Despite the large amounts of resources destined to developing filtering algorithms of LiDAR point clouds in order to obtain a Digital Terrain Model (DTM), the task remains a challenge. As a society advancing towards the democratization of information and collaborative processes, the researchers should not only focus on improving the efficacy of filters, but should also consider the users’ needs with a view toward improving the usability and accessibility of the filters in order to develop tools that will provide solutions to the challenges facing this field of study. In this work, we describe the Hybrid Overlap Filter (HyOF), a new filtering algorithm implemented in the free R software environment. The flow diagram of HyOF differs in the following ways from that of other filters developed to date: (1) the algorithm is formed by a combination of sequentially operating functions (i.e., the output of the first function provides the input of the second), which are capable of functioning independently and thus enabling integration of these functions with other filtering algorithms; (2) the variable penetrability is defined and used, along with slope and elevation, to identify ground points; (3) prior to selection of the seed points, the original point cloud is processed with the aim of removing points corresponding to buildings; and (4) a new method based on a moving window, with longitudinal overlap between windows and transverse overlap between passes, is used to select the seed points. Our hybrid filtering method is tested using 15 reference samples acquired by the International Society of Photogrammetry and Remote Sensing (ISPRS) and is evaluated in comparison with 33 existing filtering algorithms. The results show that our hybrid filtering method produces an average total error of 3.34% and an average Kappa coefficient of 92.62%. The proposed algorithm is one of the most accurate filters that has been tested with the ISPRS reference samples.

No abstract

In cost-benefit analysis of lidar data acquisition, point density is often artificially reduced in order to examine how this affects the quality of derived products. However, the performance of the different density reduction methods has not yet been compared and their influence on the accuracy of the models and results has not been evaluated. A novel method for reducing the point density, termed Proportional per Cell (PpC), is presented and compared with the performance of three other reduction methods, examining their influence on the accuracy of lidar-derived digital surface models using ISPRS reference data. The results indicate that the PpC method was better at conserving the characteristics of the original data. However, point density, sample type and slope had a greater influence than the reduction method used.Hyperlinks to the 3D point clouds (online graph). Land cover information was obtained from Buj an (2019). Surface percentage for bare earth (and low vegetation) in orange, buildings in blue and medium/high vegetation in green for each sample.The Photogrammetric Record

No abstract

In recent years LiDAR (Light Detection And Ranging) technology has experienced a noticeable increase in its relevance and usage in a number of scientific fields. Therefore, software capable of handling LiDAR data becomes a key point in those fields. In this paper, we present GVLiDAR (GPU-based Viewer LiDAR), a novel web framework for visualization and geospatial measurement of LiDAR data point sets. The design of the framework is focused on achieving three key objectives: performance in terms of real-time interaction, functionality and online availability for the LiDAR datasets. All LiDAR files are pre-processed and stored in a lossless data structure which minimizes transfer requirements and offers an on-demand LiDAR data web framework.