ROOF RECONSTRUCTION FROM AIRBORNE LASER SCANNING DATA BASED ON IMAGE PROCESSING METHODS

The paper presents a new data-driven approach to generate CityGML building models from airborne laser scanning data. The approach is based on image processing methods applied to an interpolated height map and avoids shortcomings of established methods for plane detection like Hough transform or RANSAC algorithms on point clouds. The improvement originates in an interpolation algorithm that generates a height map from sparse point cloud data by preserving ridge lines and step edges of roofs. Roof planes then are detected by clustering the height map’s gradient angles, parameterizations of planes are estimated and used to filter out noise around ridge lines. On that basis, a raster representation of roof facets is generated. Then roof polygons are determined from region outlines, connected to a roof boundary graph, and simplified. Whereas the method is not limited to churches, the method’s performance is primarily tested for church roofs of the German city of Krefeld because of their complexity. To eliminate inaccuracies of spires, contours of towers are detected additionally, and spires are rendered as solids of revolution. In our experiments, the new data-driven method lead to significantly better building models than the previously applied model-driven approach.

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ROOF RECONSTRUCTION FROM AIRBORNE LASER SCANNING DATA BASED ON IMAGE PROCESSING METHODS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-3-407-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 407-414 ◽

Cited By ~ 6

Author(s):

S. Goebbels ◽

R. Pohle-Fröhlich

Keyword(s):

Image Processing ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Data Driven ◽

Processing Methods ◽

Cloud Data ◽

Applied Model ◽

Airborne Laser ◽

Building Models

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An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

Remote Sensing ◽

10.3390/rs13152938 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2938

Author(s):

Feng Li ◽

Haihong Zhu ◽

Zhenwei Luo ◽

Hang Shen ◽

Lin Li

Keyword(s):

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Superior Performance ◽

Cloth Simulation ◽

Cloud Data ◽

Interpolation Filter ◽

Landscape Characteristics ◽

Surface Interpolation ◽

Airborne Laser

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-797-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 797-804 ◽

Cited By ~ 4

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.

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Segmentation of LiDAR Data Using Multilevel Cube Code

Journal of Sensors ◽

10.1155/2019/4098413 ◽

2019 ◽

Vol 2019 ◽

pp. 1-18 ◽

Cited By ~ 3

Author(s):

So-Young Park ◽

Dae Geon Lee ◽

Eun Jin Yoo ◽

Dong-Cheon Lee

Keyword(s):

Spatial Data ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Lidar Data ◽

Chain Code ◽

Cloud Data ◽

Airborne Laser ◽

Wide Range ◽

Scanning Systems

Light detection and ranging (LiDAR) data collected from airborne laser scanning systems are one of the major sources of spatial data. Airborne laser scanning systems have the capacity for rapid and direct acquisition of accurate 3D coordinates. Use of LiDAR data is increasing in various applications, such as topographic mapping, building and city modeling, biomass measurement, and disaster management. Segmentation is a crucial process in the extraction of meaningful information for applications such as 3D object modeling and surface reconstruction. Most LiDAR processing schemes are based on digital image processing and computer vision algorithms. This paper introduces a shape descriptor method for segmenting LiDAR point clouds using a “multilevel cube code” that is an extension of the 2D chain code to 3D space. The cube operator segments point clouds into roof surface patches, including superstructures, removes unnecessary objects, detects the boundaries of buildings, and determines model key points for building modeling. Both real and simulated LiDAR data were used to verify the proposed approach. The experiments demonstrated the feasibility of the method for segmenting LiDAR data from buildings with a wide range of roof types. The method was found to segment point cloud data effectively.

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QUALITY EVALUATION OF 3D BUILDING MODELS BASED ON LOW-ALTITUDE IMAGERY AND AIRBORNE LASER SCANNING POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2021-345-2021 ◽

2021 ◽

Vol XLIII-B2-2021 ◽

pp. 345-352

Author(s):

G. Gabara ◽

P. Sawicki

Keyword(s):

Urban Areas ◽

Point Cloud ◽

Laser Scanning ◽

Wide Spectrum ◽

Vertical Plane ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Airborne Laser ◽

