Urban Remote Sensing. Группа авторов

of Illustrations

      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