scholarly journals A new expert system module for building detection in urban areas using spectral information and LIDAR data

2009 ◽  
Vol 1 (4) ◽  
pp. 97-110 ◽  
Author(s):  
Ayman Rashad Elshehaby ◽  
Lamyaa Gamal El-deen Taha
Keyword(s):  
Expert System ◽  
Urban Areas ◽  
Spectral Information ◽  
Lidar Data ◽  
Building Detection ◽  
System Module

scholarly journals A Hybrid GWR-Based Height Estimation Method for Building Detection in Urban Environments

2014 ◽  
Vol II-2 ◽  
pp. 23-29 ◽  
Author(s):  
X. Wei ◽  
X. Yao
Keyword(s):  
Urban Areas ◽  
Estimation Method ◽  
Urban Environments ◽  
Lidar Data ◽  
Building Detection ◽  
Building Roof ◽  
Aerial Photos ◽  
The Road ◽  
Urban Modelling ◽  
Gwr Model

LiDAR has become important data sources in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated methods. The aerial photos, on the other hand, provide continuous spectral information of buildings. But the segmentation of the aerial photos cannot distinguish between the road surfaces and the building roof. This paper develops a geographically weighted regression (GWR)-based method to identify buildings. The method integrates characteristics derived from the sparse LiDAR data and from aerial photos. In the GWR model, LiDAR data provide the height information of spatial objects which is the dependent variable, while the brightness values from multiple bands of the aerial photo serve as the independent variables. The proposed method can thus estimate the height at each pixel from values of its surrounding pixels with consideration of the distances between the pixels and similarities between their brightness values. Clusters of contiguous pixels with higher estimated height values distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed hybrid method is better than those by image classification of aerial photos along or by height extraction of LiDAR data alone. We argue that this simple and effective method can be very useful for automatic detection of buildings in urban areas.

scholarly journals LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1285 ◽  
Author(s):  
Silvia Liberata Ullo ◽  
Chiara Zarro ◽  
Konrad Wojtowicz ◽  
Giuseppe Meoli ◽  
Mariano Focareta
Keyword(s):  
Environmental Monitoring ◽  
Case Studies ◽  
Urban Areas ◽  
Lidar Data ◽  
Building Detection ◽  
The Earth ◽  
Areas Of Interest ◽  
2D And 3D ◽  
3D Lidar ◽  
Very High

The aim of this paper is to highlight how the employment of Light Detection and Ranging (LiDAR) technique can enhance greatly the performance and reliability of many monitoring systems applied to the Earth Observation (EO) and Environmental Monitoring. A short presentation of LiDAR systems, underlying their peculiarities, is first given. References to some review papers are highlighted, as they can be regarded as useful guidelines for researchers interested in using LiDARs. Two case studies are then presented and discussed, based on the use of 2D and 3D LiDAR data. Some considerations are done on the performance achieved through the use of LiDAR data combined with data from other sources. The case studies show how the LiDAR-based systems, combined with optical Very High Resolution (VHR) data, succeed in improving the analysis and monitoring of specific areas of interest, specifically how LiDAR data help in exploring external environment and extracting building features from urban areas. Moreover the discussed Case Studies demonstrate that the use of the LiDAR data, even with a low density of points, allows the development of an automatic procedure for accurate building features extraction, through object-oriented classification techniques, therefore by underlying the importance that even simple LiDAR-based systems play in EO and Environmental Monitoring.

3D Model Construction in an Urban Environment from Sparse LiDAR Points and Aerial Photos—a Statistical Approach

GEOMATICA ◽  
2015 ◽  
Vol 69 (3) ◽  
pp. 271-284
Author(s):  
Xuebin Wei ◽  
Xiaobai Yao
Keyword(s):  
Urban Areas ◽  
Statistical Approach ◽  
Spectral Characteristics ◽  
Lidar Data ◽  
Building Detection ◽  
Local Regression ◽  
High Resolution Data ◽  
Aerial Photos ◽  
Gwr Model ◽  
Resolution Data

