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1 Chapter 2FIGURE 2.1 Current, former, and future 3D data sources for Earth observation...FIGURE 2.2 3D lidar‐derived visualizations of downtown Austin, Texas looking...FIGURE 2.3 Lidar data processing workflows, data products, and analysis appr...FIGURE 2.4 Lidar‐derived rasters (1 m spatial resolution) for Detroit, Michi...FIGURE 2.5 Lidar workflow to obtain building‐only volume: raw point cloud da...FIGURE 2.6 An example of built‐up change in southeast San Antonio, Texas: 20...FIGURE 2.7 Citywide built‐up change (shown with dDHM) in San Antonio, Texas,...FIGURE 2.8 Pattern of DSM backscatter for greater Los Angeles, California (a...FIGURE 2.9 Spatial trend patterns for Austin, Texas in 2006 overlaid on Goog...FIGURE 2.10 City and data extents along with raw and processed data and poly...FIGURE 2.11 City and data extents along with raw and processed data and poly...FIGURE 2.12 Geometry of incidence and scattered fields.

2 Chapter 3FIGURE 3.1 As flying along the gridded flight path, UAS collects overlapping...FIGURE 3.2 (a) An aerial view of the Urban Recreation Complex. (b) A flight ...FIGURE 3.3 An example of the 3D point cloud of the urban recreation complex ...FIGURE 3.4 (a) Point cloud data of urban recreation building. (b) 3D tiled m...FIGURE 3.5 (a) A digital surface model (DSM) of the urban recreation complex...

3 Chapter 4FIGURE 4.1 Social sensing framework at the individual and geographical aggre...FIGURE 4.2 The generation of temporal signatures in social sensing.FIGURE 4.3 Sensing place locale characteristics from street view images: (a)...FIGURE 4.4 Sensing human emotions based on facial expressions using Flickr p...

4 Chapter 5FIGURE 5.1 The coverage of Google Street View images around the world.FIGURE 5.2 Downloading GSV panoramas: (a) the street map, (b) sample points ...FIGURE 5.3 Cylindrically projected Google Street View panoramas.FIGURE 5.4 The mosaic of the six faces of Microsoft Streetside cubic skybox ...FIGURE 5.5 The transformation of the cylindrical panoramas to azimuthal hemi...FIGURE 5.6 The geometric transformation of the cubic skybox faces (a) to cyl...FIGURE 5.7 The geometric transformation of a cylindrically projected GSV pan...FIGURE 5.8 The extraction of the street greenery from the street‐level image...FIGURE 5.9 The classification of sky areas in hemispherical images: (a) the ...FIGURE 5.10 (a) The architecture of the PSPNet on segmenting GSV images....FIGURE 5.11 The workflow for collecting street‐level images and calculating ...FIGURE 5.12 Sites with different green view index values.FIGURE 5.13 The green view index map in Singapore from Treepedia.FIGURE 5.14 The calculation of sky view factor (SVF) based on hemispherical ...FIGURE 5.15 The spatial distribution of sky view factor (SVF) at street leve...FIGURE 5.16 The geometric model of the sun positions (a) and the overlay of ...FIGURE 5.17 The spatial distributions of street‐level sunlight exposure at 9...FIGURE 5.18 The paths of minimum sunlight exposure (green line) and the path...FIGURE 5.19 The smartphone with a panorama camera for collecting geotagged s...

5 Chapter 6FIGURE 6.1 Illustration of a natural city (within the red border) derived fr...FIGURE 6.2 Illustration of related distances based on city blocks of the nat...FIGURE 6.3 Illustration of creating the London natural city (Note: The creat...FIGURE 6.4 The six natural cities from the three European countries: France,...FIGURE 6.5 Spatial distribution of tweets and densities from the center of L...FIGURE 6.6 Spatial distribution of tweets and densities from the center of P...FIGURE 6.7 Spatial distribution of tweets and densities from the London cent...FIGURE 6.8 Spatial distribution of tweets and densities from the Paris cente...

6 Chapter 7FIGURE 7.1 Landscape evolution of geospatial technologies.FIGURE 7.2 The stable lights image product (a) and the tweet image (b) for t...FIGURE 7.3 Estimation efficiency of LBSM and NTL for the total personal inco...FIGURE 7.4 Estimation efficiency of LBSM and NTL for electric power consumpt...FIGURE 7.5 Logistic correlation between the natural logarithm of the number ...FIGURE 7.6 (a) Original DMSP‐OLS stable light image for the year 2013, (b) a...FIGURE 7.7 Comparison of the estimated personal incomes of DFW counties, wit...

7 Chapter 8FIGURE 8.1 An example of a fully connected Multilayer Perceptron (MLP) with ...FIGURE 8.2 The structure of a typical convolutional neural networks (CNNs) m...FIGURE 8.3 Illustrating the conventional RNNs (recurrent neural networks) ce...FIGURE 8.4 The architecture of the patch‐based CNNs (convolutional neural ne...FIGURE 8.5 Loss (a) and accuracy (b) plots for the training data.FIGURE 8.6 Land cover maps generated by different models: (a) upper left: pi...FIGURE 8.7 The architecture of the PB‐RNNs model.FIGURE 8.8 Land cover maps generated by different models: (a) upper left: pi...

