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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.

This study presents a method for automatic extraction of road lane markings from mobile light detection and ranging (LiDAR) data. Road lanes and traffic signs on the road surface provide safe driving for drivers and aid traffic flow movement along the highway and street. Mobile LiDAR systems acquire massive datasets very quickly in a short time. To simplify the data structure and feature extraction, it is essential for traffic management personnel to apply the right methods. Road lanes must be visible and are a major factor in road safety for drivers. In this study, a methodology is devised and implemented for the extraction of features such as dashed lines, continuous lanes, and direction arrows on the pavement from point clouds. Point cloud data was collected from the Riegl VMX-450 mobile LiDAR system. The alpha shape algorithm is implemented on a point cloud and compared with the widespread use of edge detection techniques applied for intensity-based raster images. The proposed methodology directly extracts three-dimensional and two-dimensional road features to control the quality of road markings and spatial positions with the obtained marking boundaries. State-of-the-art results are obtained and compared with manually digitized reference markings. The standard deviations were evaluated and acquired for intensity image-based and direct point cloud-based extractions, at 1.2 cm and 1.7 cm, respectively.

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The objective of this study was to evaluate and predict land movement by integrating geodetic, geophysical and meteorological data in a landslide area. Specifically, electrical resistivity tomography surveying, Global Navigation Satellite System and terrestrial laser scanning techniques were integrated to monitor a landslide. The study area lies to the southeast of the town of Taşkent in southern Turkey, close to Balcılar in the Central Taurus mountain chain. Landslides result in considerable damage to structures, farmland and the environment in this area; therefore, it is important to characterise the size, extent and timing of past land movements in order to mitigate damage from future landslides. Analysis presented in this paper shows that the greatest land movements in the region occur in spring, when average motions can be up to 1.5 m per month. It is demonstrated that integrated techniques provide a better means for monitoring landslide processes and gathering data for predictions of future movements. Mapping landslide movements by integrating geophysical and geodetic observations can provide a meaningful evaluation of a landslide and its dynamics.

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