UAV-based technologies are evolving and improving at a rapid pace. The abundance of solutions and systems available today can make it difficult to identify the best option for construction and civil projects. The purpose of this literature review is to examine the benefits and limitations of UAV-based sensing systems in the context of construction management and civil engineering, with a focus on camera-based and laser-based systems. The risk factors associated with UAV operations at construction sites are also considered.

Coastal environments are some of the most dynamic environments in the world. As they are constantly changing, so are the technologies and techniques we use to map and monitor them. The rapid advancement of sUAS-based remote sensing calls for rigorous field and processing workflows so that more reliable and consistent sUAS projects of coastal environments are carried out. Here, we synthesize the best practices to create sUAS photo-based surveying and processing workflows that can be used and modified by coastal scientists, depending on their project objective. While we aim to simplify the complexity of these workflows, we note that the nature of this work is a craft that carefully combines art, science, and technology. sUAS LiDAR is the next advancement in mapping and monitoring coastal environments. Therefore, future work should consider synthesizing best practices to develop rigorous field and data processing workflows used for sUAS LiDAR-based projects of coastal environments.

In cochlear implants, loudness has been shown to grow more slowly with increasing pulse phase duration (PPD) than with pulse amplitude (PA), possibly due to “leaky” charge integration. This leakiness has been recently quantified in terms of “charge integration efficiency,” defined as the log difference between the PPD dynamic range and PA dynamic range (both expressed in charge units), relative to a common threshold anchor. Such leakiness may differ across electrodes and/or test ears, and may reflect underlying neural health. In this study, we examined the across-site variation of charge integration in recipients of Cochlear© devices. PPD and PA dynamic ranges were measured relative to two threshold anchors with either a 25- or 50-microsecond PPD. Strength-duration functions, previously shown to relate to survival of spiral ganglion cells and peripheral processes, were compared to charge integration efficiency on selected electrodes. Results showed no significant or systematic relationship between the across-site variation in charge integration efficiency and electrode position or threshold levels. Charge integration efficiency was poorer with the 50-μs threshold anchor, suggesting that greater leakiness was associated with larger PPD dynamic ranges. Poorer and more variable charge integration efficiency across electrodes was associated with longer duration of any hearing loss, consistent with the idea that poor integration is related to neural degeneration. More variable integration efficiency was also associated with poorer speech recognition performance across test ears. The slopes of the strength-duration functions at maximum acceptable loudness were significantly correlated with charge integration efficiency. However, the strength-duration slopes were not predictive of duration of any hearing loss or speech recognition performance in our participants. As such, charge integration efficiency may be a better candidate to measure leakiness in neural populations across the electrode array, as well as the general health of the auditory nerve in human cochlear implant recipients.

This study provides an evaluation of multiple sensors by examining their precision and ability to capture topographic complexity. Five different small unmanned aerial systems (sUAS) were evaluated, each with a different camera, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU). A lidar was also used on the largest sUAS and as a mobile scanning system. The quality of each of the seven platforms were compared to actual surface measurements gathered with real-time kinematic (RTK)-GNSS and terrestrial laser scanning. Rigorous field and photogrammetric assessment workflows were designed around a combination of structure-from-motion to align images, Monte Carlo simulations to calculate spatially variable error, object-based image analysis to create objects, and MC32-PM algorithm to calculate vertical differences between two dense point clouds. The precision of the sensors ranged 0.115 m (minimum of 0.11 m for MaRS with Sony A7iii camera and maximum of 0.225 m for Mavic2 Pro). In a heterogenous test location with varying slope and high terrain roughness, only three of the seven mobile platforms performed well (MaRS, Inspire 2, and Phantom 4 Pro). All mobile sensors performed better for the homogenous test location, but the sUAS lidar and mobile lidar contained the most noise. The findings presented herein provide insights into cost–benefit of purchasing various sUAS and sensors and their ability to capture high-definition topography.