## 3 Proposed Work
### State Space Model
As a result, the use of individual, optimally sized neighborhoods reduce the likelihood of overfitting for classifiers. This is evident from the unbalanced test set in the Oakland 3D PC Dataset, where an overall accuracy of 70.5% could be achieved by correctly categorizing instances of the \"ground\" class, but a trend toward overfitting of 20.0% is observed when considering the mean class recall. Therefore, in our experiments, although the overall accuracy may not be adequate, the mean class recall is a more reliable indicator of the quality of the outcomes. For the investigation of the proposed system, a PCAP file was taken in the evening of a minor part of the institute's building, roads, trees, benches, lanes, pillars, etc. The LiDAR data consist of 25 recognized classes and 28 unidentified classes. The Velovierer tool is used to convert it into a CSV file for processing after opening the raw data. The CSV file is loaded and a PC object is created. The dataset includes nearly 100 frames per PC, which are transformed and stored in separate locations. The labeling process involves selecting a ground truth and uploading the image structure to generate the labels. New labels are added to the ROIs and exported to the workspace for additional reference. This object can be used as input for further processing. The connection between each point is generated using the K-Nearest Neighbor (KNN) algorithm, which categorizes 53 varieties of segmented objects. Of these, 9 are labeled with pre-defined terms. Among them, one class is labeled as \"unknown\". Clusters are identified from the prearranged PC. Each cluster has a unique CI. Different colors are assigned for each label, with red assigned to buildings, yellow to concrete, green to trees, and purple to unknown objects. The remaining objects are not considered for the pre-learning process. Figure 7 shows a color-based classified image frame, which clearly indicates that Block 4 and 6 consist of buildings, trees, benches, lanes, etc. Figure 7, on the right, shows the PC's contour image. The labeling process begins with the selection of a ground truth, followed by the uploading of the picture structure so that the labels may be generated. Updated with new labels, and the labels are then exported to the workspace for further reference. Figure 8 depicts the SOM hit for a feature building. Figure 9 depicts SOM neighbor weight distance. \"An unsupervised machine learning method called a self-organizing map (SOM) or self-organizing feature map (SOFM)\" is used to create a low-dimensional (usually 2D) representations of a higher-dimensional data collection while maintaining the topological architecture of the data. A data collection containing parameters calculated in assumptions, for instance, could be described as groups of findings with related variable values. A two-dimensional \"map\" of these clusters might then be used to represent them, with study in proximal clusters having more similar values than research in distal clusters. High-dimensional data may be easier to display and interpret as a result. In Figure 10, the refurbished 2D image of the PC data are displayed. It is noticeable that the image is not well defined and lacks complete information, but the absent information is reconstructed to enable further analysis. The image frames are incorporated into the training set for testing, and the testing period lasts for 3 days or more to verify the outcomes. Some objects are not accurately classified, and the errors observed from the results are eliminated from the image of interest. The error rate of the samples is illustrated in Figure 11.

Figure 10: Reconstructed 2D Image from PC data. Figure 9: Weight Distance. Table 5 shows the feature recognition using various models in image registration. Approximately 700 features were recognized from the PC, and 200 of these features were predicted. The mono-model provides higher accuracy, but requires more time. Feature detection takes a maximum of 100 iterations, with a relaxation factor of 0.5 and a pyramid level of 3.