Urban Remote Sensing

1 Cover

2 Title Page

3 Copyright Page

4 Dedication Page

5 List of Contributors

6 Authors Biography

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 CASE STUDY II: A PATCH‐BASED RNNs MODEL FOR LAND CLASSIFICATION 8.5 DISCUSSION AND CONCLUSIONS ACKNOWLEDGMENTS REFERENCES CHAPTER 9: Google Earth Engine Advancing Urban Land Change Science 9.1 INTRODUCTION 9.2 GEE EVOLUTION IN URBAN STUDIES 9.3 EXAMPLES 9.4 DISCUSSION 9.5 CONCLUSIONS REFERENCES CHAPTER 10: Use of Image Endmember Libraries for Multi‐Sensor, Multi‐Scale, and Multi‐Site Mapping of Urban Areas 10.1 INTRODUCTION 10.2 IMAGE ENDMEMBER LIBRARIES FOR URBAN MAPPING 10.3 EXTRACTION AND IDENTIFICATION OF IMAGE ENDMEMBER SPECTRA 10.4 PRUNING OF IMAGE ENDMEMBER LIBRARIES 10.5 USE OF IMAGE ENDMEMBER LIBRARIES FOR MEDIUM‐RESOLUTION MAPPING OF URBAN LAND COVER COMPOSITION 10.6 USE OF MULTI‐SITE SPECTRAL LIBRARIES FOR VIS MAPPING ACROSS CITIES 10.7 TOWARD A GENERIC URBAN LIBRARY MANAGEMENT TOOL ACKNOWLEDGMENTS REFERENCES CHAPTER 11: Satellite Monitoring of Urbanization and Environmental Impacts in Stockholm, Sweden, Through a Multiscale Approach 11.1 INTRODUCTION 11.2 A BRIEF REVIEW OF REMOTE SENSING‐BASED MULTISCALE ANALYSIS 11.3 STUDY AREAS AND DATA DESCRIPTION 11.4 METHODOLOGY 11.5 RESULTS 11.6 DISCUSSION 11.7 CONCLUSIONS REFERENCES

11 PART IV: Urban Socioeconomic Applications CHAPTER 12: Global Monitoring with the Atlas of Urban Expansion 12.1 INTRODUCTION 12.2 ASSEMBLING THE GLOBAL SAMPLE OF CITIES 12.3 MEASURING URBAN EXTENT 12.4 URBAN EXTENT GROWTH RATE, POPULATION GROWTH RATE, AND THE CHANGE IN LAND CONSUMPTION PER CAPITA 12.5 ASSESSING ACCURACY WITH URBAN EXTENT LOCALES 12.6 CLASSIFICATION ACCURACY AND MAP AGREEMENT 12.7 CONCLUSIONS ACKNOWLEDGMENTS REFERENCES CHAPTER 13: Effect of Image Classification Accuracy on Dasymetric Population Estimation 13.1 INTRODUCTION 13.2 ASSESSING THE ACCURACY OF IMAGE CLASSIFICATION 13.3 DASYMETRIC MAPPING AND AREAL INTERPOLATION 13.4 STUDY AREA AND DATA SOURCES 13.5 METHODS 13.6 RESULTS 13.7 CONCLUSIONS ACKNOWLEDGMENTS REFERENCES CHAPTER 14: Mapping the Morphology of Urban Deprivation 14.1 BACKGROUND 14.2 DEPRIVATION: AN AREA‐BASED AND RELATIVE CONCEPT 14.3 DATA SOURCES: OVERVIEW OF EO‐DATA IN RELATION TO THEIR BENEFITS AND COSTS 14.4 TOWARD A GLOBAL MAPPING PRODUCT: PEOPLE‐PIXELS‐PRIVACY 14.5 MAPPING METHODS: ACHIEVING SCALABILITY AND TRANSFERABILITY 14.6 EXAMPLES OF MAPPING URBAN DEPRIVATION 14.7 CONCLUSIONS ACKNOWLEDGMENTS REFERENCES CHAPTER 15: The City is the Medium and Satellite Imagery Are a Prism 15.1 INTRODUCTION 15.2 OPPORTUNITIES FOR SATELLITE‐BASED MONITORING OF URBAN CONFLICT DAMAGE 15.3 (RE‐)SITUATING URBAN CONFLICT DAMAGE 15.4 THEORIZING CHANGE IN URBAN CONFLICT DAMAGE MONITORING 15.5 CONCLUSIONS REFERENCES

