data with noise propagation model. At present, the extraction of building, ground and other terrain features between source and receiver points are accurate, but semi-automatic in nature. In the future, the entire process is planned to be made automatic, thus the algorithm will determine the shortest 3D routes directly after receiving the LiDAR data, and noise data of noise sources for all the noise receiving locations. It would then automatically determine the 3D noise map for an area accurately. The routing algorithm has an important role to play in sound propagation modelling. Routing algorithms are important for various applications, i.e., route planning, travelling salesman problem, laying out of pipeline, determination of sun angle, viewshed analysis, etc. An automatic and efficient 3D routing algorithm working over detailed terrain data can improve the performance of all the above applications. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/asi5030058/s1](https://www.mdpi.com/article/10.3390/asi5030058/s1)]([https://www.mdpi.com/article/10.3390/asi5030058/s1](https://www.mdpi.com/article/10.3390/asi5030058/s1)), Figure S1. Noise prediction. Figure S2. The two figures in the Lima Modeling: (a) Principle of the line segmentation using projection; (b) Demo of Lima technique to create model and noise map. Figure S3. Three figures show the principle workingof Nordic Noise propagation model to calculate: (a) Generated receiver for Noise prediction; (b) non-barrier angle and its bisector; (c) Barrier angle and its bisector; (d) Noise map of an area. Figure S4. (a) Step to proceed SoundPlan Techniques; (b) Graphic screen (Geo-database); (c) Model creation in SoundPlan Modeling; (d) Isolines creation by means of direct and indirect routes. Figure S5. Diagram showing results of Dijkstra and other existing algorithms. Figure S6. (a) Shows the area of RGIPT from google map; (b) The plot shows LiDAR point data for the area in step 1; (c) Extracted Ground points from 3D point cloud data. (d) Elevation data after Subtraction of BEM from DSM where building of RGIPT campus. Figure S7. (a) Non-Ground points are now classified for extraction of building coordinates, for this class 1 is mentioned for Regular shaped and class 2 is for non-regular shape; (b) Planimetric view of non-ground points after classification in different classes, the partition is done in order to find out the building edges and corners; (c) 1,2,3,4,5,6,7,8 and 9,10,11,12,13,14,15,16 are the building corners of AB1 & AB2 extracted from the algorithm. Figure S8. (a) The route over the top of the building for setup contains slanted building is S-1-D. (Values in x and y-axis are in meter); (b) The route over the top of the building for the hut-shaped building is S-1-2-3-D (Values in x and y-axis are in meter); (c) Source-1-2-Destination is the route over the top for the academic area of RGIPT between the pair of Source-Destination. Figure S9. (a) Source-1-destination is the left-side route around the sides of the building for an academic area of the RGIPT campus. It consists of AB1 and AB2. Source point and destination point are taken manually to discuss the case for the project area, (b) source-1-destination is the right-side route around the sides of the building for an academic area of the RGIPT campus. It consists of AB1 and AB2. Source point and destination point are taken manually to discuss the case for the project area. Figure S10.