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 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.

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.

scholarly journals MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING

2016 ◽  
Vol XLI-B3 ◽  
pp. 323-330 ◽  
Author(s):  
Leena Matikainen ◽  
Juha Hyyppä ◽  
Paula Litkey
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Sensor Technology ◽  
Classification Error ◽  
Surface Model ◽  
Airborne Laser ◽  
Map Updating

During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

scholarly journals MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING

2016 ◽  
Vol XLI-B3 ◽  
pp. 323-330 ◽  
Author(s):  
Leena Matikainen ◽  
Juha Hyyppä ◽  
Paula Litkey
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Sensor Technology ◽  
Classification Error ◽  
Surface Model ◽  
Airborne Laser ◽  
Map Updating

During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

scholarly journals Machine Learning Algorithms to Predict Tree-Related Microhabitats using Airborne Laser Scanning

2020 ◽  
Vol 12 (13) ◽  
pp. 2142 ◽  
Author(s):  
Giovanni Santopuoli ◽  
Mirko Di Febbraro ◽  
Mauro Maesano ◽  
Marco Balsi ◽  
Marco Marchetti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Machine Learning Algorithms ◽  
Hierarchical Level ◽  
Height Distribution ◽  
Airborne Laser ◽  
Crown Density

In the last few years, the occurrence and abundance of tree-related microhabitats and habitat trees have gained great attention across Europe as indicators of forest biodiversity. Nevertheless, observing microhabitats in the field requires time and well-trained staff. For this reason, new efficient semiautomatic systems for their identification and mapping on a large scale are necessary. This study aims at predicting microhabitats in a mixed and multi-layered Mediterranean forest using Airborne Laser Scanning data through the implementation of a Machine Learning algorithm. The study focuses on the identification of LiDAR metrics useful for detecting microhabitats according to the recent hierarchical classification system for Tree-related Microhabitats, from single microhabitats to the habitat trees. The results demonstrate that Airborne Laser Scanning point clouds support the prediction of microhabitat abundance. Better prediction capabilities were obtained at a higher hierarchical level and for some of the single microhabitats, such as epiphytic bryophytes, root buttress cavities, and branch holes. Metrics concerned with tree height distribution and crown density are the most important predictors of microhabitats in a multi-layered forest.

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

2016 ◽  
Vol III-3 ◽  
pp. 407-414 ◽  
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

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.

scholarly journals RECONSTRUCTION OF BRIDGE SUPERSTRUCTURES FROM AIRBORNE LASER SCANNING POINT CLOUDS

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 121-128
Author(s):  
S. Goebbels ◽  
C. Dalitz
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
German State ◽  
Bridge Superstructures ◽  
3D Objects ◽  
Airborne Laser ◽  
Digital Elevation ◽  
Bridge Type

Abstract. The paper deals with the 3D reconstruction of bridges from Airborne Laser Scanning point clouds and cadastral footprints. The generated realistic 3D objects can be used to enhance city models. While other studies have focused on bridge decks to fill gaps in digital elevation models, this paper focuses on the decomposition of superstructures into construction elements such as pylons, cables and arches. For this purpose, the bridge type is classified, and a combination of model-based and data-based methods is used that are built on the detection of arcs, catenaries, and line segments in the point clouds. The described techniques were successfully applied to create 3D models of the Rhine bridges in the German state of North Rhine-Westphalia.

scholarly journals FOOTPRINT MAP PARTITIONING USING AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol III-3 ◽  
pp. 241-247
Author(s):  
B. Xiong ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Decision Making ◽  
Laser Scanning ◽  
State Of The Art ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
The Other ◽  
Advantages And Disadvantages ◽  
Airborne Laser ◽  
Building Models ◽  
Height Data

Nowadays many cities and countries are creating their 3D building models for a better daily management and smarter decision making. The newly created 3D models are required to be consistent with existing 2D footprint maps. Thereby the 2D maps are usually combined with height data for the task of 3D reconstruction. Many buildings are often composed by parts that are discontinuous over height. Building parts can be reconstructed independently and combined into a complete building. Therefore, most of the state-of-the-art work on 3D building reconstruction first decomposes a footprint map into parts. However, those works usually change the footprint maps for easier partitioning and cannot detect building parts that are fully inside the footprint polygon. In order to solve those problems, we introduce two methodologies, one more dependent on height data, and the other one more dependent on footprints. We also experimentally evaluate the two methodologies and compare their advantages and disadvantages. The experiments use Airborne Laser Scanning (ALS) data and two vector maps, one with 1:10,000 scale and another one with 1:500 scale.

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

2016 ◽  
Vol III-3 ◽  
pp. 407-414
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

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.

scholarly journals FOOTPRINT MAP PARTITIONING USING AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol III-3 ◽  
pp. 241-247 ◽  
Author(s):  
B. Xiong ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Decision Making ◽  
Laser Scanning ◽  
State Of The Art ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
The Other ◽  
Advantages And Disadvantages ◽  
Airborne Laser ◽  
Building Models ◽  
Height Data

Nowadays many cities and countries are creating their 3D building models for a better daily management and smarter decision making. The newly created 3D models are required to be consistent with existing 2D footprint maps. Thereby the 2D maps are usually combined with height data for the task of 3D reconstruction. Many buildings are often composed by parts that are discontinuous over height. Building parts can be reconstructed independently and combined into a complete building. Therefore, most of the state-of-the-art work on 3D building reconstruction first decomposes a footprint map into parts. However, those works usually change the footprint maps for easier partitioning and cannot detect building parts that are fully inside the footprint polygon. In order to solve those problems, we introduce two methodologies, one more dependent on height data, and the other one more dependent on footprints. We also experimentally evaluate the two methodologies and compare their advantages and disadvantages. The experiments use Airborne Laser Scanning (ALS) data and two vector maps, one with 1:10,000 scale and another one with 1:500 scale.

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