Deterioration of bridge infrastructure is a serious concern to transport and government agencies as it declines serviceability and reliability of bridges and jeopardizes public safety. Maintenance and rehabilitation needs of bridge infrastructure are periodically monitored and assessed, typically every two years. Existing inspection techniques, such as visual inspection, are time-consuming, subjective, and often incomplete. Non-destructive testing (NDT) using Unmanned Aerial Vehicles (UAVs) have been gaining momentum for bridge monitoring in the recent years, particularly due to enhanced accessibility and cost efficiency, deterrence of traffic closure, and improved safety during inspection. The primary objective of this study is to conduct a comprehensive review of the application of UAVs in bridge condition monitoring, used in conjunction with remote sensing technologies. Remote sensing technologies such as visual imagery, infrared thermography, LiDAR, and other sensors, integrated with UAVs for data acquisition are analyzed in depth. This study compiled sixty-five journal and conference papers published in the last two decades scrutinizing NDT-based UAV systems. In addition to comparison of stand-alone and integrated NDT-UAV methods, the facilitation of bridge inspection using UAVs is thoroughly discussed in the present article in terms of ease of use, accuracy, cost-efficiency, employed data collection tools, and simulation platforms. Additionally, challenges and future perspectives of the reviewed UAV-NDT technologies are highlighted.

Climate change is undoubtedly affecting the global weather of the Earth. Rapid human civilization has mainly caused this in the last few decades. This research examined the spatial and temporal land surface temperature (LST) in the United Arab Emirates (UAE) coastal cities located in an arid region that faced massive urbanization over the last 50 years. We estimated the LST using by-products of Landsat and MODIS images covering 2000 until 2020. The assessment of LST was performed in two contexts, i.e., spatially and temporally, covering daytime vs. nighttime during the summer and winter seasons. Additionally, a supervised classification technique was adopted to extract the land use and land cover in the study area from the late 1970s until 2018. Unexpectedly, the results indicated that daytime LST in districts near the coastlines (heavily urbanized areas) are lower than the ones far away from the coast (about 9 °C). This observation represents the spatial LST inversion in the study area. Nevertheless, this difference was not observed during nighttime LST temporally, the daytime LST did not increase significantly during either summer or winter seasons. However, the nighttime LST has increased temporally by about 17% since 2000 (the temporal LST inversion). Both LST inversions could be attributed to the uniqueness of the study area, given that bare land desert was replaced by vegetation, high-rise buildings, and industrial activities. Additionally, the wind breeze blowing from the gulf might potentially contribute to cooling the coastal urban area during the daytime. Furthermore, in-depth zonal statistics were conducted to visualize the effect of land use on LST. The study observed that fully built-up areas with vegetation have lower LST than built-up areas without vegetation or a combination of sand and vegetation. The research outcomes are invaluable for decision-makers and researchers in achieving sustainable urban development.