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Table of Contents
1 Cover
7 Preface
8 PART I: Introduction CHAPTER 1: Progress in Urban Remote Sensing: An Overview 1.1 INTRODUCTION 1.2 ADVANCES IN URBAN REMOTE SENSING 1.3 OVERVIEW OF THE BOOK 1.4 SUMMARY AND CONCLUDING REMARKS REFERENCES
9 PART II: Sensors and Systems for Urban Areas CHAPTER 2: Examining Urban Built‐up Volume: Three‐Dimensional Analyses with Lidar and Radar Data 2.1 INTRODUCTION 2.2 THREE‐DIMENSIONAL (3D) GEOSPATIAL DATA FOR URBAN REMOTE SENSING 2.3 LIGHT DETECTION AND RANGING (LIDAR) APPROACHES 2.4 RADIO DETECTION AND RANGING (RADAR) APPROACHES 2.5 A LOOK FORWARD 2.6 CONCLUSION ACKNOWLEDGMENTS REFERENCES CHAPTER 3: Opportunities and Challenges of Unmanned Aircraft Systems for Urban Applications 3.1 INTRODUCTION 3.2 COMMON UAS MODELS AND SENSORS 3.3 DATA COLLECTION AND PROCESSING 3.4 UAS FOR URBAN APPLICATIONS 3.5 CASE STUDY: MAPPING AN URBAN RECREATION COMPLEX WITH UAS 3.6 MAJOR CHALLENGES AND POSSIBLE SOLUTIONS 3.7 SUMMARY AND OUTLOOK REFERENCES CHAPTER 4: Methods of Social Sensing for Urban Studies 4.1 INTRODUCTION 4.2 SENSING FIRST‐ORDER PLACE CHARACTERISTICS 4.3 SENSING SECOND‐ORDER SPATIAL DEPENDENCY AND INTERACTIONS 4.4 INTEGRATING PLACE CHARACTERISTICS WITH SPATIAL INTERACTIONS 4.5 CONCLUSIONS REFERENCES CHAPTER 5: Urban Remote Sensing Using Ground‐Based Street View Images 5.1 INTRODUCTION 5.2 LITERATURE REVIEW 5.3 DATA SOURCES OF STREET‐LEVEL IMAGES 5.4 STREET‐LEVEL IMAGE PROCESSING 5.5 URBAN MAPPING AND MODELING 5.6 DISCUSSION AND FUTURE RESEARCH DIRECTIONS REFERENCES CHAPTER 6: Spatial Distribution of City Tweets and Their Densities 6.1 INTRODUCTION 6.2 NATURAL CITIES, STREET BLOCKS, AND RELATED DISTANCES 6.3 DATA AND DATA PROCESSING 6.4 TWO MAJOR FINDINGS 6.5 IMPLICATIONS OF THE STUDY AND ITS FINDINGS 6.6 CONCLUSIONS ACKNOWLEDGMENTS REFERENCES CHAPTER 7: Integrating Remote Sensing and Social Sensing to Examine Socioeconomic Dynamics 7.1 INTRODUCTION 7.2 REMOTE SENSING AND SOCIAL SENSING 7.3 PEOPLE AND PIXELS 2.0: TOWARD AN INTEGRATION OF REMOTE SENSING AND SOCIAL SENSING 7.4 DISCUSSION AND CONCLUSIONS REFERENCES
10 PART III: Algorithms and Techniques for Urban Attribute Extraction CHAPTER 8: Deep Learning for Urban and Landscape Mapping from Remotely Sensed Imagery 8.1 INTRODUCTION 8.2 AN OVERVIEW OF SOME COMMONLY USED DEEP LEARNING MODELS 8.3 CASE STUDY I: A PATCH‐BASED CNNs MODEL FOR LAND COVER CLASSIFICATION 8.4