8 Chapter 9FIGURE 9.1 The yearly number of publications by searching “Earth Engine” as ...FIGURE 9.2 Flowchart of the Ppf‐CM.FIGURE 9.3 The classification results in the Dandou Sea area in the mapping ...

9 Chapter 10FIGURE 10.1 Concept of the LUISA framework for automated extraction and iden...FIGURE 10.2 LUISA validation results for automatically determined pure pixel...FIGURE 10.3 AMUSES consistently outperforms the IES pruning algorithm for 20...FIGURE 10.4 True endmember spectra and pruned libraries generated using IES,...FIGURE 10.5 Workflow of a map‐based approach to produce quantitative trainin...FIGURE 10.6 Workflow of a library‐based approach to produce quantitative tra...FIGURE 10.7 VIS mapping results for map‐based and library‐based training. En...FIGURE 10.8 Average MAEs and GPR uncertainties over all VIS categories for B...

10 Chapter 11FIGURE 11.1 Location of the Stockholm study areas: Stockholm County is outli...FIGURE 11.2 The Stockholm City study area used in Furberg et al. (2020). Cit...FIGURE 11.3 Land‐cover classification methodology.FIGURE 11.4 Conceptual relation of the environmental indicators used in the ...FIGURE 11.5 Growth trends for urban versus nonurban land cover in Stockholm ...FIGURE 11.6 Contagion trends for Stockholm County and City during 1986–2018....FIGURE 11.7 Percent population increases in Stockholm County for specific 10...FIGURE 11.8 Conceptual and technical framework of the multiscale analysis ap...

11 Chapter 12FIGURE 12.1 The global distribution of all 4231 observations in the 2010 uni...FIGURE 12.2 The eight world regions and the locations of the 200 sample citi...FIGURE 12.3 The sampling framework consisting of 96 boxes, each box correspo...FIGURE 12.4 The three‐way classification of Baku, Azerbaijan into water (blu...FIGURE 12.5 A close up of Baku’s three‐way classification at 2014, illustrat...FIGURE 12.6 The subclassification of built‐up area into urban pixels (dark r...FIGURE 12.7 The subclassification of open space into fringe open space (ligh...FIGURE 12.8 Urban clusters across the Baku study area in July 1989 (left) an...FIGURE 12.9 The Baku urban extent in July 1989 (left) and August 2014 (right...FIGURE 12.10 The untransformed and log‐transformed distribution of urban ext...FIGURE 12.11 The untransformed and log‐transformed distribution of urban ext...FIGURE 12.12 Exponential growth rates and quantity increase over time.FIGURE 12.13 Distributions of urban extent and population growth rates for T...FIGURE 12.14 The distribution of the urban extent growth rate minus the popu...FIGURE 12.15 The upper left quadrant of shows the bounding box containing th...FIGURE 12.16 An Addis Ababa locale boundary (left); the digitization of its ...FIGURE 12.17 The transformation of the digitized reference data and Atlas la...FIGURE 12.18 Distributions of pixel‐based overall accuracy calculated at the...FIGURE 12.19 Outlines of the Toledo, Ohio urban extent created by Atlas (blu...

12 Chapter 13FIGURE 13.1 Study area of Mecklenburg County, North Carolina, displayed with...FIGURE 13.2 Gridded population estimates for a portion of Mecklenburg County...FIGURE 13.3 Image classification accuracy (a) and dasymetric map error (b) a...FIGURE 13.4 Classification outputs for 6 of the 16 iterations: (a) RF0, (b) ...FIGURE 13.5 Consistency of false‐positive pattern in (a) RF0, versus inconsi...

13 Chapter 14FIGURE 14.1 Pan‐sharpened Pleiades image of Dar es Salaam deprived area (a) ...FIGURE 14.2 Various areal conceptualizations to aggregate deprivation areas ...FIGURE 14.3 Level of detail of different sensors, comparing WorldView‐2, Pla...FIGURE 14.4 Combining datasets for the development of a global repository on...FIGURE 14.5 Mumbai deprived areas mapped at an aggregation level of 100 m (a...FIGURE 14.6 Summary of Earth Observation methods used in the key peer‐review...FIGURE 14.7 Ahmedabad, India: part of the historic city center (a) and depri...FIGURE 14.8 Recent developments in the field of CNNS to (a) work with limite...

