# Real-Time DTM Generation with Sequential Estimation and OptD Method
## 3 Results and Analysis  
The research was carried out on LiDAR data obtained from the website [[https://pzgik.geoportal.gov.pl/imap/](https://pzgik.geoportal.gov.pl/imap/)]([https://pzgik.geoportal.gov.pl/imap/](https://pzgik.geoportal.gov.pl/imap/)) (date of data download 1 October 2023). The data were collected as part of the ISOK Project (IT system for protecting a country against extraordinary threats) from the aircraft platform (airborne laser scanning--ALS). The datasets N-34-114-B-b-4-3-3 with 4 points/m\({}^{2}\) were obtained in.las format. DTMs often rely on multiple data sources, such as a LiDAR point cloud, satellite imagery, or GPS measurements. These datasets must undergo georeferencing to a common coordinate system to ensure seamless integration. Misaligned data can lead to errors in the model, such as incorrect elevation values, distorted slopes, or inaccurate representation of topographic features. In our concept, we used a single type of data: an ALS point cloud. These data were georeferenced, meaning that spatial coordinates were assigned to each point in the dataset, aligning it with a specific geographic coordinate system. Thisstep ensures the data accurately represent the real-world location and can be integrated with other spatial datasets. The research object which was adopted as block 1 is shown in Figure 3.  
The ALS point cloud was processed using the new concept of DTM generation based on real-time data acquisition with sequential estimation and the OptD method. The stages of processing in the measuring strips are presented in Figure 4 to show how the process takes place over time. The degree of reduction is important during processing, because a smaller set means a faster DTM generation process. Different degrees of reduction were used in the study during testing of the new concept. In order to show what the area looks like at different degrees of reduction, preview figures were made. Four variants of reduction, 50%, 30%, 10%, and 2% (percentage of data remaining in the point cloud) were tested. The entire dataset contained  
Figure 4: Stages of processing with the new concept in block 1. Figure 3: Original ALS point cloud in block 1 (RGB color). 1,998,402 points. After OptD reduction, the following remained in the set: 997,850 points (for 50%), 601,058 points (for 30%), 199,176 points (for 10%), and 40,077 points (for 2%). The final effect after reduction in all strips is presented in Figure 5. The choice of the degree of reduction depends on the user, the density of points, and the purpose of the generated DTM. Not only different degrees of reduction, but also different widths of measuring strips (15 m--v.1 and 25 m--v.2) were used during processing. The width of the measuring strips depends on the flight altitude and the scanning angle during data acquisition. The new concept was used to generate DTMs. The generated models can be stored as a binary file or as a characteristics file: *.txt. For the generated DTMs, the parameter \(m_{0}^{2}=\frac{\phi^{2}P\phi}{n-m}\), describing their quality was determined. That parameter was calculated for each sequence, which means that it is different in different places of the generated DTM. Values of \(m_{0}\), maximum \(m_{0}\) (necessary during processing with sequential estimation), function and model parameters, the average time for creating models in sequences, extreme coordinate values, the name of the input data file, and the number of input data are stored in the form of the characteristics file. Characteristics files were prepared for variants v.1 and v.2 taking into account four options of reduction. A fragment of such a file is presented in Appendix A. In order to assess the quality of the generated DTMs using the new concept, it was necessary to generate a model using an existing method. The nearest neighbor method was chosen [43].