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Climate change poses a critical global challenge, necessitating the monitoring of shorelines to understand its impact. Satellite images and Google Earth Engine (GEE) are commonly used to monitor shoreline changes with reasonable accuracy. This study aims to improve the precision and reliability of shoreline monitoring by developing an automated deterministic technique using Landsat images and GEE. The developed technique identifies pure water and land pixels by applying spectral index thresholds. In addition, it employs Monte Carlo simulation to generate multispectral reflectance values for pixels with varying water percentages. Then a linear fitting model is employed to estimate the wetness coefficient in each pixel in the image. Finally, geospatial software, in the GIS environment, is used for estimating the shoreline changes using the estimated wetness coefficient map. To assess and verify the proposed shoreline estimation technique, five regions were selected, including Egypt’s northern coast, as well as shorelines in Morocco, India, Japan and Portugal, spanning the years from 2003 to 2022. The results show an effective estimation of shoreline changes with a root mean square error of 0.56-pixel size, indicating subpixel accuracy. A notable advantage of this method is its flexibility, as it derives information directly from the image, making it suitable for a wide range of regions with different water and soil characteristics. Therefore, it can be used to offer valuable insights for monitoring shoreline changes and supporting coastal management and planning efforts. The findings of the case studies revealed that breakwaters effectively reduced erosion in coastal areas of Egypt and Portugal, whereas the coastal regions of India and Morocco remained stable. Conversely, Japan experienced a high erosion rate (− 2.83 ± 4.08 m/year) in its coastal areas due to wave height. This emphasises the importance of monitoring shoreline changes and developing effective strategies to mitigate the negative impacts of climate change on coastal areas.

A dependable transportation infrastructure is an essential local resource, linking communities and facilitating economic expansion. The Nile Delta, renowned for its high population density, is experiencing subsidence as a result of natural forces exerting pressure on its sediments. Land subsidence exerts a substantial influence on infrastructure, economic, and social progress. This study examines the evaluation of land subsidence in roads experiencing various issues in the Nile Delta. Each year, subsidence results in significant damage and, in certain instances, complete disruption of numerous road networks, with potential durations ranging from a few days to several months. Conventional methods for identifying and monitoring subsidence on a large and intricate road network can be challenging due to the substantial time and resource requirements. Satellite-based monitoring is essential as it can effectively detect ground displacements caused by human activities (such as subterranean diggings and disruption of other infrastructures) or environmental dangers, specifically those associated with crucial hydrogeological incidents. Moreover, it possesses the benefit of efficiently encompassing extensive regions with precision. This study utilized the persistent scatterer interferometry (PSI) of SNAP-StaMPS workflow to employ differential interferometry synthetic aperture radar (DInSAR) processing methods for investigating the area. By utilizing Sentinel-1 data, we successfully acquired a comprehensive understanding of the deformation occurring in the study area, specifically in the roads where subsidence is persistently taking place. The data has demonstrated its value in enhancing our understanding of the safety status of infrastructure and facilitating maintenance actions. The accuracy of the radar interferometry results was confirmed through the use of in situ methods, including Global Navigation Satellite System (GNSS), Global Positioning System (GPS), smart phone, and unmanned aerial vehicle. This approach has demonstrated its capability to consistently detect vertical displacement in critical areas of roads. Furthermore, there is a strong correlation between the PSI interferometry and the in situ measurements when absolute values are taken into consideration. The findings of this study also demonstrate that open-source solutions are more effective, which encompass freely available data and software, offered through the COPERNICUS program, possess significant potential for monitoring vertical displacements in roads.

The digital elevation model (DEM) is crucial in many global and regional scientific studies in civilian and military applications. The aim of this research is to develop and test a new DEM approach for correcting the various errors in the Shuttle Radar Topography Mission (SRTM) digital elevation model. Firstly, the DEMs with the feature attributes from Sentinel-2 multispectral imagery are generated. Secondly, SRTM DEM with one band and attributes of a sentinel-2 image with eight bands are used as input data in supervised max-like hood, an artificial neural network (ANN), and support vector machine (SVM) classification models. Thirdly, ANN, supervised max-like hood, and SVM classification models, which have various properties, are fused by fuzzy majority voting (probability fusion). Finally, the fused probability is assigned for each pixel of the image, which has 12 fixed ground control points (GCPs), which is considered new input data for the inverse probability weighted interpolation (IPWI) approach to create the corrected SRTM elevations. The results were contrasted with a reference DEM (RD) created by image matching with Worldview-1 stereo satellite images, which had a 1-m vertical accuracy. The results of this study demonstrated that the RMSE of the original SRTM DEM was 5.95. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 1.98 compared with that of the MLR method (3.01). The study shows a series of significant improvements in the SRTM when assessed with the reference DEM, with an RMSE reduction of (66.72%) when compared to the widely utilized multiple linear regression (MLR) method. It can be concluded that the elevation error of the original SRTM DEM is clearly reduced by the suggested approach.