Building Models ◽

Low Altitude

Abstract. The term “3D building models” is used in relation to the CityGML models and building information modelling. Reconstruction and modelling of 3D building objects in urban areas becomes a common trend and finds a wide spectrum of utilitarian applications. The paper presents the quality assessment of two multifaceted 3D building models, which were obtained from two open-access databases: Polish national Geoportal (accuracy in LOD 2 standard) and Trimble SketchUp Warehouse (accuracy in LOD 2 standard with information about architectural details of façades). The Geoportal 3D models were primary created based on the airborne laser scanning data (density 12 pts/sq. m, elevation accuracy to 0.10 m) collected during Informatic System for Country Protection against extraordinary hazards project. The testing was performed using different validation low-altitude photogrammetric datasets: RIEGL LMS-Q680i airborne laser scanning point cloud (min. density 25 pts/sq. m and height accuracy 0.03 m), and image-based Phase One iXU-RS 1000 point cloud (average accuracy in the horizontal and in the vertical plane is respectively to 0.015 m and 0.030 m). The visual comparison, heat maps with the function of the signed distance, and histograms in predefined ranges were used to evaluate the quality and accuracy of 3D building models. The aspect of error sources that occurred during the modelling process was also discussed.

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Airborne Laser Scanning and Image Processing Techniques for Archaeological Prospection

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-5-231-2014 ◽

2014 ◽

Vol XL-5 ◽

pp. 231-235 ◽

Cited By ~ 1

Author(s):

M. Faltýnová ◽

P. Nový

Keyword(s):

Image Processing ◽

Laser Scanning ◽

Average Density ◽

Airborne Laser Scanning ◽

Aerial Photographs ◽

Processing Methods ◽

Archaeological Prospection ◽

Airborne Laser ◽

Visualization Techniques ◽

Shaded Relief

Aerial photography was, for decades, an invaluable tool for archaeological prospection, in spite of the limitation of this method to deforested areas. The airborne laser scanning (ALS) method can be nowadays used to map complex areas and suitable complement earlier findings. This article describes visualization and image processing methods that can be applied on digital terrain models (DTMs) to highlight objects hidden in the landscape. Thanks to the analysis of visualized DTM it is possible to understand the landscape evolution including the differentiation between natural processes and human interventions. Different visualization methods were applied on a case study area. A system of parallel tracks hidden in a forest and its surroundings – part of old route called "Devil's Furrow" near the town of Sázava was chosen. The whole area around well known part of Devil's Furrow has not been prospected systematically yet. The data from the airborne laser scanning acquired by the Czech Office for Surveying, Mapping and Cadastre was used. The average density of the point cloud was approximately 1 point/m2 The goal of the project was to visualize the utmost smallest terrain discontinuities, e.g. tracks and erosion furrows, which some were not wholly preserved. Generally we were interested in objects that are clearly not visible in DTMs displayed in the form of shaded relief. Some of the typical visualization methods were tested (shaded relief, aspect and slope image). To get better results we applied image-processing methods that were successfully used on aerial photographs or hyperspectral images in the past. The usage of different visualization techniques on one site allowed us to verify the natural character of the southern part of Devil’s Furrow and find formations up to now hidden in the forests.

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A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w8-131-2019 ◽

2019 ◽

Vol IV-4/W8 ◽

pp. 131-137

Author(s):

A. V. Vo ◽

D. F. Laefer

Keyword(s):

Point Cloud ◽

Input Data ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Data Set ◽

Cloud Data ◽

Airborne Laser ◽

Alternative Approach ◽

Hadoop Cluster

Abstract. Because of the importance of access to sunlight, shadow analysis is a common consideration in urban design, especially for dense urban developments. As shadow computation is computationally expensive, most urban shadow analysis tools have to date circumvented the high computational costs by representing urban complexity only through simplified geometric models. The simplification process removes details and adversely affects the level of realism of the ultimate results. In this paper, an alternative approach is presented by utilizing the highest level of detail and resolution captured in the geometric input data source, which is an extremely high-resolution airborne laser scanning point cloud (300 points/m2). To cope with the high computational demand caused by the use of this dense and detailed input data set, the Comprehensive Urban Shadow algorithm is introduced to distribute the computation for parallel processing on a Hadoop cluster. The proposed comprehensive urban shadow analysis solution is scalable, reasonably fast, and capable of preserving the original resolution and geometric detail of the original point cloud data.

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

Remote Sensing ◽

10.3390/rs13020261 ◽

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.

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