The construction industry is an essential tributary of the global economy and an important resource in the overall product of the local economies. In December 2019, the construction industry was exposed to a shock beyond the local level as a result of COVID-19, which was classified by the World Health Organization as a global pandemic after its rapid and sudden spread in multiple countries. The nature of the effects of COVID-19 on the construction industry varied according to the diversity of its projects. The pandemic raised many challenges at the level of the workforce, low or halting productivity, production time and costs, as well as disputes in contractual formulas for construction projects. But some of the effects were more severe and caused consequences for all stakeholders in the construction sector. Researchers and those interested in the construction industry have dealt with the repercussions of COVID-19, but most of the publications focused on studying the impact of the pandemic on the local construction industry, so this paper attempts to shed light on the most prominent effects of COVID-19 on the construction industry around the world, and which were repeated with high frequency in previous studies and reports by following the methodology of review, analysis and selection, and then suggesting optimal coping strategies to reduce the damages of these effects.

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The Digital Elevation Model (DEM) has been known as a quantitative description of the surface of the Earth, which provides essential information about the terrain. DEMs are significant information sources for a number of practical applications that need surface elevation data. The open-source DEM datasets, such as the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), and the Advanced Land Observing Satellite (ALOS) usually have approximately low accuracy and coarser resolution. The errors in many datasets of DEMs have already been generally examined for their importance, where their quality could be affected within different aspects, including the types of sensors, algorithms, terrain types, and other features. Ground control points (GCPs) used in this study were observed through the utilization of differential global positioning system (DGPS) with dual frequencies. Statistical indices were used to compare, evaluate, and validate the DEMs data against DGPS data. Statistical analysis of DEMs pointed out that SRTM accuracy was higher, with Root Mean Square Error (RMSE) of ±6.276m as compared to the other DEMs. ASTER showed the biggest residual error with an RMSE of ±10.241m. Nevertheless, ALOS was noticeably improved by having an RMSE of ±6.988m.