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•Algorithms often misclassified low vegetation in steppes.•Performance of all algorithms decreased rapidly with slopes over 15°.•SMRF filter implemented in PDAL showed good results in ...forests.•PTIN filter implemented in LAStools provided balanced error rates in all environments.•Some algorithms tended to cause Type I error while others tended to the Type II error.
Ground filtering is an inevitable step of processing the Light detection and ranging-acquired point clouds. Our objective was to evaluate the performance of six filtering algorithms. The point clouds filtering and vertical accuracy were evaluated qualitatively, quantitatively and by comparison with a GNSS survey. All tested algorithms achieved good results but their performance was affected by the terrain slope and vegetation cover. Algorithms performed better in forests than in steppes with a high density of low vegetation. The performance of all algorithms decreased with slopes over 15°. Our results show that some algorithms tended to cause Type I error while others tended more to the Type II error. Furthermore, for some algorithms this tendency depended on the vegetation and terrain character. The Progressive Triangulated Irregular Network algorithm provided overall well-balanced results in all environments. We propose that software developers should provide users with recommendations of optimal parameters for individual environments.
Light detection and ranging (LiDAR) data are essential for scientific discoveries such as Earth and ecological sciences, environmental applications, and responding to natural disasters. While ...collecting LiDAR data over large areas is quite possible the subsequent processing steps typically involve large computational demands. Efficiently storing, managing, and processing LiDAR data are the prerequisite steps for enabling these LiDAR-based applications. However, handling LiDAR data poses grand geoprocessing challenges due to data and computational intensity. To tackle such challenges, we developed a general-purpose scalable framework coupled with a sophisticated data decomposition and parallelization strategy to efficiently handle 'big' LiDAR data collections. The contributions of this research were (1) a tile-based spatial index to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, (2) two spatial decomposition techniques to enable efficient parallelization of different types of LiDAR processing tasks, and (3) by coupling existing LiDAR processing tools with Hadoop, a variety of LiDAR data processing tasks can be conducted in parallel in a highly scalable distributed computing environment using an online geoprocessing application. A proof-of-concept prototype is presented here to demonstrate the feasibility, performance, and scalability of the proposed framework.
The airborne laser scanning has been proven as very efficient method for collecting the surface data. As ALS collects extremely large amount of data, efficient use of this data involves the use of ...some semi-automatic or automatic methods. For this purpose, large number of solutions, different in scope, cost and efficiency are available on the market. One of the available solutions is LAStools software package. Focus of this paper is on the functionalities of the LAStools software package, which are tested and assessed through the experiment. The experiment covered creation of standard topographic products from ALS point cloud using LAStools. At the end of this paper, conclusions about the possibilities of the LAStools software package are made, with a special overview of the advantages and disadvantages, the possibilities and shortcomings.
Lidar has provided significant benefits for forest development and engineering operations and provides a good means to collect information on forest stands.
A common analysis using LiDAR data ...computes the CHM as a difference between DSM and DTM, create a DTM from the ground returns and a DSM from the first returns and subtract the two rasters, but how exactly are generated the DTM and the DSM. Irregular height variations, called data pits are present in the CHM and appear when the first Lidar return is far below the canopy. The purpose of this study is an approach that computes the CHM directly from height-normalized LiDAR points.
Este artigo contempla o desenvolvimento de um método que combina os dados obtidos por sistema de varredura a LASER (Light Amplification by Stimulated Emission of Radiation) aerotransportado e imagens ...aéreas a fim de extrair os contornos de telhados de edificações. Os pontos de contorno das edificações são extraídas dos dados LiDAR e são projetados em duas imagens que formam um modelo estereoscópico. A estas imagens, cujos parâmetros de orientação são conhecidos, são aplicados o algoritmo de detecção de bordas de Canny com o objetivo de identificar as bordas de edificações no espaço imagem. Com os contornos dos telhados de edificações, provenientes dos dados LiDAR projetados nas imagens de bordas é realizado o refinamento das bordas a partir da busca dos pontos de bordas de edificações nas imagens. Com base nos pixels identificados como bordas de edificações e com o propósito de obter os modelos matemáticos que representem os contornos, é aplicado o ajuste de retas 2D pelo Método dos Mínimos Quadrados (MMQ) integrado à filtragem de pontos espúrios por meio do Teste Tau. Para avaliar o método proposto e implementado foram utilizados dados LiDAR com densidade média de 6,7 pontos/m² e imagens aéreas digitais com GSD de 8 cm. Os resultados obtidos na avaliação dos experimentos mostraram que o método proposto conseguiu extrair os contornos dos telhados, com melhores resultados para edificações isoladas que não possuíam projeção de sombras ou objetos sobre elas, atingindo valores da ordem de 0,97 GSD e 1,80 GSD, para o REMQ em planimetria e altimetria, respectivamente.
