scholarly journals STATISTICAL OUTLIER DETECTION METHOD FOR AIRBORNE LIDAR DATA

2018 ◽  
Vol XLII-1 ◽  
pp. 87-92 ◽  
Author(s):  
A. C. Carrilho ◽  
M. Galo ◽  
R. C. Santos
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser System ◽  
Airborne Lidar ◽  
External Information ◽  
Airborne Laser ◽  
Software Packages ◽  
Complete Automation ◽  
Elevation Difference ◽  
Airborne Lidar Data

<p><strong>Abstract.</strong> Sampling the Earth’s surface using airborne LASER scanning (ALS) systems suffers from several factors inherent to the LASER system itself as well as external factors, such as the presence of particles in the atmosphere, and/or multi-path returns due to reflections. The resulting point cloud may therefore contain some outliers and removing them is an important (and difficult) step for all subsequent processes that use this kind of data as input. In the literature, there are several approaches for outlier removal, some of which require external information, such as spatial frequency characteristics or presume parametric mathematical models for surface fitting. A limitation on the height histogram filtering approach was identified from the literature review: outliers that lie within the ground elevation difference might not be detected. To overcome such a limitation, this paper proposes an adaptive alternative based on point cloud cell subdivision. Instead of computing a single histogram for the whole dataset, the method applies the filtering to smaller patches, in which the ground elevation difference can be ignored. A study area was filtered, and the results were compared quantitatively with two other methods implemented in both free and commercial software packages. The reference data was generated manually in order to provide useful quality measures. The experiment shows that none of the tested filters was able to reach a level of complete automation, therefore manual corrections by the operator are still necessary.</p>

scholarly journals Detection of Landscape patterns in airborne LIDAR data in the Nature Reserve of Castelporziano (Rome)

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
F. Recanatesi ◽  
M. Tolli ◽  
M.N. Ripa ◽  
R. Pelorosso ◽  
F. Gobattoni ◽  
...  
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Nature Reserve ◽  
Airborne Lidar ◽  
Landscape Patterns ◽  
Actual State ◽  
Airborne Laser ◽  
Natural Reserve ◽  
Scanning Lidar ◽  
Airborne Lidar Data

During the last decades the protection of nature has become a very important aspect due to the anthropogenic interference and structural alteration of the environment, especially in the peri-urban areas. Therefore, for a sustainable forest planning it is fundamental to get information about the actual state and the variation in landscape patterns concerning the forest layers. To this aim, an airborne laser scanning (LIDAR), a type of sensor which explicitly measures canopy height, was used to detect landscape patterns of broadleaf oak forest and for detecting the monumental trees in a peri-urban Natural Reserve: the Presidential Estate of Castelporziano (Rome).

scholarly journals Suitability of ground-based SfM–MVS for monitoring glacial and periglacial processes

2016 ◽  
Vol 4 (2) ◽  
pp. 425-443 ◽  
Author(s):  
Livia Piermattei ◽  
Luca Carturan ◽  
Fabrizio de Blasi ◽  
Paolo Tarolli ◽  
Giancarlo Dalla Fontana ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Surface Displacement ◽  
Airborne Lidar ◽  
Rock Glacier ◽  
Low Contrast ◽  
Factors Affecting ◽  
Digital Elevation ◽  
Elevation Difference ◽  
Geodetic Mass Balance ◽  
Airborne Lidar Data

Abstract. Photo-based surface reconstruction is rapidly emerging as an alternative survey technique to lidar (light detection and ranging) in many fields of geoscience fostered by the recent development of computer vision algorithms such as structure from motion (SfM) and dense image matching such as multi-view stereo (MVS). The objectives of this work are to test the suitability of the ground-based SfM–MVS approach for calculating the geodetic mass balance of a 2.1 km2 glacier and for detecting the surface displacement of a neighbouring active rock glacier located in the eastern Italian Alps. The photos were acquired in 2013 and 2014 using a digital consumer-grade camera during single-day field surveys. Airborne laser scanning (ALS, otherwise known as airborne lidar) data were used as benchmarks to estimate the accuracy of the photogrammetric digital elevation models (DEMs) and the reliability of the method. The SfM–MVS approach enabled the reconstruction of high-quality DEMs, which provided estimates of glacial and periglacial processes similar to those achievable using ALS. In stable bedrock areas outside the glacier, the mean and the standard deviation of the elevation difference between the SfM–MVS DEM and the ALS DEM was −0.42 ± 1.72 and 0.03 ± 0.74 m in 2013 and 2014, respectively. The overall pattern of elevation loss and gain on the glacier were similar with both methods, ranging between −5.53 and + 3.48 m. In the rock glacier area, the elevation difference between the SfM–MVS DEM and the ALS DEM was 0.02 ± 0.17 m. The SfM–MVS was able to reproduce the patterns and the magnitudes of displacement of the rock glacier observed by the ALS, ranging between 0.00 and 0.48 m per year. The use of natural targets as ground control points, the occurrence of shadowed and low-contrast areas, and in particular the suboptimal camera network geometry imposed by the morphology of the study area were the main factors affecting the accuracy of photogrammetric DEMs negatively. Technical improvements such as using an aerial platform and/or placing artificial targets could significantly improve the results but run the risk of being more demanding in terms of costs and logistics.