12 PART V: Urban Environmental Applications CHAPTER 16: US Cities in the Dark 16.1 INTRODUCTION 16.2 NASA BLACK MARBLE DATA 16.3 CO 2EMISSION MODELING 16.4 RESULTS 16.5 EXAMINING BLACK MARBLE NTL DATA AS ESTIMATOR OF CO 2EMISSIONS 16.6 NEW ADVANTAGES IN NTL‐BASED MAPPING, CHALLENGES, AND FUTURE PERSPECTIVES 16.7 CONCLUSIONS ACKNOWLEDGMENTS REFERENCES CHAPTER 17: Thermal Infrared Imaging of the Urban Landscape to Understand Urban Microclimate 17.1 INTRODUCTION 17.2 METHODOLOGY 17.3 STUDY SITE AND DATA 17.4 RESULTS 17.5 DISCUSSION AND CONCLUSIONS ACKNOWLEDGMENTS REFERENCES CHAPTER 18: Monitoring Air Pollution in the Urban Environment by Remote Sensing 18.1 AIR POLLUTION MONITORING PROBLEM: THE COMPLEXITY 18.2 SATELLITE RETRIEVED AOD vs GROUND‐BASED PM2.5 MEASUREMENTS: BASIC DEFINITIONS 18.3 APPLYING DIFFERENT SATELLITE AOD RETRIEVALS OVER THE URBAN ENVIRONMENT: DATA COVERAGE AND SPATIAL RESOLUTION 18.4 SATELLITE‐RETRIEVED AOD COMPARED TO GROUND‐BASED AND OTHER MEASUREMENTS AVAILABLE 18.5 MODELING PM 2 .5USING SATELLITE AND LAND USE DATA 18.6 MODEL ACCURACY ASSESSMENT 18.7 LEARNING AIR POLLUTION FROM PUBLICLY AVAILABLE SOURCES: SEVERAL EXAMPLES 18.8 CURRENT AND FUTURE STEPS AND NEW TECHNOLOGIES: BEYOND THE SATELLITE MONITORING REFERENCES CHAPTER 19: Characterizing After‐Rain Standing Waters in Urban Built Environments Through a Multilevel Image Analysis 19.1 INTRODUCTION 19.2 MATERIALS AND METHODS 19.3 RESULTS 19.4 DISCUSSION 19.5 CONCLUSIONS ACKNOWLEDGMENTS REFERENCES CHAPTER 20: Remote Sensing and Urban Green Infrastructure 20.1 INTRODUCTION 20.2 CURRENT APPLICATIONS 20.3 RECENT ADVANCES 20.4 CONCLUSIONS REFERENCES CHAPTER 21: Remote Sensing for Urban Sustainability Research and Sustainable Development Goals 21.1 INTRODUCTION 21.2 REMOTE SENSING AND EARTH OBSERVATION 21.3 REMOTE SENSING FOR SDGs 21.4 AN ANALYSIS OF THE SCIENTIFIC LITERATURE IN THE FIELD OF SUSTAINABILITY 21.5 DISCUSSION 21.6 SUMMARY AND CONCLUSIONS REFERENCES

13 Index

14 End User License Agreement

1 Chapter 2 Table 2.1 Coefficients of determination between DSM and lidar for nine US ci...

2 Chapter 6Table 6.1 Basic statistics for the six natural cities after the data process...

3 Chapter 8Table 8.1 Deep learning models used for various remote sensing applications.Table 8.2 The land cover classification scheme used in the two case studies.Table 8.3 Sample dimensions and architecture of the three related models.Table 8.4 Summary of the classification accuracy assessment for the proposed...Table 8.5 Summary of the classification accuracy assessment for the proposed...

4 Chapter 10Table 10.1 Indicators of mapping accuracy for vegetation, impervious surface...