14 Chapter 16FIGURE 16.1 The NASA Black Marble‐based CO₂ emission map over CONUS. EPA yea...FIGURE 16.2 An ISS camera image (a) and CO₂ emission map (b) for the New Yor...FIGURE 16.3 Same as Figure 16.2, but for Texas cities. The ISS camera image ...FIGURE 16.4 Same as Figure 16.2, but for the Atlanta area. Atlanta Nightlife...FIGURE 16.5 The ISS camera images and CO₂ over Chicago, Los Angeles, Phoenix...FIGURE 16.6 Comparisons of CO₂ emissions scaled by different NTL and populat...FIGURE 16.7 A comparison of NBM (VIIRS) estimated state CO₂ and EPA state es...FIGURE 16.A.1 A comparison of DMSP OLS (a), (b) and Radiance calibrated ligh...FIGURE 16.8 The spatial correlation and emission difference (normalized) as ...FIGURE 16.9 Atmospheric CO₂ concentration enhancement over Los Angeles (LA) ...FIGURE 16.10 The column‐averaged CO₂ differences from the control run: (a) O...FIGURE 16.A.2 Histograms of modeled column averaged CO₂ differences. (a) ODI...FIGURE 16.A.3 Same as Figure 16.9, but atmospheric CO₂ mole fractions calcul...FIGURE 16.A.4 Same as Figure 16.10, but the boundary layer atmospheric CO₂ d...FIGURE 16.A.5 Same as Figure 16.A.4, but atmospheric CO₂ differences. (a) Bi...

15 Chapter 17FIGURE 17.1 Radiation source area of a remote sensor that receives radiance ...FIGURE 17.2 Urban space with 3D geometry.FIGURE 17.3 Actual versus estimated T_c: (a) T_c from Eq. (17.11) (daytime) an...FIGURE 17.4 Workflow to estimate the complete from the radiometric urban sur...FIGURE 17.5 Location of the study area.FIGURE 17.6 Building height of the study area.FIGURE 17.7 Surface temperature retrieved by the single‐channel method from ...FIGURE 17.8 Surface temperature retrieved by the single‐channel method from ...FIGURE 17.9 Daytime, T_r and T_c and their difference T_c − T_r retrieved from t...FIGURE 17.10 Nighttime T_r and T_c estimated by applying Eq. (17.10) to the AS...

16 Chapter 18FIGURE 18.1 A conceptual framework to study urban air quality by remote sens...FIGURE 18.2 (a) Satellite‐derived aerosol optical depth (AOD) vs ground‐base...FIGURE 18.3 Global average AOD during April 2018 as an example. Black dots r...FIGURE 18.4 The spatial and temporal resolution considerations. (a) Coarse (...FIGURE 18.5 (a) MOD04 10‐km (left column), 3‐km (middle), and MAIAC 1‐km (ri...FIGURE 18.6 The location of PM2.5 monitoring sites over pixels of different ...FIGURE 18.7 Using AOD coefficient of variability (CV) to infer local vs regi...FIGURE 18.8 MODIS MAIAC (470 nm) vs AERONET AOD (extrapolated 470 nm) correl...FIGURE 18.9 (a) Frequency distribution of daily AOD vs PM2.5 correlations. (...FIGURE 18.10 (a) Building percentage coverage for a 30‐m grid generated in A...FIGURE 18.11 Different spatial patterns emerged when using different input c...FIGURE 18.12 Mixed effect model performance using the AOD + Land Use (LU) + ...FIGURE 18.13 (a) The relationship between column aerosol optical depth (AOD)...FIGURE 18.14 Center: A scatter plot of the measured and predicted PM_2.5 conc...FIGURE 18.15 Spatial distribution and RMSE values of the ground monitoring s...FIGURE 18.16 An example of using the publicly available dataset of estimated...

17 Chapter 19FIGURE 19.1 An overview of the research framework consisting of two major pa...FIGURE 19.2 Location of the study area, Taipei City: (a) The nonmountainous ...FIGURE 19.3 An illustration of the daily accumulated precipitation and the t...FIGURE 19.4 The multiscale data and images that were used in this study: (a)...FIGURE 19.5 An illustration of change detection to monitor new water bodies ...FIGURE 19.6 Spatial distributions of rainfall patterns during the heavy rain...FIGURE 19.7 (a) The spatial distribution of the calculated NDVI with a resol...FIGURE 19.8 The relationship between the predicted probability of standing w...FIGURE 19.9 The spatial distribution of urban morphological characteristics ...FIGURE 19.10 The identified urban areas with a higher probability of having ...

18 Chapter 20FIGURE 20.1 Basic machine learning flowchart describing the assessment of ur...FIGURE 20.2 Example of a Convolutional Neural Network architecture.

19 Chapter 21FIGURE 21.1 The 17 Sustainable Development Goals (SDGs) of the 2030 Agenda: ...FIGURE 21.2 Comparison of current research activities in terms of contents. ...FIGURE 21.3 Different green spaces in urban areas as shown in a false‐color ...FIGURE 21.4 Quantitative results from the Scopus search analysis. Note that ...FIGURE 21.5 Country‐specific publication activities (top 10) regarding “remo...FIGURE 21.6 Country‐specific publication activities (top 10) regarding “remo...FIGURE 21.7 Country‐specific publication activities (top 10) regarding “remo...FIGURE 21.8 Country‐specific publication activities (top 10) regarding “remo...FIGURE 21.9 Country‐specific publication activities (top 10) regarding “remo...FIGURE 21.10 Country‐specific publication activities (top 10) regarding “rem...FIGURE 21.11 Number of publications per year for the period of 2000 and 2019...