text:
data descriptors
Outlining where humans live, the World Settlement Footprint 2015
A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia
Hadi, Ping Yowargana, … Florian Kraxner
A 10 m resolution urban green space map for major Latin American cities from Sentinel-2 remote sensing images and OpenStreetMap
Yang Ju, Iryna Dronova & Xavier Delclòs-Alió
So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale
Sugandha Doda, Yuanyuan Wang, … Xiao Xiang Zhu
CatLC: Catalonia Multiresolution Land Cover Dataset
Carlos García, Oscar Mora, … Jordi Vitrià
A crowdsourced global data set for validating built-up surface layers
Linda See, Ivelina Georgieva, … Steffen Fritz
Global forest management data for 2015 at a 100 m resolution
Myroslava Lesiv, Dmitry Schepaschenko, … Steffen Fritz
Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics
Fiona M. Jones, Carlos Arteta, … Tom Hart
High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015
Xiaoping Liu, Yinghuai Huang, … Zhenzhong Zeng
Estimating global economic well-being with unlit settlements
Ian McCallum, Christopher Conrad Maximillian Kyba, … Steffen Fritz
Data Descriptor
Mattia Marconcini ORCID: orcid.org/0000-0002-5042-51761,
Annekatrin Metz-Marconcini1,
Soner Üreyen ORCID: orcid.org/0000-0003-3733-00491,
Daniela Palacios-Lopez1,
Wiebke Hanke1,
Felix Bachofer1,
Julian Zeidler1,
Thomas Esch ORCID: orcid.org/0000-0002-5868-90451,
Noel Gorelick2,
Ashwin Kakarla3,
Marc Paganini4 &
Emanuele Strano5
Scientific Data volume 7, Article number: 242 (2020) Cite this article
Developing world
Human settlements are the cause and consequence of most environmental and societal changes on Earth; however, their location and extent is still under debate. We provide here a new 10 m resolution (0.32 arc sec) global map of human settlements on Earth for the year 2015, namely the World Settlement Footprint 2015 (WSF2015). The raster dataset has been generated by means of an advanced classification system which, for the first time, jointly exploits open-and-free optical and radar satellite imagery. The WSF2015 has been validated against 900,000 samples labelled by crowdsourcing photointerpretation of very high resolution Google Earth imagery and outperforms all other similar existing layers; in particular, it considerably improves the detection of very small settlements in rural regions and better outlines scattered suburban areas. The dataset can be used at any scale of observation in support to all applications requiring detailed and accurate information on human presence (e.g., socioeconomic development, population distribution, risks assessment, etc.).
Measurement(s) global settlement extent
Technology Type(s) satellite imaging • machine learning
Factor Type(s) geographic location
Sample Characteristic - Environment anthropogenic environment • populated place
Sample Characteristic - Location Earth (planet)
Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12424970
Background & Summary
Scientific investigations related to the human presence on Earth strongly rely on the availability of accurate and reliable information on the extent and location of settlements. In this framework, since early 1980s satellite imagery has been used as primary source to outline settlements at global scale1,2 and - along with technical, methodological and computational advances - their detail evolved from low resolution (1 km - 500 m) to medium resolution (100 m) and, since the last few years, to high resolution (30–10 m).
Nowadays, satellite-based settlement extent maps are widely used for many scientific purposes. For instance, in global urbanization analyses3 they are used to define urban areas, as well as characterize their morphology and assess the correlation with socio-economic variables. In spatial demography4, the location of settlements represents a fundamental spatial covariate to model the displacement of people, whereas in land-use science5,6 settlement extent is exploited as key input for calibrating land-use change models.
In the last few years, different high resolution layers outlining the global settlement extent have been presented in the literature7,8,9,10,11,12,13. Among these, the three most largely employed include:
the Global Urban Footprint – GUF7 (available at 12 m resolution and referring to the year 2012) generated by the German Aerospace Center (DLR) from 3 m resolution TerraSAR-X/TanDEM-X radar imagery;
the 2014 instance of the Global Human Settlement Layer – GHSL8, generated at 30 m resolution by the Joint Research Center (JRC) of the European Commission from Landsat-8 optical imagery;
the artificial surfaces mask of the GLOBELAND30 – GLC309 (available at 30 m resolution and referring to the year 2010), generated by the National Geomatics Center of China from Landsat-5/7 optical imagery.
Among these, the GUF outperforms the other two layers13, which show severe under- and over-estimation in large parts of the world. Nevertheless, the GUF itself still exhibits two major drawbacks. On the one hand, it has been generated (like the GHSL and the GLC30) from single-date scenes, which are sometimes strongly affected by the specific acquisition conditions, hence resulting in misclassification errors. On the other hand, commercial imagery has been employed, which prevents a systematic update due to its high costs. Moreover, the exclusive use of optical or radar imagery alone represents an additional limitation since these two types of data are sensitive to different structures on the ground (i.e., artificial surfaces and built-up areas, respectively). For instance, bare soil and sand tend to be misclassified as settlements using optical imagery, while this does generally not occur with radar data; on the contrary, complex topography areas or forested regions can be wrongly categorized as settlements with radar imagery, whereas normally this does not occur using optical data.
