31 Mar 2022

Continued Investigation of Thermal and Lidar Surveys of Building Infrastructure

ABSTRACT: We conducted a combined lidar and thermal infrared survey from both ground-based and Unmanned Aerial System (UAS) platforms at McMurdo Station, Antarctica, in February 2020 to assess the building thermal envelope and infrastructure of the Crary Lab and the wet utility corridor (utilidor). These high-accuracy, coregistered data produced a 3-D model with assigned temperature values for measured surfaces, useful in identifying thermal anomalies and areas for potential improvements and for assessing building and utilidor infrastructure by locating and quantifying areas settlement and structural anomalies. The ground-based survey of the Crary Lab was similar to previous work performed by the team at both Palmer (2015) and South Pole (2017) Stations. The UAS platform focused on approximately 10,500 linear-feet of utilidor throughout McMurdo Station. The datasets of the two survey areas overlapped, allowing us to combine them into a single, georeferenced 3-D model of McMurdo Station. Coincident exterior temperature and atmospheric measurements and Global Navigation Satellite System real-time kinematic surveys provided further insights. Finally, we assessed the thermal envelope of the Crary Lab and the structural features of the utilidor. The resulting dataset is available for analysis and quantification.

12 May 2021

Methodology for Remote Assessment of Pavement Distresses from Point Cloud Analysis

Abstract: The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.

14 Apr 2020

PUBLICATION NOTIFICATION: Coincidence Processing of Photon-Sensitive Mapping Lidar Data

 Link: http://dx.doi.org/10.21079/11681/35599 Report Number: ERDC/GRL TR-20-1Title: Coincidence Processing of Photon-Sensitive Mapping Lidar DataBy Christian Marchant, Ryan Kirkpatrick, and David OberApproved for Public Release; Distribution is Unlimited February 2020Abstract: Photon-sensitive mapping lidar systems are able to image at greater

15 Jan 2020

PUBLICATION NOTICE: An Airborne Lidar Point Cloud-based Below-Canopy Line-of-Sight Visibility Estimator

 Link: http://dx.doi.org/10.21079/11681/35175Report Number: ERDC/GRL MP-20-1Title: An Airborne Lidar Point Cloud-based Below-Canopy Line-of-Sight Visibility EstimatorBy Heezin Lee, S. Bruce Blundell, Michael J. Starek, and John G. HarrisApproved for Public Release; Distribution is Unlimited January 2020Abstract:  Point cloud data collected by