## 3 Proposed Work
### State Space Model
The organized PCs facilitate the identification of clusters, and each cluster is assigned a distinctive color index value. Buildings are assigned the color red, trees the color green, concrete the color yellow, and unknowns the color purple. The remaining clusters are not included in the pre-learning process. The PC data are organized in a specific order, with buildings, trees, lanes, concrete benches, an open terrace, and other features clearly visible in this frame. The ground truth value is utilized to extract the building points from the organized LiDAR PC. The Self-Organizing Map (SOM) clustering topology is created by passing the ground truth value to SOM as an

Figure 6: PC data distribution. input variable. Approximately 700 features are distinguished from the PC, with the main 200 elements being the target. Although the Mono model has high accuracy, it needs an important amount of time complexity. The process of detecting features using the Mono model requires a maximum of 100 iterations, a relaxation factor of 0.5, and a pyramid level of 3. ## 6 Results and Discussion

Python and C++ were used to create the point cloud categorization system. The point cloud library (PCL) was modified to conduct point cloud downsampling and feature extractions in order to process the point clouds quickly. The LiDAR and camera data algorithms were implemented in the Scikit-learn framework for the classification procedure. The implementation of both classification methods used parallel computing. Consequently, both the training and testing procedures were sped up using a multicore processor. Python version 2.7 was used to put the suggested tree location recognition and building footprint extraction techniques into practice. Our framework has the benefit of being made up of four separate components, which can be individually evaluated, giving us the ability to thoroughly examine any possible configuration. The free software OpenDroneMap was then used to process the photos that had been gathered. Using the gathered photos, this software reassembles point clouds and detailed 3D models of the survey objects. CloudCompare technique was used to analyze the 3D models' and point clouds' degree of accuracy. Moreover, Jupiter Notebook's implementation of all the phases of the technique we describe here for LiDAR data uses the Python programming language and open-source tools, making it free to use on any machine. In this part, we briefly explain the key findings from our research for each component. The use of individual, optimally sized neighborhoods provides a generic strategy for the first part of neighborhood selection, eliminating the need for prior knowledge of the scene that would be required when specifying neighborhoods using traditional methods. The concept that the most suitable neighborhood size may differ between different classes and also rely on the correct point density is reinforced by the use of each 3D neighborhoods. According on the results of our classifying particular groups, neighborhood definitions with set scale parameters may not be applicable across all groups [47]. Many tests have been run to improve this method throughout this effort. These tests were carried out on the grounds of Active Space Technologies and involved recording bags utilizing the depth with multiple sorts of obstacles placed at varied distances. We evaluated the method for both stationary and moving obstructions in both indoor and outdoor settings. On the other hand, the eigen entropy-based scale selection method that has been suggested is capable of immediately adjusting for the provided 3D PC data, resulting in enhanced classification results, particularly with regard to the mean class recall values. This enhancement has been observed across a broad range of classifiers. The substantial impact on the smaller classes' \"wire\" and \"pole/trunk\" is due to the positive effect of the optimal size of individual neighborhoods on mean class recall values.