To overcome these issues, we have developed a novel and robust methodology to reliably outline settlements which jointly exploits, for the first time, open-and-free multitemporal optical and radar data. In particular, the rationale is that the temporal dynamics of human settlements over time are different than those of all other non-settlement classes. First, we gather all the images acquired over a region of interest within a target period during which we do not expect considerable changes (e.g., one year). Next, we extract key temporal statistics (i.e., temporal mean, minimum, maximum, etc.) of: (i) the original backscattering value in the case of radar data; and (ii) different spectral indices (e.g., vegetation index, built-up index, etc.) derived after performing cloud/cloud-shadow masking in the case of optical imagery. After automatically extracting candidate training samples for the settlement and non-settlement class, binary classification based on advanced machine learning is separately applied to the optical- and radar-based temporal features. Finally, the two outputs are properly combined together.
Once tested its high robustness on a variety of study sites, the method has been employed to generate the World Settlement Footprint (WSF) 2015, a 10 m resolution (0.32 arc sec) binary mask outlining the extent of human settlements globally derived by means of 2014–2015 multitemporal Sentinel-1 (S1) radar and Landsat-8 optical imagery (of which ~107,000 and ~217,000 scenes have been processed, respectively). The WSF2015 is extremely accurate and reliable and outclasses all other mostly employed similar datasets. This has been quantitatively assessed through an unprecedented validation exercise based on 900,000 ground-truth samples collected by crowdsourcing photointerpretation and carried out in collaboration with Google. To this purpose a statistically robust and transparent protocol has been defined following recommended state-of-the-art practices.
In this Section, we describe the novel methodology developed for outlining human settlement extent based on the joint use of multitemporal radar and optical imagery. The corresponding block scheme is reported in Fig. 1. First, both S1 and Landsat-8 data are pre-processed and suitable temporal statistics and texture features are computed. Then, training points for the settlement and non-settlement classes are derived by jointly exploiting both radar- and optical-based temporal statistics (along with additional ancillary information). Classification is performed separately for the two types of data by means of an ensemble of Support Vector Machines (SVM) classifiers. A final post-classification phase is dedicated to properly combine the Landsat- and S1-based classification maps and automatically identifying and deleting potential false alarms.
Block scheme. Schematization of the workflow implemented for outlining human settlement extent from Sentinel-1 (S1) radar and Landsat-8 optical multitemporal satellite imagery.
Each of the abovementioned steps is described into detail in the following. Next, the WSF2015 layer is presented along with all relevant details concerning its implementation.
Preprocessing and feature extraction
As concerns S1 data, we take into account imagery acquired in Interferometric Wide swath (IW) mode (i.e., S1 main mode over land with 250 km swath). In particular, we consider High-Resolution Level-1 Ground Range Detected (GRD) products available at 10 m resolution.
All scenes acquired over the given study area in the target timeframe are first gathered and then pre-processed by means of the S1 Toolbox14. Specifically, this task includes:
orbit correction (for improving the geocoding);
thermal noise removal (for removing dark strips near scene edges with invalid data);
radiometric calibration (for computing backscattering intensity using sensor calibration parameters in the GRD metadata);
Range-Doppler terrain correction (for removing the brightness and geometric distortions occurring in correspondence of elevated and sloping terrain);
conversion to decibel (dB) values (for reducing the very high dynamic range of data).
Scenes acquired in ascending and descending pass are treated separately due to the strong influence of the viewing angle in the backscattering of built-up areas. Furthermore, experimental analyses assessed that the joint employment of VV/VH imagery does not provide any considerable improvement with respect to the solely use of VV data; accordingly, VH data are disregarded.
As pointed out above, the rationale of the proposed approach is that given a series of multi-temporal images for a study area, the corresponding temporal dynamics of human settlements are sensibly different than those of all other non-settlement classes. For instance, in the case of radar data the backscattering temporal mean of built-up areas (due to double bounce reflection) is higher than that of forest areas (which might result in high backscattering in one/few acquisitions due to specific conditions, but in general exhibit lower values). To properly characterize this behavior, for each pixel we compute 5 key temporal statistics, namely the backscattering temporal maximum, minimum, mean, standard deviation, and mean slope (i.e., defined as the average absolute difference between consecutive items of the temporal series).
Texture information is also extracted to ease the identification of lower-density residential areas mostly characterized by single houses surrounded by vegetation (which are generally challenging to detect due to their lower backscattering values with respect to that of denser urban areas). To this purpose, we compute the coefficient of variation (COV) of the temporal mean backscattering, which is defined for each pixel as the ratio between the local standard deviation and the local mean calculated over a NxN pixel spatial neighborhood. In particular, the COV represents an estimate of the local image heterogeneity. Here, in the light of the 10 m spatial resolution of the considered S1 data, a neighborhood of 5 × 5 pixels proved to be an effective choice.
Overall, both for VV ascending/descending passes, the final S1 feature stack includes 7 features, namely: the 5 abovementioned temporal statistics and the COV derived from the backscattering temporal mean, plus the number of available scenes per pixel.
In the case of Landsat-8, imagery taken at 30 m resolution by the Operational Land Imager (OLI) sensor is used. In particular, we only consider scenes acquired in the target period over the study area with cloud cover lower than 60% (as reported in the corresponding metadata). Indeed, we experienced that further raising this threshold often results in accounting for images with non-negligible misregistration error. Data are then calibrated and Top-Of-Atmosphere (TOA) radiance is extracted.