Road pavement deformation monitoring is considered the main task for maintenance purposes, especially potholes and cracks, which are the most common types of road deformation surfaces. In order to make pavement inspections more effective, new types of remote sensing data that do not damage the pavement are being used more and more to find pavement distress. This article presents a proposed approach for extracting surface cracks from unmanned aerial vehicle (UAV) images using machine learning, focusing on the data pre-treatment processes. The objective of this study is to evaluate the effectiveness of decision tree classification (DT) in detecting cracks. The performance of the models is also evaluated. The performance evaluation approach is predicated on two primary criteria: model validation and testing. Also, the extent of the impact of post-classification operations, edge detection technology, and morphological processes on crack identification as well as classification accuracy, the digital orthomosaic was generated by the use of a technique commonly referred to as backward projection. To achieve this, the study uses a fusion of gray-level co-occurrence matrix (GLCM) attribute data and RGB images. Cracks are discovered using a classification tree (CT)-based classification approach with an overall classification rate of 86%. Ultimately, morphological processes using the closed image that was formed had a commendable level of accuracy, with an overall classification rate of 96%. The Canny edge detection algorithm has demonstrated its efficacy as a preferred method for detecting cracks from UAV images, providing invaluable decision support for actual road maintenance.

AbstractRising floodwaters in the Blue Nile (BN) and White Nile (WN) tributaries continually affects people’s lives and the safety of infrastructures in Khartoum, the capital of Sudan. Recently, floods have covered a wide area of Khartoum and destroyed many buildings and bridges. We conducted this study to evaluate and predict the flood effects around BN and WN in Khartoum to make recommendations in view of upcoming flood disasters. Land-use/land-cover (LULC) changes after floods that occurred in 2014, 2016, and 2020 were evaluated and LULC was predicted for 2024 by applying a combination of remote sensing (RS) and geographic information-system (GIS) techniques. We also evaluated the expected hazard due to floods. Our results showed that the agricultural, built-up, and water changes around BN were 0.30%, 0.25%, and 0.05%, respectively, for the period 2014–2016, and, that changes for the period 2016–2020 were 3.38%, 3.68%, and 7.28%, respectively. Correspondingly, the agricultural, built-up, and water changes around WN were 5.56%, 3.26%, and 8.02% during 2014–2016 and 13.79%, 5.62%, and 21.40% during 2016–2020. The change estimation indicates that the most flood effects in the region occurred around WN, possibly covering the whole areal extent of Khartoum city. The area covered by waterbodies may decrease by 4.18% (BN) and 20.35% (WN) during 2020–2024, based on RS–GIS evaluation, but hydraulic evaluation shows that the expected hazard due to floods in Khartoum is high. Thus, flood-risk assessments should take into account both physical hazard and the distribution of the population across the whole area of Khartoum.

AbstractThe number of bridges in South Korea has almost tripled in the past few decades. Monitoring the structural behavior of these bridges is essential to maintain their performance in the infrastructure network and keep them in the best possible condition. The study presented in this paper aims to evaluate the Tun Bridge, located in Seoul, Korea. The bridge was tested under static and dynamic loads following the American Association of State Highway and Transportation Officials (AASHTO) standards. Twenty load cases and a wide range of truck speeds were applied during the bridge test. The bridge responses were captured using a short-period structural health monitoring system. Statistical analysis and signal processing for all the measurements were used to assess the structural behavior and fatigue life of the bridge. Bridge deformation/deflection and fatigue responses were evaluated. Furthermore, comparison with the available data from previous tests on the same bridge (17 years earlier) showed that the changes in static deflection and dynamic frequency were 13.87% and 2.73%, respectively. In addition, deflection and fatigue responses were found to be negligible under static and dynamic loads. Therefore, it was concluded that the bridge’s behavior during its operational state was shown to be safe.