Cluster-Based Roof Covering Damage Detection in Ground-Based Lidar Data

Kashani, Alireza G.; Graettinger, Andrew J.

doi:10.1016/j.autcon.2015.07.007

Cited by 51 publications

(18 citation statements)

References 23 publications

(31 reference statements)

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“…Several researchers used LIDAR intensity to detect cracks in concrete structural components in their laboratory tests or post-disaster field investigations [ 42 , 43 , 44 ]. Kashani et al [ 46 , 47 , 48 ] indicated that the LIDAR intensity data is an appropriate means to automatically detect cladding damage of buildings after wind storm events.…”

Section: Applications Of Lidar Intensitymentioning

confidence: 99%

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Kashani

Olsen

Parrish

et al. 2015

Sensors

283

214

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In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.

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Section: Applications Of Lidar Intensitymentioning

confidence: 99%

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Kashani

Olsen

Parrish

et al. 2015

Sensors

283

214

View full text Add to dashboard Cite

show abstract

“…The second classification of point cloud damage detection workflows relies only upon color information and intensity return values. Kashani and Graettinger developed an automatic damage detection method based on the k-means clustering algorithm using intensity return values and color information collected by ground-based lidar (GBL) [17]. Kashani and Graettinger studied various combinations of color and intensity data as damage sensitive features to produce the most accurate results for roof coverings and wall sheathing losses [17].…”

Section: Damage Detection Based On Color and Intensity Informationmentioning

confidence: 99%

“…Kashani and Graettinger developed an automatic damage detection method based on the k-means clustering algorithm using intensity return values and color information collected by ground-based lidar (GBL) [17]. Kashani and Graettinger studied various combinations of color and intensity data as damage sensitive features to produce the most accurate results for roof coverings and wall sheathing losses [17]. This method demonstrated that intensity field values from GBL could result in high damage detection accuracies with an average false detection of only 5% for laboratory conditions.…”

Section: Damage Detection Based On Color and Intensity Informationmentioning

confidence: 99%

Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures

Mohammadi

Wood

Wittich

2019

IJGI

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Assessment and evaluation of damage in civil infrastructure is most often conducted visually, despite its subjectivity and qualitative nature in locating and verifying damaged areas. This study aims to present a new workflow to analyze non-temporal point clouds to objectively identify surface damage, defects, cracks, and other anomalies based solely on geometric surface descriptors that are irrespective of point clouds’ underlying geometry. Non-temporal, in this case, refers to a single dataset, which is not relying on a change detection approach. The developed method utilizes vertex normal, surface variation, and curvature as three distinct surface descriptors to locate the likely damaged areas. Two synthetic datasets with planar and cylindrical geometries with known ground truth damage were created and used to test the developed workflow. In addition, the developed method was further validated on three real-world point cloud datasets using lidar and structure-from-motion techniques, which represented different underlying geometries and exhibited varying severity and mechanisms of damage. The analysis of the synthetic datasets demonstrated the robustness of the proposed damage detection method to classify vertices as surface damage with high recall and precision rates and a low false-positive rate. The real-world datasets illustrated the scalability of the damage detection method and its ability to classify areas as damaged and undamaged at the centimeter level. Moreover, the output classification of the damage detection method automatically bins the damaged vertices into different confidence intervals for further classification of detected likely damaged areas. Moving forward, the presented workflow can be used to bolster structural inspections by reducing subjectivity, enhancing reliability, and improving quantification in surface-evident damage.

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“…Applications based on LiDAR intensity for structures and infrastructures were proposed in the field of damage detection by González et al (2010), Kashani (2014), Kashani & Graettinger (2015), Kashani et al (2015b), and Hou et al (2017). Usually, the proposed approach is based on the use of well-known algorithms (or enhanced versions) for different preliminary tasks, such as classification or feature extraction.…”

Section: Buildings and Infrastructuresmentioning

confidence: 99%

Methods From Information Extraction From Lidar Intensity Data and Multispectral Lidar Technology

Scaioni

Höfle

Kersting

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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LiDAR is a consolidated technology for topographic mapping and 3D reconstruction, which is implemented in several platforms On the other hand, the exploitation of the geometric information has been coupled by the use of laser intensity, which may provide additional data for multiple purposes. This option has been emphasized by the availability of sensors working on different wavelength, thus able to provide additional information for classification of surfaces and objects. Several applications ofmonochromatic and multi-spectral LiDAR data have been already developed in different fields: geosciences, agriculture, forestry, building and cultural heritage. The use of intensity data to extract measures of point cloud quality has been also developed. The paper would like to give an overview on the state-of-the-art of these techniques, and to present the modern technologies for the acquisition of multispectral LiDAR data. In addition, the ISPRS WG III/5 on ‘Information Extraction from LiDAR Intensity Data’ has collected and made available a few open data sets to support scholars to do research on this field. This service is presented and data sets delivered so far as are described.

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Cluster-Based Roof Covering Damage Detection in Ground-Based Lidar Data

Cited by 51 publications

References 23 publications

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures

Methods From Information Extraction From Lidar Intensity Data and Multispectral Lidar Technology

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