Assessing The Positional Accuracy Of Airborne Laser Scanning in urban areas

2013 ◽  
Vol 28 (142) ◽  
pp. 196-210 ◽  
Author(s):  
Joachim Höhle
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Positional Accuracy ◽  
Airborne Laser

Terrestrial and airborne laser scanning and 2-D modelling for 3-D flood hazard maps in urban areas: new opportunities and perspectives

2021 ◽  
Vol 135 ◽  
pp. 104889
Author(s):  
Pierfranco Costabile ◽  
Carmelina Costanzo ◽  
Gianluca De Lorenzo ◽  
Rosa De Santis ◽  
Nadia Penna ◽  
...  
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Flood Hazard ◽  
Airborne Laser Scanning ◽  
Hazard Maps ◽  
Airborne Laser ◽  
Flood Hazard Maps

scholarly journals Mapping Drainage Structures Using Airborne Laser Scanning by Incorporating Road Centerline Information

2021 ◽  
Vol 13 (3) ◽  
pp. 463
Author(s):  
Chi-Kuei Wang ◽  
Nadeem Fareed
Keyword(s):  
Laser Scanning ◽  
Google Earth ◽  
Airborne Laser Scanning ◽  
Rural Site ◽  
Urban Site ◽  
Positional Accuracy ◽  
The Road ◽  
Airborne Laser ◽  
Public Data ◽  
Manual Methods

Wide-area drainage structure (DS) mapping is of great concern, as many DSs are reaching the end of their design life and information on their location is usually absent. Recently, airborne laser scanning (ALS) has been proven useful for DS mapping through manual methods using ALS-derived digital elevation models (DEMs) and hillshade images. However, manual methods are slow and labor-intensive. To overcome these limitations, this paper proposes an automated DS mapping algorithm (DSMA) using classified ALS point clouds and road centerline information. The DSMA begins with removing ALS ground points within the buffer of the road centerlines; the size of the buffer varies according to different road classes. An ALS-modified DEM (ALS-mDEM) is then generated from the remaining ground points. A drainage network (DN) is derived from the ALS-mDEM. Candidate DSs are then obtained by intersecting the DN with the road centerlines. Finally, a refinement buffer of 15 m is placed around each candidate DS to prevent duplicate DS from being generated in close proximity. A total area of 50 km2, including an urban site and a rural site, in Vermont, USA, was used to assess the DSMA. Based on the road functional classification scheme of the Federal Highway Administration (FHWA), the centerline information regarding FHWA roads was obtained from a public data portal. The centerline information on non-FHWA roads, i.e., private roads and streets, was derived from the impervious surface data of a land cover dataset. A benchmark DS dataset was gathered from the transport agency of Vermont and was further augmented using Google Earth Street View images by the authors. The one-to-one correspondence between the benchmark DS and mapped DS for these two sites was then established. The positional accuracy was assessed by computing the Euclidian distance between the benchmark DS and mapped DS. The mean positional accuracy for the urban site and rural site were 13.5 m and 15.8 m, respectively. F1-scores were calculated to assess the prediction accuracy. For FHWA roads, the F1-scores were 0.87 and 0.94 for the urban site and rural site, respectively. For non-FHWA roads, the F1-scores were 0.72 and 0.74 for the urban site and rural site, respectively.

scholarly journals MULTIPLE-PRIMITIVES-BASED HIERARCHICAL CLASSIFICATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREAS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 837-843
Author(s):  
H. Ni ◽  
X. G. Lin ◽  
J. X. Zhang
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Rough Surfaces ◽  
Hierarchical Classification ◽  
Airborne Laser Scanning ◽  
Smooth Surfaces ◽  
Airborne Laser ◽  
Individual Point ◽  
Point Cloud Segmentation ◽  
Third Stage

A hierarchical classification method for Airborne Laser Scanning (ALS) data of urban areas is proposed in this paper. This method is composed of three stages among which three types of primitives are utilized, i.e., smooth surface, rough surface, and individual point. In the first stage, the input ALS data is divided into smooth surfaces and rough surfaces by employing a step-wise point cloud segmentation method. In the second stage, classification based on smooth surfaces and rough surfaces is performed. Points in the smooth surfaces are first classified into ground and buildings based on semantic rules. Next, features of rough surfaces are extracted. Then, points in rough surfaces are classified into vegetation and vehicles based on the derived features and Random Forests (RF). In the third stage, point-based features are extracted for the ground points, and then, an individual point classification procedure is performed to classify the ground points into bare land, artificial ground and greenbelt. Moreover, the shortages of the existing studies are analyzed, and experiments show that the proposed method overcomes these shortages and handles more types of objects.

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

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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