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This study evaluates two density-based outlier detection methods, Statistical Outlier Removal (SOR) and Local Outlier Factor (LOF), on large spatial point clouds from photogrammetric surveys and Floating Car Data (FCD). The results indicate that the effectiveness of each method is highly dependent on the characteristics of the dataset, with no universal solution applicable to all scenarios. The research provides insights into optimal parameters for these methods, emphasizing the need for tailored approaches

Author

Ahu Dede

Language

EN