Light Detection and Ranging (LiDAR) has become an important data source in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated algorithms. The aerial photos, on the other hand, provide continuous spectral information on buildings. However, the accuracy of classified building boundaries from aerial photos is constrained when building roofs and their surroundings share analogous spectral characteristics. This paper develops a statistical approach that can integrate characteristic variables derived from sparse LiDAR points and air photos to detect buildings by estimating object heights and identifying clusters of similar heights. Within this study, the approach chooses a local regression method, namely geographically-weighted regression (GWR), to account for local variations of building surface height. In the GWR model, LiDAR data provide the height information of spatial objects, which is the dependent variable, while the brightness values from visible bands of the aerial photo serve as the independent variables. The established GWR model estimates the height at each pixel based on height values of its surrounding pixels with consideration of the distances between the pixels as well as similarities between their brightness values in visible bands. Clusters of contiguous pixels with higher estimated height val ues distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed statistical method is better than those by image classification of aerial photos alone or by building extraction of LiDAR data alone. The results demonstrate that this simple and effective method can be very useful for automatic detection of buildings in urban areas. The approach can be most helpful for studies of urban areas where more suitable but expensive high resolution data are not available.

scholarly journals 3D RECONSTRUCTION OF BUILDINGS WITH GABLED AND HIPPED STRUCTURES USING LIDAR DATA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 47-52
Author(s):  
H. Amini ◽  
P. Pahlavani ◽  
R. Karimi
Keyword(s):  
Urban Areas ◽  
3D Model ◽  
Remote Sensing Data ◽  
Training Data ◽  
Aerial Images ◽  
Lidar Data ◽  
Building Detection ◽  
3D Model Reconstruction ◽  
Accuracy And Precision ◽  
Intersection Line

Buildings are the most important objects in urban areas. Thus, building detection using photogrammetry and remote sensing data as well as 3D model of buildings are very useful for many applications such as mobile navigation, tourism, and disaster management. In this paper, an approach has been proposed for detecting buildings by LiDAR data and aerial images, as well as reconstructing 3D model of buildings. In this regard, firstly, building detection carried out by utilizing a Supper Vector Machine (SVM) as a supervise method. The supervise methods need training data that could be collected from some features. Hence, LiDAR data and aerial images were utilized to produce some features. The features were selected by considering their abilities for separating buildings from other objects. The evaluation results of building detection showed high accuracy and precision of the utilized approach. The detected buildings were labeled in order to reconstruct buildings, individually. Then the planes of each building were separated and adjacent planes were recognized to reduce the calculation volume and to increase the accuracy. Subsequently, the bottom planes of each building were detected in order to compute the corners of hipped roofs using intersection of three adjacent planes. Also, the corners of gabled roofs were computed by both calculating the intersection line of the adjacent planes and finding the intersection between the planes intersection line and their detected parcel. Finally, the coordinates of some nodes in building floor were computed and 3D model reconstruction was carried out. In order to evaluate the proposed method, 3D model of some buildings with different complexity level were generated. The evaluation results showed that the proposed method has reached credible performance.

An expert system for real-time well field management

2012 ◽  
Vol 12 (5) ◽  
pp. 699-706 ◽  
Author(s):  
B. S. Marti ◽  
G. Bauser ◽  
F. Stauffer ◽  
U. Kuhlmann ◽  
H.-P. Kaiser ◽  
...  
Keyword(s):  
Drinking Water ◽  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
Urban Areas ◽  
Industrial Sites ◽  
Water Wells ◽  
Well Field ◽  
Field Management ◽  
Historical Management

Well field management in urban areas faces challenges such as pollution from old waste deposits and former industrial sites, pollution from chemical accidents along transport lines or in industry, or diffuse pollution from leaking sewers. One possibility to protect the drinking water of a well field is the maintenance of a hydraulic barrier between the potentially polluted and the clean water. An example is the Hardhof well field in Zurich, Switzerland. This paper presents the methodology for a simple and fast expert system (ES), applies it to the Hardhof well field, and compares its performance to the historical management method of the Hardhof well field. Although the ES is quite simplistic it considerably improves the water quality in the drinking water wells. The ES knowledge base is crucial for successful management application. Therefore, a periodic update of the knowledge base is suggested for the real-time application of the ES.

scholarly journals Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

2017 ◽  
Vol 9 (8) ◽  
pp. 771 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Dunyong Zheng ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data

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.

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