This paper investigates tree canopy cover mapping of urban barangays (smallest administrative division in the Philippines) in Cebu City using LiDAR (Light Detection and Ranging). Object-Based Image ...Analysis (OBIA) was used to extract tree canopy cover. Multi-resolution segmentation and a series of assign-class algorithm in eCognition software was also performed to extract different land features. Contextual features of tree canopies such as height, area, roundness, slope, length-width and elliptic fit were also evaluated. The results showed that at the time the LiDAR data was collected (June 24, 2014), the tree cover was around 25.11 % (or 15,674,341.8 m2) of the city’s urban barangays (or 62,426,064.6 m2). Among all urban barangays in Cebu City, Barangay Busay had the highest cover (55.79 %) while barangay Suba had the lowest (0.8 %). The 16 barangays with less than 10 % tree cover were generally located in the coastal area, presumably due to accelerated urbanization. Thirty-one barangays have tree cover ranging from 10.59--27.3 %. Only 3 barangays (i.e., Lahug, Talamban, and Busay) have tree cover greater than 30 %. The overall accuracy of the analysis was 96.6 % with the Kappa Index of Agreement or KIA of 0.9. From the study, a grouping can be made of the city’s urban barangays with regards to tree cover. The grouping will be useful to urban planners not only in allocating budget to the tree planting program of the city but also in planning and creation of urban parks and playgrounds.
Measuring the height of a building is very crucial for various factors like understanding residential and commercial building permit, to find out how many buildings are violating the height limit set ...by the government, studying the structural capacity of the area, thus infer something about the population density of the area etc. This paper presents a technique to estimate building height. This is completed utilizing LiDAR data. Nowadays LiDAR remote sensing technology is getting spotlight in the field of research and development. Airborne-LiDAR sensor usually fitted in an aircraft uses laser pulses for scanning the surface of earth. The LiDAR data is processed in software, LAStools and various models are derived like Digital Terrain Model (DTM), Digital Surface Model (DSM), and Canopy Height Model (CHM) and so on. These models are used for determining shape, height and peak location of building. This can be a little contribution to smart city modelling and urban development also.
LiDAR (Light Detection and Ranging) is an emerging technology now and has proved to be one of the best techniques for 3D city modeling. Building detection is an important aspect of 3D city modeling ...as it can help in urban planning, utility services, disaster management, traffic management, environmental monitoring and many other applications. In this paper we propose a method in which building structures can be accurately discriminated from vegetation. Pre-processing of the data is done using a remote sensing tool called Las Tools. The data is structured using kd tree and then further segmented using Euclidean distance clustering. Then we process this data using another open-source remote sensing tool called CloudCompare. This paper will help in imparting a clear idea of how efficiently and accurately building detection can be performed with the help of these open-source tools without deploying much complex algorithms.
Estimation of Peaks and Canopy Height Using LiDAR Data Lavenya, R; Shanmukha, Kinnera; Vaishnavi, Khokalay ...
2018 International Conference on Communication and Signal Processing (ICCSP),
2018-April
Conference Proceeding
3D city modelling plays a major role in today's smart city development. LiDAR facilitates acquisition and processing of point cloud data. The proposed plan in this paper will estimate the height of a ...building. LiDAR data processing is done by the software, LAS tools. In this paper the study focuses on LiDAR scan of urban scenes (buildings). Canopy Height Model (CHM) of a building is obtained which is useful in finding the height of a building, shape of the building and peak location. This can be a contribution to smart city modeling and urban development. Canopy height model for LiDAR data is obtained through classifiers and filters available in LAStools. Also this paper presents about Digital Terrain Model (DTM) and Digital Surface Model (DSM) for LiDAR point cloud data. Further the obtained CHM can be used for the application of finding the number of buildings in an area which are above a threshold height.
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