A mask is then generated for each image to exclude pixels affected by cloud and cloud shadows from the analysis. To this purpose the Function of mask (FMask) algorithm is applied given its assessed effectiveness in the scientific community15. Besides pixels covered by clouds and cloud shadows, the algorithm also identifies snow, clear land and clear water pixels; in particular, this is done by jointly analyzing the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Snow Index (NDSI), and the Brightness Temperature for the given scene.
A thorough experimental analysis has been carried out to identify a set of spectral indices highly suitable for an effective delineation of human settlements; in particular, the final list and corresponding formulas are reported in Table 1. The Normalized Difference Built-Up Index (NDBI)16 has been applied to extract built-up areas in many studies17,18; nevertheless, due to the use of the first short-wave infrared (SWIR) band (i.e., OLI band 6) this index is also sensitive to vegetation with low water content19, which exhibits values comparable to those of settlement areas. Accordingly, the Normalized Difference Middle Infrared index (NDMIR) and the NDVI are applied to overcome this issue. On the one hand, the NDMIR is computed using both SWIR bands (i.e., OLI bands 6 and 7), thus being sensitive to vegetation moisture20. On the other hand, the NDVI21 has been widely employed in a variety of land cover applications as well as in the context of settlement extent classification22,23. Moreover, the Modified Normalized Difference Water Index (MNDWI)24 is also employed to discriminate water from settlement areas. Such index enhances the performance of the NDWI25 by replacing the MIR with the NIR band (i.e., OLI band 5), which leads to a reduction of noise from built-up areas. In addition to the previous, two other spectral indices have been included for improving the discrimination between settlement areas and bare soil/bare rocks; specifically, these are the Normalized Difference Red Blue (NDRB) and Normalized Difference Green Blue (NDGB) indices26.
Table 1 Landsat-8 spectral indices.
To characterize the generally stable temporal dynamics of the settlement class with respect to the other non-settlement classes, the same set of 5 key temporal statistics used in the case of S1 data are extracted for each of the 6 Landsat-8 spectral indices presented above. Moreover, to improve the detection of rural and suburban areas (mostly characterized by a low share of built-up areas and a high share of vegetation, thus resulting in a heterogeneous environment compared to denser built-up areas), also here additional texture features are extracted. In particular, for each of the derived 6 temporal mean indices, we computed the corresponding COV in a neighborhood of 3 × 3 pixels, which empirically proved the most effective choice in the light of the 30 m spatial resolution of Landsat data.
The final Landsat-8 feature stack includes 37 bands, namely: temporal maximum, minimum, mean (plus the corresponding 3 × 3 COV), standard deviation and mean slope for NDBI, NDVI, MNDWI, NDMRI, NDRG and NDGB, along with the number of available cloud/cloud-shadow-free acquisitions per pixel.
In Fig. 2, examples are given for Ho Chi Minh (Vietnam), Istanbul (Turkey), Johannesburg-Pretoria (South Africa), Karachi (Pakistan), Lagos (Nigeria), and Moscow (Russia). Specifically, in addition to reference Google Earth imagery we display for each city: (i) a RGB color composition obtained combining the Landsat-8 temporal mean NDBI (red), NDVI (green) and MNDWI (blue); (ii) the S1 VV backscattering temporal mean. The same visualization parameters have been consistently applied to all 6 sites.
Temporal features. Examples for the cities of Ho Chi Minh (Vietnam), Istanbul (Turkey), Johannesburg-Pretoria (South Africa), Karachi (Pakistan), Lagos (Nigeria) and Moscow (Russia) including: i) Google Earth reference imagery; ii) RGB combination of the Landsat-8 temporal mean NDBI (Red), NDVI (Green) and MNDWI (Blue); and iii) Sentinel-1 IW GRDH VV temporal mean backscattering.
Yet by simple visual inspection, it is possible to appreciate the advantage of jointly employing radar- and optical-based temporal statistics. In particular, even if it is not feasible to properly delineate settlements by solely using radar data, it is clear how optical imagery helps overcoming this issue and vice-versa. For instance, in the case of Lagos, the backscattering is counterintuitively low in several highly urbanized areas; nevertheless, this occurs due to the extremely high building density (mostly informal housing) which prevents the typical radar double bounce reflection. Instead, when employing Landsat imagery the settlement outline is clearly distinguishable. On the contrary, optical-based temporal features are often not effective alone in arid regions - as in Karachi - where bare areas tend to be misclassified as settlements. Nevertheless, these can be effectively outlined by means of S1 temporal statistics.
Training points selection
Reliably identifying training points for the settlement and non-settlement class proved being the most critical task of the whole classification system; indeed, a training set including a consistent number of mislabeled samples would most likely result in poor performances. To this purpose, we designed a strategy which jointly exploits the temporal statistics computed for both S1 and Landsat data, along with additional ancillary information. In particular, any given sample x in the study area is labelled as potentially settlement or non-settlement if it satisfies all the corresponding conditions listed in Table 2 (where different thresholds have been determined based on extensive empirical analysis against Google Earth VHR imagery carried out over 450 test sites of 1 × 1 degree size distributed all over the world).
Table 2 Training sample definition.
