Michael Olsen

Dr. Michael Olsen

School of Civil and Construction Engineering, Oregon State University, Corvallis, United States
orcid

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
6 November 2015 in Sensors
Verified
Journal Article
Open access
PDF file
AKAlireza Kashani
Alireza Kashani
Michael OlsenMichael Olsen
Michael Olsen
CPChristopher Parrish
Christopher Parrish
NWNicholas Wilson
Nicholas Wilson
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.

1 May 2010 in Journal of Computing in Civil Engineering
Verified
Journal Article
Closed access
Michael OlsenMichael Olsen
Michael Olsen
FKFalko Kuester
Falko Kuester
BCBarbara J. Chang
Barbara J. Chang
THTara Hutchinson
Tara Hutchinson
Terrestrial laser scanning (TLS) provides a rapid, remote sensing technique to model 3D objects. Previous work applying TLS to structural analysis has demonstrated its effectiveness in capturing simple beam deflections and modeling existing structures. This paper extends TLS to the application of damage detection and volumetric change analysis for a full-scale structural test specimen. Importantly, it provides a framework necessary for such applications, in combination with an analysis approach that does not require tedious development of complex surfaces. Intuitive slicing analysis methods are presented, which can be automated for rapid generation of results. In comparison with conventional photographic and surface analysis methods, the proposed approach proved consistent. Furthermore, the TLS data provided additional insight into geometric change not apparent using conventional methods. As with any digital record, a key benefit to the proposed approach is the resulting virtual test specimen, which is av...

16 February 2019 in Sensors
Verified
Journal Article
Open access
PDF file
ECErzhuo Che
Erzhuo Che
JJJaehoon Jung
Jaehoon Jung
Michael OlsenMichael Olsen
Michael Olsen
Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.

18 September 2013 in Remote Sensing
Verified
Journal Article
Open access
PDF file
KWKeith Williams
Keith Williams
Michael OlsenMichael Olsen
Michael Olsen
GRGene V. Roe
Gene V. Roe
Craig GlennieCraig Glennie
Craig Glennie
A thorough review of available literature was conducted to inform of advancements in mobile LIDAR technology, techniques, and current and emerging applications in transportation. The literature review touches briefly on the basics of LIDAR technology followed by a more in depth description of current mobile LIDAR trends, including system components and software. An overview of existing quality control procedures used to verify the accuracy of the collected data is presented. A collection of case studies provides a clear description of the advantages of mobile LIDAR, including an increase in safety and efficiency. The final sections of the review identify current challenges the industry is facing, the guidelines that currently exist, and what else is needed to streamline the adoption of mobile LIDAR by transportation agencies. Unfortunately, many of these guidelines do not cover the specific challenges and concerns of mobile LIDAR use as many have been developed for airborne LIDAR acquisition and processing. From this review, there is a lot of discussion on “what” is being done in practice, but not a lot on “how” and “how well” it is being done. A willingness to share information going forward will be important for the successful use of mobile LIDAR.

1 September 2012 in Remote Sensing of Environment
Verified
Journal Article
Closed access
BWBrian M. Wing
Brian M. Wing
MRMartin W. Ritchie
Martin W. Ritchie
KBKevin Boston
Kevin Boston
WCWarren B. Cohen
Warren B. Cohen
AGAlix Gitelman
Alix Gitelman
Michael OlsenMichael Olsen
Michael Olsen

30 June 2017 in Bulletin of the New Zealand Society for Earthquake Engineering
Verified
Journal Article
Open access
MCMisko Cubrinovski
Misko Cubrinovski
JBJonathan D. Bray
Jonathan D. Bray
CTChristopher De La Torre
Christopher De La Torre
Michael OlsenMichael Olsen
Michael Olsen
BBBrendon A. Bradley
Brendon A. Bradley
LWLiam Wotherspoon
Liam Wotherspoon
Widespread liquefaction occurred in the end-dumped gravelly fills and hydraulically-placed dredged sandy fill at the CentrePort of Wellington as a result of the 14 November 2016 Mw7.8 Kaikoura earthquake. This liquefaction resulted in substantial global (mass) settlement and lateral movement (spreading) of the fills towards the sea, which adversely affected wharf structures and buildings constructed on shallow and deep foundations. This paper presents key observations from the QuakeCoRE-GEER post-earthquake reconnaissance efforts at the CentrePort Wellington. The different materials and methods used to construct the reclaimed land at CentrePort influenced the patterns of observed liquefaction and its effects. Areas of gravel liquefaction at the port are especially important due to the limited number of these case histories in the literature. Liquefaction-induced ground deformations caused the wharves to displace laterally and damage their piles and offloading equipment. Lateral ground extension and differential settlement damaged buildings, whereas buildings in areas of uniform ground settlement without lateral extension performed significantly better.