TUM Library

Cover; Half Title; Series Page; Title Page; Copyright Page; Table of Contents; Preface; Editors; Contributors; 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment; 1.1 Introduction; 1.2 Smartphone-Driven Assessment of Airport Pavement Condition; 1.2.1 Description of Smartphone Application; 1.2.2 Smartphone Characteristics; 1.3 Case Study of Missouri Airports; 1.3.1 Calibration Study; 1.3.2 Missouri Airport Smartphone Data Collection Methodology; 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport; 1.3.4 Discussion.

1.4 Prediction of PCI Based on Smartphone-Measured IRI1.4.1 Machine Learning Method; 1.4.2 GEP-Based Formulation of PCI; 1.5 Conclusions; Acknowledgments; References; 2: Global Satellite Observations for Smart Cities; 2.1 Introduction; 2.2 Overview of NASA Satellite-Based Global Data Products for Smart Cities; 2.2.1 Satellite-Based Data Products at the GES DISC; 2.2.1.1 Multi-Satellite and Multi-Sensor Merged Global Precipitation Products; 2.2.1.2 Global and Regional Land Data Assimilation Products; 2.2.1.3 Modern-Era Retrospective Analysis for Research and Applications (MERRA) Products.

2.3 Data Services2.3.1 Point-and-Click Online Tools; 2.3.1.1 NASA's Worldview; 2.3.1.2 NASA GES DISC Giovanni; 2.3.2 Data Rod Services; 2.3.3 Other Web Data Services; 2.4 Examples; 2.4.1 The Pearl River Delta; 2.4.1.1 Typhoon Nida Rainfall; 2.4.1.2 Atmospheric Composition Preliminary Analysis; 2.4.2 Estimation of Hurricane Contribution to Annual Precipitation in Maryland; 2.4.2.1 Data and Methods; 2.4.2.2 Preliminary Results; 2.5 Summary and Future Plans; Acknowledgments; References; 3: Advancing Smart and Resilient Cities with Big Spatial Disaster Data; 3.1 Introduction.

3.2 The Role of Spatial Data in Coastal Resilience Applications3.2.1 Disaster Management Cycle; 3.2.2 Data Acquisition; 3.2.3 Challenges and Opportunities; 3.3 A Hurricane Sandy Inspired Big Data Framework for Coastal Resilience Investigations with Heterogeneous Spatial Data; 3.3.1 Geospatial Response to Hurricane Sandy; 3.3.2 Data Analytic Framework; 3.3.3 Anatomy of Big Spatial Disaster Data; 3.3.3.1 Volume; 3.3.3.2 Data Structure; 3.3.3.3 Spatial Completeness; 3.3.3.4 Veracity; 3.3.3.5 Velocity; 3.3.4 Decomposition of Processing Tasks; 3.3.4.1 Digital Elevation Models.

3.3.4.2 Feature Extraction3.3.4.3 Change Detection; 3.3.4.4 Core Operation Categories; 3.3.5 Identify the Uncertainty Associated with Big Data Acquisition and Processing; 3.3.6 Computing with Big Data Infrastructure; 3.3.7 Connecting Data Processing with Decision-Making Models; 3.3.8 Future Improvement; 3.4 Conclusion; References; 4: Smart City Portrayal; 4.1 Introduction; 4.2 Background and Related Work; 4.2.1 Point Representation; 4.2.2 Geographic Generalization; 4.2.3 Heatmap; 4.2.4 Circular Plot; 4.2.5 Schematic Map; 4.3 Point Representation: DESIGN STUDY; 4.3.1 Concept and Formalization.

4.3.2 Color-Coding.

The development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradigms such as cloud computing and Internet of Things (IoT). The book serves as a reference for researchers and engineers in domains of advanced computation, optimization, and data mining for smart civil infrastructure condition assessment, dynamic visualization, intelligent transportation systems (ITS), cyber-physical systems, and smart construction technologies. The chapters are presented in a hands-on manner to facilitate researchers in tackling applications. Arguably, data analytics technologies play a key role in tackling the challenge of creating smart cities. Data analytics applications involve collecting, integrating, and preparing time- and space-dependent data produced by sensors, complex engineered systems, and physical assets, followed by developing and testing analytical models to verify the accuracy of results. This book covers this multidisciplinary field and examines multiple paradigms such as machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. The book explores new territory by discussing the cutting-edge concept of Big Data analytics for interpreting massive amounts of data in smart city applications.

OCLC-licensed vendor bibliographic record.