# Real-Time DTM Generation with Sequential Estimation and OptD Method
## 4 Conclusions  
This study presents a concept of developing a DTM based on the sequential estimation and the OptD method. The proposed methodology allows the DTM to be saved in a characteristics file (a text file), which occupies less computer memory and is easier to store. In contrast, existing methods save DTMs in model files (binary files). The primary difference between these two approaches is that loading a binary file allows immediate use as part of a program, whereas loading a characteristics file requires \"deciphering\" the file and constructing the model first. While both approaches ultimately produce the same result, the binary file is more convenient as it is already built and ready to use. On the other hand, the characteristics file contains all the essential information about the model and allows to determine the height of any point on the object when processed. The application of the OptD method during processing resulted in dataset reduction, which influenced both the efficiency and effectiveness of processing observations using sequential estimation. The model generated with sequential estimation method has variable DTM accuracy. This means that in each fragment of the model, the value of \(m_{0}\) is different, which proves the variability of the accuracy characteristics of the DTM. Thus, the accuracy of the model is not given by one accuracy value as in the case of the existing methods. The proposed concept of generating DTMs with prior reduction using the OptD method can be applied during data acquisition when we aim to obtain a model either immediately during measurement or shortly after; for instance, measurements conducted with a multibeam echosounder for comparative navigation purposes. While acquiring data about the bottom of a reservoir, it is possible to generate a bottom model and identify areas requiring more detailed measurements or where obstacles are located. Similarly, during measurements performed with a drone equipped with a LiDAR sensor, we can simultaneously acquire a point cloud and generate a DTM or digital elevation model (DEM). In this article, only the equation of the plane was used to build the DTM of the studied area. Interesting further research topics include the following:  
* the use of a polynomial with higher degrees to create a DTM in more complex terrain configurations;
* the use of a sequential combination of different degrees of polynomials to approximate the measured object. The conclusion states that the method is novel but lacks specific improvements over existing methods. The authors are invited to clearly state how the proposed method outperforms conventional DTM generation techniques. Based on the results, the main advantages of the proposed method for developing DTMs using sequential estimation and the OptD method can be formulated as follows:  
1. Reduced storage space. Saving the DTM in a characteristics file (a text file) requires less computer memory compared to binary files, making data management easier;
2. Analytical flexibility. Characteristics files contain essential model information, allowing for height determination at any point after processing;
3. Increased processing efficiency. The OptD method reduces datasets, improving the efficiency and effectiveness of observations processed via sequential estimation;4. Variable DTM accuracy. The model shows varying accuracy across different fragments, allowing for tailored precision based on terrain conditions, unlike traditional methods;
5. Potential for further development. The methodology supports future research, including the use of higher-degree polynomials for complex terrain modeling;
6. Application in data acquisition systems. The method is suitable for implementation in systems that facilitate mass data collection, enhancing its practical utility in various fields. These and other scientific problems arising from detailed research will be considered in the authors' subsequent articles.