Infrastructure condition assessment based on low-cost hyper-spatial resolution multispectral digital aerial photography Zhang, Su; Bogus, Susan M.; Lippitt, Christopher D.
Infrastructure condition information is critical for effective asset management. Infrastructure managers are tasked with regularly assessing asset conditions to make effective maintenance, repair, and rehabilitation decisions. Currently there are two types of methods broadly adopted for infrastructure condition assessment: on-site evaluation methods and airplane-based observation methods. On-site evaluation methods are expensive, labor-intensive, time-consuming, potentially dangerous to inspectors, inconsistent, and requiring specialized staff on a regular basis. Airplane-based observation methods can provide reliable overall condition information for ground infrastructure assets such as roadways, bridges, dams, or buildings, but the spatial resolutions of 0.075-meter (3-inch) to 1-meter are insufficient to examine detailed asset conditions such as individual cracks on a pavement surface or on a bridge. Using roadway pavement assets as an example, this research explored the utility of hyper-spatial resolution (3-millimeter) multispectral digital aerial photography acquired from a low-altitude unmanned remote sensing system to permit characterization of detailed surface distress conditions. With the help of orthogonal regression analysis, detailed pavement surface distress rates manually estimated from hyper-spatial resolution multispectral digital aerial photography were compared to reference pavement distress rates manually collected on the ground. The results show that the hyper-high spatial resolution imaging techniques provide detailed and reliable data suitable for informing infrastructure system management decisions. These results open the way for the future application of low-cost hyper-spatial resolution digital aerial photography for automated assessment of detailed infrastructure system condition.
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Attribution-NonCommercial-NoDerivs 2.5 Canada