THE UTILIZATION OF SYNTHETIC AND SEMISYNTHETIC POINT CLOUDS AND IMAGES FOR TESTING NOVEL APPROACHES FOR CORRECTING LIDAR DATA
Keywords: LIDAR, Benchmark, Synthetic Data, Semisynthetic Data, Point Clouds
Abstract. The paper presents the application of lidar data and photo datasets, external orientation parameters (EOPs), ground control points (GCPs), and check points for testing new methods of geometric lidar data correction. These datasets are utilized to validate novel approaches such as altimetric deformation methods based on stereo models or lidargrammetric methods that utilize image matching and specialized lidar data formats. The paper presents specific use cases of these data as examples of two tested processes. After describing these processes, the methods of synthetic and semisynthetic data simulation are presented. The simulation is directed and subordinated to the aspects of the new method being tested. The data must be used for testing starting from basic functionality up to specific and untypical cases of new method application. By presenting specific cases of the application of synthetic and semisynthetic data, the paper introduces the general idea of benchmarking based on synthetic and semisynthetic data as another means of validating new methods. These artificially generated datasets provide a controlled environment for evaluating the effectiveness of new methods to be investigated.