Floor Area Ratio extraction based on Airborne Laser Scanning data over urban areas

2010 ◽  
Author(s):  
Chengyi Wang ◽  
Jianglin Ma ◽  
Fuyuan Liang
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Area Ratio ◽  
Airborne Laser Scanning ◽  
Floor Area ◽  
Floor Area Ratio ◽  
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 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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