To Expedite Roadway Identification and Damage Assessment in LiDAR 3D Imagery for Disaster Relief Public Assistance

Sharad Mehta,John Peach,Andrew Weinert

doi:10.3390/infrastructures7030039

Sharad Mehta, John Peach + Show 1 more

Open Access

https://doi.org/10.3390/infrastructures7030039

Copy DOI

Abstract

Aerial surveys using LiDAR systems can play a vital role in the quantitative assessment of infrastructure damage caused by hurricanes, floods, and other natural disasters. GmAPD LiDAR provides high-resolution 3D point-cloud data which enables the surveyor to take accurate measurements of damages to roads, buildings, communication towers, power lines, etc. Due to the high point cloud density, a very large volume of data is generated during an aerial survey. The data collected during the airborne imaging is post-processed with calibration, geo-registration, and segmentation. Albeit very accurate, extracting useful information from this data is a slow and laborious process. For disaster response, methods of automating this process have spurred the development of simple, fast algorithms that can be used to recognize physical structures from the point-cloud data that can later be assessed for structural damage. In this paper, we describe an efficient algorithm to extract roadways from a massive Lidar data-set to assist the Federal Emergency Management Agency (FEMA) in assessing road conditions as a step toward helping surveyors expedite a quantitative assessment of road damages for providing and distributing public assistance for disaster relief.

Highlights

With the increasing frequency and cost associated with disasters such as tornadoes, flooding, and hurricanes, there is a critical need to develop capabilities that are optimized to support the processing, exploitation, and dissemination (PED) needs of an incident or disaster response [1]
The use of light detection and ranging (LiDAR) imagery has fundamentally changed the methods and approaches used by field surveyors and damage assessors
The site assessor can focus on taking accurate measurements of a few strategically selected features of physical structures at or near the site

Summary

Introduction

With the increasing frequency and cost associated with disasters such as tornadoes, flooding, and hurricanes, there is a critical need to develop capabilities that are optimized to support the processing, exploitation, and dissemination (PED) needs of an incident or disaster response [1]. Capability development is needed to support civilians and public safety before the disaster, during the immediate response, and over the long-term recovery. Remote sensing technologies, such as traditional two-dimensional optical imagery collected by the Civil Air Patrol (CAP) or three-dimensional light detection and ranging (LiDAR) point clouds are enabling technologies to develop the applications that public safety needs. Due to recent advances in sensing techniques and commercial technology transition, LiDAR is being more integrated into incident and disaster response [2]. Examples of this integration are the deployment of an airborne Geiger-mode Avalanche

Methods

Results

Discussion

Conclusion