Concerning optical data, we generally observed that most of the pixels can be effectively outlined as settlement/non-settlement by jointly thresholding the corresponding NDBI, NDVI, and MNDWI temporal mean features. Nevertheless, being all 3 spectral indices correlated to the presence of vegetation, absolute threshold values are not globally effective as vegetation strongly varies depending on climate. To overcome this drawback, we took into account the well-established Köppen Geiger scheme27 and for each climate type we determined specific thresholds for outlining both candidate settlement and non-settlement training samples. Referring to Table 2, \(KG\left(x\right)\) denotes the Köppen-Geiger classification for the given pixel x. \({A}_{Smin}\left(KG\left(x\right)\right)\) and \({A}_{Smax}\left(KG\left(x\right)\right)\) denote minimum and maximum thresholds, respectively, for the temporal mean \(\bar{\rm{A}}\left(x\right)\) of the spectral index A, A∈{NDBI,NDVI,MNDWI} defined to determine whether x is a candidate settlement training sample. Similarly, \({A}_{NSmin}\left(KG\left(x\right)\right)\) and \({A}_{NSmax}\left(KG\left(x\right)\right)\) denote minimum and maximum thresholds defined to determine whether \(x\) is a candidate non-settlement training sample. Furthermore – in the reasonable hypothesis that the higher is the number of cloud/cloud-shadow free acquisitions, the more robust are the corresponding temporal statistics – we exclude all pixels whose number of Landsat-8 clear observations (i.e., NLC8 (x)) is lower than 5. Since the Köppen Geiger classification includes 30 different climate types, overall we determined 360 thresholds on the three indices.
Regarding radar data, we expect the temporal mean backscattering of most settlement samples to be sensibly higher than that of all other land-cover classes. Accordingly, samples whose temporal mean backscattering (either in the case of data acquired in ascending \({\bar{\sigma }}_{A}^{0}\left(x\right)\) and descending \({\bar{\sigma }}_{D}^{0}\left(x\right)\) pass) is:
lower than -7 dB are not eligible to be labelled as settlement training samples (if the number of ascending/descending scenes used for computing the temporal statistics \({N}_{S1A}\left(x\right)\)/\({N}_{S1D}\left(x\right)\) is higher than or equal to 5);
greater than -11 dB are not eligible to be labelled as non-settlement training samples (if the number of ascending/descending scenes used for computing the temporal statistics \({N}_{S1A}\left(x\right)\)/\({N}_{S1D}\left(x\right)\) is higher than or equal to 5).
It is worth noting that in complex topography regions: (i) radar data often show high backscattering comparable to that of settlements; and (ii) bare rocks are present, which often exhibit a behavior similar to that of built-up areas in the Landsat-based temporal statistics. Accordingly, to exclude these from the analysis, we mask all pixels whose slope28 (i.e., the angle corresponding to the maximum elevation difference between the given pixel and its 8 neighbors) is higher than 10 degrees. To this purpose, we employed the Shuttle Radar Topography Mission (SRTM)29 Digital Elevation Model (DEM) for latitudes between −60° and +60° and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)30 DEM elsewhere.
In the light of their proven effectiveness and high generalization capabilities, Support Vector Machines (SVM)31,32 with Radial Basis Function (RBF) Gaussian Kernel have been chosen for the classification task.
In general, the criteria defined in the previous section result in a high number of candidate training points; thus, a subset should be sampled to keep the computational burden under control. For instance, a reasonable choice when investigating large regions is to subdivide the study area in working units of 1 × 1 degree size; in this case, an effective strategy proved extracting 500 samples for the settlement and 500 for the non-settlement class.
The stacks of Landsat- and S1-based temporal features are classified separately since this proved more effective than performing a single classification on the merger of the two stacks. In both cases, a grid search with a 5-fold cross validation33 approach is employed to identify the optimal values for the learning parameters (i.e., the ones expected to provide the best possible discrimination between the settlement and non-settlement classes). These include \(\gamma \) and \(C\) which tune the SVM kernel spread and error penalization, respectively32. In our analyses, we test all combinations with \(C={2}^{i},i\in {{\mathbb{Z}}}^{\ge 0},i\le 13\) and \(\gamma =0.1\cdot j,j\in {{\mathbb{Z}}}^{+},j\le 20\).
Since results might vary depending on the specific subset of selected training points, as a means to further improve the final performances and obtain more robust classification maps, we randomly subset 20 different training sets and feed an ensemble of as many SVM classifiers. Then, we apply a majority voting approach34,35 to handle the resulting maps and each pixel is finally associated with the settlement class only if it is labeled as settlement at least 11 over 20 times.
Post-Classification
A final post-classification phase is dedicated to properly combine the Landsat- and S1-based classification maps and automatically identifying and deleting false alarms. To this purpose, an updated version of the post-editing object-based approach adopted in the production of the GUF layer has been used7, which exploits the 9 reference binary datasets (7 global and 2 continental) described in Table 3.
Table 3 Reference layers. Reference.
A settlement agreement mask is first generated from the combination of 6 reference layers (i.e., DLR-RC, CIL, OSM-S, OSM-R, GL30-S, and NLCD), which is labeled as positive only where two or more of these are positive. Likewise, a settlement exclusion mask is obtained by combining 3 reference layers (i.e., DLR-RM, GLC30-W, GLC30-WL), which is labelled as positive where at least one of these is positive.
