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Unmanned aerial vehicle Point cloud Classification Random forestToday, unmanned aerial vehicle (UAV)-based images have become an important data sources for researchers who deals with mapping from various disciplines on photogrammetry and remote sensing. Reconstruction of an area with three-dimensional (3D) point clouds from UAV-based images are an essential process to be used for traditional 2D cadastral maps or to produce a topographic maps. Point clouds should be classified since they subjected to various analyses for extraction for further information from direct point cloud data. Due to the high density of point clouds, data processing and gathering information makes the classification of point clouds a challenging task and may take a long time. Therefore, the classification processing allows an optimal solution to acquire valuable information. In this study, random forest machine learning algorithm for classification processing is applied with radiometric features (Red band, Green band and Blue band) and geometric characteristics derived from covariance feature (curvature, omnivariance, flatness, linearity, surface variance, anisotropy and normalized terrain surface) of points. In addition, the case study is presented in order to test applicability of the proposed methodology to acquire an accuracy and performance of random forest method on the UAV based point cloud. After the classification processing, a class assigned each point from the model was compared with the reference data class. Lastly, the overall accuracy of the classification was achieved as 96% and the Kappa index was reached to 91% on data set.

Inexpensive and small unmanned aerial vehicles (UAVs) provide high-accuracy positional data and enable users to collect high-resolution aerial images. The analysis of images captured using UAVs in a specific reference system is traditionally accomplished using the georeferencing method with high-accuracy ground control points (GCPs). This study aims to test and compare the benchmarks and point clouds’ positional accuracy produced on three consecutive days with different flight combinations at 75 and 100 m flight altitude by means of network-based continuously operating reference stations and differential-based real-time kinematic georeferencing systems without using GCPs. A root mean squared error values were obtained 1–3 cm for the horizontal accuracy and 4–6 cm for the vertical accuracy values. Thus, the proposed system proved an acceptable positional accuracy level. According to the results of the proposed approach, it can be said that the evaluation and use of UAV data without using GCPs is within an adequate range for various mapping purposes.