  Citation: Mehta, S.; Peach, J.; Weinert, A. To Expedite Roadway Identification and Damage Assessment in LiDAR 3D Imagery for Disaster Relief Public Assistance. Infrastructures 2022, 7, 39. https:// doi.org/10.3390/infrastructures 7030039 Academic Editors: Víctor Yepes, Ignacio J. Navarro Martínez and Antonio J. Sánchez-Garrido Received: 7 January 2022 Accepted: 20 February 2022 Published: 11 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). infrastructures Technical Note To Expedite Roadway Identification and Damage Assessment in LiDAR 3D Imagery for Disaster Relief Public Assistance Sharad Mehta *, John Peach and Andrew Weinert Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA; john.x.peach@gmail.com (J.P.); andrew.weinert@ll.mit.edu (A.W.) * Correspondence: sharad.mehta@ll.mit.edu 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 measure- ments 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. Keywords: lidar; airborne imagery; road marking; point cloud; road detection; disaster response 1. 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 recov- ery. 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. In particular, LiDAR is a sensing modality that uses photon light reflections to produce three-dimensional point clouds. 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 Photo-diode (Gm-APD) LiDAR to comprehensively map Puerto Rico to support the post- Hurricane Maria recovery efforts in summer 2018 and targeted collections of North and South Carolina to support the Hurricane Florence response efforts in fall 2018. In conjunc- tion with ground-based local field surveys, satellite imagery, and open-source datasets, a highly automated workflow was developed to expedite a post-disaster damage assessment. This paper provides an overview of the development and application of an algorithm to assist in processing LiDAR data to enable remote roadway assessments. Infrastructures 2022, 7, 39. https://doi.org/10.3390/infrastructures7030039 https://www.mdpi.com/journal/infrastructures