Next, segmentation is applied to both Landsat- and S1-based classification maps for categorizing each cluster of connected pixels as individual objects; in particular, this is carried out by exploiting contour tracing to iterate over an image only once36.
Objects are then removed if:
their extent overlaps for less than 30% the settlement agreement mask and, concurrently, it overlaps for more than 30% the settlement exclusion mask (this helps excluding objects wrongly covering complex topography regions, water or wetlands);
the zonal mean of the Landsat-based temporal mean NDVI is higher than 0.6 (this is mostly the case of false detections in the S1-based classification occurring in correspondence of specific types of dense forests);
the zonal mean of the S1 temporal mean backscattering (either computed for scenes acquired with ascending or descending pass) is lower than -11 dB (this is mostly the case of false detections in the Landsat-based classification occurring in correspondence of bare soil and sand).
The final classification map is given by the merger of the objects preserved in the Landsat- and S1-based classification maps.
The WSF2015
The methodology presented above has been applied globally to generate the WSF2015 layer. Concerning radar data, pre-processing and feature extraction have been performed for ~107,000 S1 scenes (i.e., ~51,000 collected with ascending pass and ~56,000 with descending pass) acquired in 2014–2015. In particular, this task has been directly supported by Google through its Earth Engine cloud computing platform37.
As regards optical imagery, pre-processing and feature extraction have been performed for ~217,000 Landsat-8 scenes acquired in 2014–2015 with less than 60% cloud cover and downloaded from US Geological Survey (USGS), European Space Agency (ESA) and the Google Cloud Storage. All cloud/cloud-shadow masks have been obtained from USGS via the ESPA (Earth Resources Observation and Science (EROS) Center Science Processing Architecture) on demand interface which employs a C version of the FMask algorithm. The resulting dataset, for which more than 1.5PB of intermediate products were generated, is referred to as Landsat TimeScan 201538. Specifically, the whole processing has been carried out at the IT4Innovations Czech supercomputing center (Ostrava) in the framework of ESA's Urban Thematic Exploitation Platform (U-TEP)39 project. Classification has been also carried out in the same infrastructure, whereas post-classification activities have been performed in the Calvalus system40 available at DLR's Earth Observation Center.
To effectively handle the huge amount of data to process, working units of 1 × 1 degree size have been defined and the final WSF2015 is obtained as a mosaic of ~14 K tiles (where at least a single settlement has been detected).
In Fig. 3, an overview of the WSF2015 is given for the entire World, along with 4 different zooms referring to: (a) Eastern China and Korea; (b) Western Europe; (c) Mid-Atlantic USA; and (d) the Nairobi region in Kenya. Zoom a refers to one of the most populated regions of the world, including - among others - the Bohai Economic Rim (at the top), as well as the Yangtze River Delta and Pearl River Delta megalopolis (at the center and bottom, respectively). However, yet at this scale it is immediately evident how, besides the major cities, the WSF2015 also outlines the thousands of medium and small-size settlements scattered throughout the whole region, especially in the North China Plain which exhibits an extremely flat topography. This is also evident in Zoom b, where the myriad of towns and (especially) small villages characterizing the Western European landscape are properly mapped. Moreover, one can also start noticing the high detail in the delineation of the bigger cities (e.g., London at the left, Paris at the bottom left, Berlin at the top right), which can be further appreciated in Zoom c. Here, a portion of the US Northeast megalopolis is shown stretching from Washington-Baltimore (bottom left corner), to Philadelphia (center) and to New York (top right corner). The WSF2015 reliably outlines all major centers, as well as their fragmented metropolitan and suburban areas; concurrently, it also detects the small rural villages located in the nooks of the Appalachian Mountains (top left corner). Finally, in Zoom d the layer proves capable of capturing the very complex settlement pattern north of Nairobi (located at the bottom center) which includes the counties of Muranga and Kiambo. Specifically, these are mostly characterized by a rugged landscape interspersed with several hillocks where residents intensively settled along the many valleys in the region (thus resulting in the striped linear pattern that might be falsely interpreted as misclassification at first sight).
WSF2015. Overview of the WSF2015 for the entire World, along with 4 different zooms referring to: (a) Eastern China and Korea; (b) Western Europe); (c) Mid-Atlantic USA; and (d) the Nairobi region in Kenya. Validation sites selected for assessing the quality of the layer are reported as red squares.
Overall, the WSF2015 estimates a global settlement surface of ~1.28 MKm², which corresponds to ~0.95% of the emerged surfaces (i.e., ~134.77Mkm² excluding Antarctica). The dataset has been recently used by the Authors to perform a thorough analysis of the worldwide settlement spatial variability and structure through advanced scaling analysis41. Settlement density proved not suitable for explaining alone the high variability of existing patterns, hence a novel global categorization is proposed.
Data Records
The WSF2015 layer described in this article is publicly and freely available through figshare42. The dataset is organized for download in 306 GeoTIFF files (EPSG4326 projection, deflate compression) each one referring to a portion of 10 × 10 degree size (~1110 × 1110 km) whose upper-left and lower-right corner coordinates are specified in the file name [e.g., the tile WSF2015_v1_EPSG4326_e010_n60_e020_n50.tif covers the area between (10E;60 N) and (20E;50 N)]. A virtual mosaic file (i.e., WSF2015_v1_EPSG4326.vrt) is also provided which allows visualizing the global product at once on most diffused GIS platforms (e.g., ArcGIS, QGIS, MapInfo). Settlements are associated with value 255; all other pixels are associated with value 0.