scholarly journals AN ADAPTED CONNECTED COMPONENT LABELING FOR CLUSTERING NON-PLANAR OBJECTS FROM AIRBORNE LIDAR POINT CLOUD

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 191-195 ◽  
Author(s):  
B. Aissou ◽  
A. Belhadj Aissa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Lidar ◽  
Support Vector ◽  
Connected Component ◽  
Planar Surfaces ◽  
Airborne Laser ◽  
Connected Component Labeling ◽  
Sensing Technology

Abstract. Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. A segmentation of Airborne Laser Scanning (ALS) point cloud is very important task that still interest many scientists. In this paper, the Connected Component Analysis (CCA), or Connected Component Labeling is proposed for clustering non-planar objects from Airborne Laser Scanning (ALS) LiDAR point cloud. From raw point cloud, sub-surface segmentation method is applied as preliminary filter to remove planar surfaces. Starting from unassigned points , CCA is applied on 3D data considering only neighboring distance as initial parameter. To evaluate the clustering, an interactive labeling of the resulting components is performed. Then, components are classified using Support Vector Machine, Random Forest and Decision Tree. The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany. The visualization of the results shown the potential of the proposed method to identify dormers, chimneys and ground class.

scholarly journals Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

2021 ◽  
Vol 13 (2) ◽  
pp. 261
Author(s):  
Francisco Mauro ◽  
Andrew T. Hudak ◽  
Patrick A. Fekety ◽  
Bryce Frank ◽  
Hailemariam Temesgen ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Field Measurements ◽  
Point Clouds ◽  
Semiparametric Models ◽  
Airborne Laser Scanning ◽  
Parametric Models ◽  
Fuel Load ◽  
Forest Fuels ◽  
Airborne Laser

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

Urban Thematic Mapping by Integrating LiDAR Point Cloud with Colour Imagery

GEOMATICA ◽  
2011 ◽  
Vol 65 (4) ◽  
pp. 375-385 ◽  
Author(s):  
Haiyan Guan ◽  
Jonathan Li ◽  
Michael A. Chapman
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Local Variation ◽  
Airborne Lidar ◽  
Aerial Imagery ◽  
Building Detection ◽  
Thematic Mapping ◽  
Airborne Lidar Data ◽  
Spectral Channels ◽  
Height Data

This paper presents an effective approach to integrating airborne lidar data and colour imagery acquired simultaneously for urban mapping. Texture and height information extracted from lidar point cloud is integrated with spectral channels of aerial imagery into an image segmentation process. Then, the segmented polygons are integrated with the extracted geometric features (height information between first- and lastreturn, eigenvalue-based local variation and filtered height data) and spectral features (line segments) into a supervised classifier. The results for two different urban areas in Toronto, Canada, demonstrated that a satisfactory overall accuracy of 84.96% and Kappa of 0.76 were achieved in Scene I, while a building detection rate of 92.11%, comission error of 2.10% and omission error of 9.25% were obtained in Scene II.

scholarly journals On the potential of very high-resolution repeat DEMs in glacial and periglacial environments

2010 ◽  
Vol 4 (1) ◽  
pp. 53-65 ◽  
Author(s):  
J. Abermann ◽  
A. Fischer ◽  
A. Lambrecht ◽  
T. Geist
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Airborne Lidar ◽  
Primary Data ◽  
Glacier Area ◽  
Airborne Laser ◽  
Multi Temporal ◽  
Rock Glaciers ◽  
Data Source ◽  
Very High

Abstract. The potential of high-resolution repeat DEMs was investigated for glaciological applications including periglacial features (e.g. rock glaciers). It was shown that glacier boundaries can be delineated using airborne LIDAR-DEMs as a primary data source and that information on debris cover extent could be extracted using multi-temporal DEMs. Problems and limitations are discussed, and accuracies quantified. Absolute deviations of airborne laser scanning (ALS) derived glacier boundaries from ground-truthed ones were below 4 m for 80% of the ground-truthed values. Overall, we estimated an accuracy of +/−1.5% of the glacier area for glaciers larger than 1 km2. The errors in the case of smaller glaciers did not exceed +/−5% of the glacier area. The use of repeat DEMs in order to obtain information on the extent, characteristics and activity of rock glaciers was investigated and discussed based on examples.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals 3D MODELS OF OBJECTS IN PROCESS OF RECONSTRUCTION

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Jovana Radović
Keyword(s):  
Laser Scanning ◽  
Laser System ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
3D Models ◽  
Terrestrial Laser Scanner ◽  
Accurate Data ◽  
Final Model ◽  
Airborne Laser

Within the last years terrestrial and airborne laser scanning has become a powerful technique for fast and efficient three-dimensional data acquisition of different kinds of objects. Airborne laser system (LiDAR) collects accurate georeferenced data of extremely large areas very quickly while the terrestrial laser scanner produces dense and geometrically accurate data. The combination of these two segments of laser scanning provides different areas of application. One of the applications is in the process of reconstruction of objects. Objects recorded with laser scanning technology and transferred into the final model represent the basis for building an object as it was original. In this paper, there will be shown two case studies based on usage of airborne and terrestrial laser scanning and processing of the data collected by them.

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