# Real-Time DTM Generation with Sequential Estimation and OptD Method
## 2 Materials and Methods
### Data Reduction with the OptD Method
By adjusting the strip width, the method can manage the amount of data being processed and ensure that the desired point retention criteria are fulfilled. After each iteration, the algorithm evaluates the remaining points against the set criteria. If the percentage of retained points is above or below the target, the method iteratively refines the tolerance and strip width until the desired conditions are satisfied. In this way, it is possible to select the optimal dataset, in terms of the introduced optimization criterion, and the user may decide how many observations will remain in the reduced dataset. The OptD method effectively reduces the size of point cloud datasets while retaining essential structural and geometric features. It adapts to the characteristics of the dataset, ensuring optimal reduction even for irregular or complex terrains. The OptD method improves computational performance by significantly reducing the number of points, the method accelerates subsequent processing steps, such as modeling or analysis. Despite reducing the data size, the method maintains high fidelity in representing critical features, ensuring accuracy in the DTM. The OptD method also has some limitations. The effectiveness of the method depends heavily on the choice of parameters, such as tolerance, which may require fine-tuning for different datasets. In cases where the data contain significant noise or outliers, preprocessing may be necessary to achieve optimal results. These limitations can be eliminated by testing the reduction results using different tolerance ranges and parameter settings. Each dataset is different and requires preliminary testing first. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reduction Method** & **Principle** & **Data Retention** & \begin{tabular}{c} **Computational** \\ **Efficiency** \\ \end{tabular} & **Impact on Accuracy** \\ \hline \multirow{4}{*}{Octree-based downsampling [22]} & Hierarchically & \multirow{2}{*}{\begin{tabular}{c} High \\ cloud and selects \\ representative points at \\ each level. \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Moderate \\ (adaptive \\ retention) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Low \\ (preserves key \\ structures well) \\ \end{tabular} } \\  & Prioritizes points with & & & \\ \multirow{2}{*}{Feature-based reduction [18]} & high curvature or & \multirow{2}{*}{\begin{tabular}{c} Very high \\ (retains essential \\ features) \\ \end{tabular} } & Low & Very Low \\  & & & (computationally \\  & & & & \\ \multirow{2}{*}{OptD method (_proposed_) [23] & Selects optimal points & & & \\  & based on optimization & Customizable & \
\begin{tabular}{c} High \\ (adaptive and \\ efficient) \\ \end{tabular} &
\begin{tabular}{c} Low \\ (maintains accuracy \\ while reducing \\ redundancy) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Tests have shown that the OptD method works effectively in conjunction with filtering methods, such as adaptive TIN models, to enhance data quality further. The OptD method has evolved along with attempts to apply it to various types of data. In [25], observations from a multibeam echosounder were reduced; in paper [31], the optimization concerned data collected from satellite altimetry. The OptD method has found particular application in the detection of wall defects in buildings [32] and has been used for extraction of off-road objects such as traffic signs, power lines, roadside trees, and light poles [33]. The scheme of the OptD method algorithm for one measuring strip (_Li_, where \(i\) is the number of measuring strip) acquired in real time is presented in Figure 1.  
Figure 1: The OptD method for a single measuring strip, illustrating decisions based on predefined thresholds for data reduction. The diagram illustrates the OptD method applied to a single measuring strip, outlining the step-by-step decision-making process based on predefined thresholds for data reduction. 1. **Initial data input**.