Additionally, 5 resampled versions are also provided at 100 m, 250 m, 500 m, 1 km and 10 km, respectively, reporting for each pixel the corresponding ground percent surface covered by settlements. These can be efficiently used, for instance, as input to regional, continental, or global models and are distributed as individual GeoTIFF files embedding overviews for the levels 2, 4, 8, 16, 32, 64, 128 and 256.
Technical Validation
In the framework of remote sensing, accuracy assessment is generally separated into three major components43, namely:
response design, which defines the protocol for determining whether the map and reference classifications are in agreement; sampling design, which defines the protocol for identifying a representative subset of the region under analysis (given the impossibility of applying the response design to the entire classification map);
analysis, which defines how to quantify accuracy.
In the following, the strategy designed for validating the WSF2015 is presented; in particular, specific details are given about the protocols adopted for each of the abovementioned components, while final results are discussed afterwards.
Response design
The four major features of the response design include the source of information from which reference data are taken, the spatial unit, the labeling protocol for the reference classification, and a definition of agreement:
Source of Reference Data: Google Earth satellite/aerial VHR imagery available for the period 2014–2015 has been used. The spatial resolution varies depending on the specific data source; in the case of SPOT imagery it is ~1.5 m, for Digital Globe's WorldView-1/2 series, GeoEye-1, and Airbus' Pleiades it is in the order of ~0.5 m resolution, whereas for airborne data (mostly available for North America, Europe and Japan) it is about 0.15 m.
Spatial Assessment Unit: since input data with different spatial resolutions have been employed to generate the WSF2015 (i.e., 30 m Landsat-8 and 10 m S1), a 3 × 3 block spatial assessment unit composed of 9 cells of 10 × 10 m has been chosen.
Reference Labeling Protocol: in our study we define:
building as any structure having a roof supported by columns or walls and intended for the shelter, housing, or enclosure of any individual, animal, process, equipment, goods, or materials of any kind;
building lot as the area contained within an enclosure (e.g., wall, fence, hedge) surrounding a building or a group of buildings;
road as any long, narrow stretch with a smoothed or paved surface, made for traveling by motor vehicle, carriage, etc., between two or more points;
paved surface as any level horizontal surface covered with paving material.
Based on this taxonomy, 4 possible labels have been defined, namely:
Buildings: if the given cell intersects any building;
Building Lots: if the given cell intersects any building lot and no buildings;
Roads/Paved-Surfaces: if the given cell intersects any road/paved surface and no buildings or building lots;
None of the previous.
The labelling task has been performed by crowdsourcing internally at Google. Specifically, by means of an ad-hoc tool, operators have been iteratively prompted a 3 × 3 assessment unit on top of the available Google Earth reference VHR scene closest in time to the year 2015 and given the possibility of assigning any of the 4 labels defined above to each cell. For training the operators, a representative set of 100 reference 3 × 3 units was prepared in collaboration between Google and DLR.
Defining Agreement: to cope with the different existing definitions of settlement, we computed the assessment figures by separately considering as settlement all areas covered by: (i) buildings; (ii) buildings or building lots; and iii) buildings, building lots or roads/paved-surfaces. Furthermore, 4 different agreement criteria have been defined, specifically:
for each cell, positive agreement occurs only for matching labels between the classification and the reference;
for each block, a majority rule is applied over the entire 3 × 3 block of both the classification and the reference; if the final labels match, then the agreement is positive;
for the classification, a majority rule is applied over the entire 3 × 3 block; for the reference, each block is labelled as settlement only if it contains at least one cell marked as settlement; if the final labels match, then the agreement is positive;
for both the classification and the reference, each block is labelled as settlement only if it contains at least one cell marked as settlement; if the final labels match, then the agreement is positive.
Sampling design
As recommended in the state-of-the-art good practices for assessing land-cover map accuracy44,45, stratified random sampling design has been chosen. In particular, it is a probability sampling design and one of the easiest to implement; indeed, it involves first the division of the population (i.e., the collection of all pixels contained in the map) into mutually exclusive subsets (i.e., strata) within which random sampling is performed afterwards.
To include a representative set of settlement patterns, 50 tiles of 1 × 1 degree size (out of the ~14.000 composing the WSF2015) have been selected based on the ratio between the number of settlements (i.e., disjoint clusters of pixels categorized as settlement in the WSF2015) and their overall area. In particular, the i-th selected tile has been chosen randomly among those whose ratio belongs to the interval \([{P}_{2\left(i-1\right)};{P}_{2i}],i\in \left[1;50\right]\subset {\mathbb{N}}\) (where Px denotes the x-th percentile of the ratio). The final selected tiles are shown in red in Fig. 3.
As the settlement class covers a sensibly smaller area compared to the merger of all other non-settlement classes, an equal allocation reduces the standard error of its class-specific accuracy. Moreover, such an approach allows to best address user's accuracy estimation, which corresponds to the map "reliability" and is indicative of the probability that a pixel classified on the map actually represents the corresponding category on the ground46,47. Accordingly, for each of the 50 selected tiles we randomly extracted 1,000 settlement and 1,000 non-settlement samples from the WSF2015 and used these as center cells of the 3 × 3 block assessment units to be labelled by photointerpretation. Such a strategy resulted in an overall amount of (1,000 + 1,000) × 9 × 50 = 900,000 cells labelled by the crowd. To our knowledge, this outnumbers any other similar exercise presented so far in the literature.
