Urbanization and Crisis Management Using Geomatics Technologies

Substantial work has been done by Geospatial Information and Communications Technology (GeoICT) and Disaster Management communities to evaluate and develop tools and applications that integrate the complex interrelationships that are required for adequate preparedness, planning, mitigation, response, and recovery from extreme situations. GeoICT technologies have contributed and are contributing to saving life and property throughout the globe. Over the past decade, extensive research has resulted in more advanced GeoICT technologies. This has helped to maximize the demand for these tools, with a noticeable pattern of adoption and expanding user community. This chapter provides an overview of selected rising stars in GeoICT technology and their applications in disaster management. This discussion evaluates the trends in technology development, with emphasis on data collection, processing, and visualization

Lessons Learned from the Establishments of the First Hydrographic Surveying Program in the Middle East

The fast pace of technology development and voluntary adoption of international standards requires interdisciplinary and skill-based education. This chapter presents an approach for the development of an interdisciplinary, internationally recognized geomatics program, at King Abdulaziz University (KAU), using a multilevel approach that combined the international guidelines with the local stakeholders’ needs being in line with the global demand for professionals in this field. The methodology of this study consisted of interviews with subject matter experts (SMEs), students survey and operational analysis, and observation was used to analyze the program challenges and opportunities. Results obtained showed that the transferability of the approach adopted in this research, along with the commonality of lessons learned from the process, contributes to faster execution for similar programs in various parts of the world. The program was successful to secure to international recognition within 10 years of its inception. The quality of learning outcomes supported by the high employability of graduates was among the key socioeconomic impact of the program

Sensitivity Analysis and Modeling for DEM Errors

The Digital Elevation Model (DEM) can be created using airborne Light Detection And Ranging (LIDAR), Image or Synthetic-Aperture Radar (SAR) mapping techniques. The direct georeferencing of the DEM model is conducted using a GPS/inertial navigation system. The airborne mapping system datasets are processed to create a DEM model. To develop an accurate DEM model, all errors should be considered in the processing step. In this research, the errors associated with DEM models are investigated and modeled using Principal Component Analysis (PCA) and the least squares method. The sensitivity analysis of the DEM errors is investigated using PCA to define the significant GPS/inertial navigation data components that are strongly correlated with DEM errors. Then, the least squares method is employed to create a functional relationship between the DEM errors and the significant GPS/inertial navigation data components. The DEM model errors associated with airborne mapping system datasets are investigated in this research. The results show that the combined PCA analysis and least squares method can be used as a powerful tool to compensate the DEM error due to the GPS/inertial navigation data with about 27% in average for DEM errors produced by the direct georeferenced airborne mapping system

Trends in Geomatics - An Earth Science Perspective

The applications of geomatics technology in its broader context have resulted in significant progress in the field of earth science. This book provides brief coverage on some trends in geomatics technology as it relates to earth scientists. The development in geomatics, whether GIS, remote sensing, GPS or photogrammetry, can be seen from trends in the applications of Big Data, Smart City, Internet of Things (IoT), the use of augmented reality and utilization of unmanned aerial vehicles (UAVs) and in the impact of machine learning and AI on geomatics

Transforming the Industry: Digitalization and Automation in Oil and Gas Wells

The oil and gas industry is undergoing a significant transformation with the advent of digitalization and automation technologies. This chapter explores the impact of digitalization and automation on drilling and completion operations in oil and gas wells. The integration of advanced technologies, such as artificial intelligence, machine learning, and robotics, has revolutionized the way wells are planned, drilled, and completed. Digitalization has enabled real-time data acquisition, analysis, and visualization, allowing operators to make informed decisions and optimize drilling and completion processes. Automated systems, including robotic drilling and remotely operated equipment, have enhanced operational efficiency, safety, and cost-effectiveness. The chapter discusses the implementation of digital twin models for virtual well planning and simulation, as well as the use of autonomous drilling systems and smart completion technologies. Moreover, the chapter addresses the challenges and opportunities associated with digitalization and automation, such as data security, workforce reskilling, and the need for collaboration across the industry. It emphasizes the potential for improved well performance, reduced environmental impact, and enhanced reservoir management through the integration of digitalization and automation in oil and gas wells

Mapping Coastal Flood Susceptible Areas Using Shannon’s Entropy Model: The Case of Muscat Governorate, Oman

Floods are among the most common natural hazards around the world. Mapping and evaluating potential flood hazards are essential for flood risk management and mitigation strategies, particularly in coastal areas. Several factors play significant roles in flooding and recognizing the role of these flood-related factors may enhance flood disaster prediction and mitigation strategies. This study focuses on using Shannon’s entropy model to predict the role of seven factors in causing floods in the Governorate of Muscat, Sultanate of Oman, and mapping coastal flood-prone areas. The seven selected factors (including ground elevation, slope degree, hydrologic soil group (HSG), land use, distance from the coast, distance from the wadi, and distance from the road) were initially prepared and categorized into classes based on their contribution to flood occurrence. In the next step, the entropy model was used to determine the weight and contribution of each factor in overall susceptibility. Finally, results from the previous two steps were combined using ArcGIS software to produce the final coastal flood susceptibility index map that was categorized into five susceptibility zones. The result indicated that land use and HSG are the most causative factors of flooding in the area, and about 133.5 km2 of the extracted area is threatened by coastal floods. The outcomes of this study can provide decision-makers with essential information for identifying flood risks and enhancing adaptation and mitigation strategies. For future work, it is recommended to evaluate the reliability of the obtained result by comparing it with a real flooding event, such as flooding during cyclones Gonu and Phet