Mr. Colin Axel Profile

Colin Axel

Graduate Research Assistant

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

SPIE Journal Paper | 18 December 2017 Open Access

Building damage assessment using airborne lidar

Colin Axel, Jan A. van Aardt

JARS, Vol. 11, Issue 04, 046024, (December 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.046024

KEYWORDS: LIDAR, Clouds, Image segmentation, Vegetation, Earthquakes, Surface properties, Image classification, Visualization, Remote sensing, Natural disasters

Read Abstract +

The assessment of building damage following a natural disaster is a crucial step in determining the impact of the event itself and gauging reconstruction needs. Automatic methods for deriving damage maps from remotely sensed data are preferred, since they are regarded as being rapid and objective. We propose an algorithm for performing unsupervised building segmentation and damage assessment using airborne light detection and ranging (lidar) data. Local surface properties, including normal vectors and curvature, were used along with region growing to segment individual buildings in lidar point clouds. Damaged building candidates were identified based on rooftop inclination angle, and then damage was assessed using planarity and point height metrics. Validation of the building segmentation and damage assessment techniques were performed using airborne lidar data collected after the Haiti earthquake of 2010. Building segmentation and damage assessment accuracies of 93.8% and 78.9%, respectively, were obtained using lidar point clouds and expert damage assessments of 1953 buildings in heavily damaged regions. We believe this research presents an indication of the utility of airborne lidar remote sensing for increasing the efficiency and speed at which emergency response operations are performed.

DOWNLOAD PAPER

Proceedings Article | 13 May 2016 Paper

Remote sensing-based detection and quantification of roadway debris following natural disasters

Colin Axel, Jan A. van Aardt, Felipe Aros-Vera, José Holguín-Veras

Proceedings Volume 9832, 98320C (2016) https://doi.org/10.1117/12.2223073

KEYWORDS: LIDAR, Roads, Natural disasters, Buildings, Clouds, Image filtering, RGB color model, Earthquakes, Neptunium, 3D modeling

Read Abstract +

Rapid knowledge of road network conditions is vital to formulate an efficient emergency response plan following any major disaster. Fallen buildings, immobile vehicles, and other forms of debris often render roads impassable to responders. The status of roadways is generally determined through time and resource heavy methods, such as field surveys and manual interpretation of remotely sensed imagery. Airborne lidar systems provide an alternative, cost-e↵ective option for performing network assessments. The 3D data can be collected quickly over a wide area and provide valuable insight about the geometry and structure of the scene. This paper presents a method for automatically detecting and characterizing debris in roadways using airborne lidar data. Points falling within the road extent are extracted from the point cloud and clustered into individual objects using region growing. Objects are classified as debris or non-debris using surface properties and contextual cues. Debris piles are reconstructed as surfaces using alpha shapes, from which an estimate of debris volume can be computed. Results using real lidar data collected after a natural disaster are presented. Initial results indicate that accurate debris maps can be automatically generated using the proposed method. These debris maps would be an invaluable asset to disaster management and emergency response teams attempting to reach survivors despite a crippled transportation network.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years