To finally assess the accuracy of the WSF2015, we considered a series of measures commonly employed in the remote sensing community44, namely:
the Kappa coefficient48,49, which jointly takes into account omission (i.e., underestimation) and commission (i.e., overestimation) errors, as well as the possibility of chance agreement between classification and reference maps. Kappa assumes values between −1 and 1 and a common rule-of-thumb for its interpretation is the following50: < 0 no agreement; 0–0.20 slight; 0.21–0.40 fair; 0.41–0.60 moderate; 0.61–0.80 substantial; 0.81–1.0 perfect;
the percent producer's accuracies PAS% and PANS% of the settlement and non-settlement class, respectively. Specifically, they denote the portion of assessment units (i.e., cells or blocks) categorized as settlement/non-settlement according to the collected reference information which are correctly categorized as settlement/non-settlement in the classification map. Its complementary measure (100 – PA%) corresponds to the percent omission error;
the percent user's accuracies UAS% and UANS% of the settlement and non-settlement class, respectively. Specifically, they denote the proportion of all assessment units (i.e., cells or blocks) categorized as settlement/non-settlement in the classification map which are categorized as settlement/non-settlement also according to the collected reference information. Its complementary measure (100 – UA%) corresponds to the percent commission error;
the percent average accuracy AA%, which is obtained as the mean between PAS% and PANS% and represents a balanced measure of correct settlement and non-settlement detection.
Figure 4 reports the accuracies over the 900,000 collected reference samples computed for the WSF2015 and, concurrently, the GUF, GHSL and GLC30 layers for comparison. In particular, results are given for all combinations (overall 12) of three considered settlement definitions and four assessment criteria. Due to the different spatial resolution of the GUF (12 m) and both the GHSL and GLC30 (30 m), while assessing their quality, each 10 × 10 m cell of the considered block spatial assessment unit is tagged as settlement only if the intersection with the specific layer is positive.
Quantitative accuracy assessment of the WSF2015 and comparison against the currently most largely employed global settlement extent layers. Quality assessment figures computed over the 900,000 collected reference samples for the WSF2015, GUF, GHSL and GLC30. Results are concurrently reported for all three settlement definitions and four assessment criteria considered in terms of percent average accuracy (AA%), Kappa coefficient, as well as percent producer's (PA%) and user's (UA%) accuracies for both the settlement (S) and non-settlement (NS) classes.
Noticeably, in all experiments the WSF2015 exhibited the best AA%, with a remarkable average of 86.37 and a mean increase with respect to GUF, GHSL and GLC30 of +6.24, +15.28 and +18.58, respectively. Alongside, it resulted in an average Kappa of 0.6885 with a mean increase of +0.0754 with respect to the GUF and, especially, +0.2338 and +0.2975 with respect to GHSL and GLC30, respectively.
By analyzing the numbers into detail, one can notice a noteworthy increase of the WSF2015 Kappa coefficient for assessment criteria 3 and 4 (0.7646 on average) with respect to criteria 1 and 2 (0.6123 on average). This is due to the fact that 30 m resolution Landsat imagery has been employed to generate the product. Hence, even if just a portion of the Landsat pixel on the ground intersects any building, building lot or paved surface, this mostly has a considerable effect in the corresponding spectral signature and the pixel tends to be finally categorized as settlement. This is taken into account by assessment criteria 3 and 4, since the entire 30 × 30 m reference block spatial assessment unit is labelled as settlement even if it contains just one cell marked as settlement.
Assessment criteria 1 and 2 should be then considered more suitable for a fair comparison against the GUF given its 12 m spatial resolution. In this case, one can appreciate how the AA% and Kappa reported for the WSF2015 are in line with those exhibited by the GUF, which has been generated from highly expensive 3 m resolution commercial TerraSAR-X/TanDEM-X imagery. Instead, assessment criteria 3 and 4 allow a fair comparison against GHSL and GLC30 as they are both derived from Landsat data. Here, the WSF2015 exhibits notable AA% and Kappa up to 89.33 and 0.7822, respectively, outperforming both GHSL and GLC30 (with an increase always higher than 17 and 0.32, respectively).
From Fig. 4, one can also notice that on average results do not significantly vary across the three considered definitions of settlement; however, a proper analysis allows to better understand which one fits best with the different layers. Concerning the WSF2015, the highest accuracies mostly occur when considering as settlement the combination of buildings and building lots. Only for assessment criteria 1 and 2 Kappa is higher when also roads/paved surfaces are included. Indeed, despite generally associated with very low S1 backscattering values, most of these are not masked out given their fine scale within urban areas. As regards the GUF, highest AA% and Kappa occur partly when only buildings and partly when buildings and building lots are considered as settlement. This is in line with the theory, since the layer has been generated from radar imagery which is sensitive to vertical structures (these comprise both buildings, as well as main elements delimiting building lots like walls, fences, hedges, etc.). In the case of GHSL and GLC30, the two layers show a similar behavior and provide on average a slightly higher Kappa when settlements are defined as combination of buildings and building lots.
Giving a closer look to producer's and user's accuracies it is possible to better understand the nature of the different performances. All GUF, GHSL and GLC30 generally show very high PANS% (i.e., >85), but mostly exhibit consistently lower PAS%, with values never greater than 75.80, 52.39 and 44.26, respectively. On the contrary, the WSF2015 scores overall remarkably high PAS% and PANS% (on average 88.71 and 84.04, respectively) and, concurrently, it always shows the best UANS% in front of a UANS% only marginally lower than that of the other layers (on average 92.15 and 75.95, respectively). This quantitatively assesses the capability of the WSF2015 to effectively detect the presence of a considerable number of settlements actually unseen in the other global products. This occurs at the price of a minor settlement extent overestimation mostly due to the employment of the COV texture features; specifically, these allow a more accurate detection in rural and suburban areas, but sometimes result in an overestimation of 1–2 pixels around the actual settlement.
The improved detection performances of the WSF2015 can be qualitatively appreciated in Fig. 5, where a cross-comparison against GUF, GHSL and GLC30 is reported for three representative regions including the Igboland (i.e., a cultural and common linguistic region located in south-eastern Nigeria), Kampala (i.e., the capital and largest city of Uganda) and Bangalore (i.e., the capital of the Indian state of Karnataka). Despite the rather different settlement patterns, all three sites are characterized by the presence of medium and large size cities surrounded by a number of very small settlements. As one can notice, the WSF2015 proves extremely effective in all three cases, outperforming all other layers; specifically, it is capable of detecting a higher amount of small villages and better outlining the fringes of major urban areas. The GUF performs equally good only in the Igboland region, but detects considerably less settlements in the Bangalore and, especially, the Kampala case studies. Both GHSL and GL30 exhibit severe underestimation in all three test sites.
Qualitative cross comparison of the WSF2015 against the currently most largely employed global settlement extent layers. Samples for the WSF2015, GUF, GHSL and GLC30 are reported for the Igboland (Nigeria), Kampala (Uganda) and Bangalore (India) regions.
The WSF2015 will be a valuable product in support to all applications requiring detailed and accurate information on human presence. In particular, combined either with other EO or non-EO-based datasets (e.g., related to climate, health, economy, demography, etc.), it will enable deriving indices and metrics of help not only for scientific research but even decision making.
As assessed by the extensive validation exercise, the WSF2015 proved to be the currently most accurate and reliable product of its kind and will hence allow to improve any type of analysis carried out so far with other existing similar layers. Nevertheless, it is worth pointing out that - due to limitations specific of the data used - it was not feasible to consistently detect very small structures (e.g., huts, shacks, tents) because of their reduced scale, the specific building material employed (e.g., cob, mudbricks, sod, straw, fabric), their temporal nature (e.g., nomad or refugee camps), or the presence of dense vegetation preventing their identification.
The WSF2015 is the result of several processing steps involving tens of sub-modules run on multiple architectures and using different software. While S1 pre-processing and feature extraction has been supported by Google through its Earth Engine platform, the computation of Landsat-8 temporal statistics, the training point extraction and classification tasks have been performed in the IT4Innovations Czech supercomputing center by means of DLR proprietary software, GDAL (Geospatial Data Abstraction Library v.2.4) and Pktools (Processing Kernels for geospatial data v2.6) scripts. Post-classification has been carried out in the Calvalus system available at DLR's Earth Observation Center by means of proprietary software and dedicated Python (v3.5) scripts. Given the use of proprietary tools, the code cannot be openly released to the public.
Potere, D. & Schneider, A. A critical look at representations of urban areas in global maps. GeoJournal 69, 55–80 (2007).
Gamba, P. & Herold, M. Global mapping of human settlement: experiences, datasets, and prospects (CRC Press, 2009).
Fragkias, M., Güneralp, B., Seto, K. C. & Goodness, J. A synthesis of global urbanization projections. In Urbanization, biodiversity and ecosystem services: Challenges and opportunities, 409–435 (Springer, 2013).
Tatem, A. J. Worldpop, open data for spatial demography. Sci. Data 4, 170004 (2017).
Lambin, E. & Meyfroidt, P. Trends in global land-use competition. In Seto, K and Reenberg, A. Rethinking Global Land Use in an Urban Era (Strungmann Forum Reports, 2014).
Seto, K. C., Güneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. 109, 16083–16088 (2012).
Esch, T. et al. Breaking new ground in mapping human settlements from space - The Global Urban footprint. ISPRS J. Photogramm. Remote Sens. 134, 30–42 (2017).
Corbane, C. et al. Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data 3, 140–169 (2019).
Chen, J. et al. Global Land Cover Mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 103, 7–27 (2015).
Gong, P. et al. Finer Resolution Observation and Monitoring of Global Land Cover: First Mapping Results with Landsat TM and ETM+ Data. Int. J. Remote Sens. 34, 2607–2654 (2013).
Wang, P., Huang, C., Brown de Colstoun, E. C., Tilton, J. C. & Tan, B. Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat. NASA Socioeconomic Data and Applications Center –SEDAC, https://doi.org/10.7927/H4DN434S (2017).
Liu, X. et al. High-Resolution Multi-Temporal Mapping of Global Urban Land Using Landsat Images Based on the Google Earth Engine Platform. Remote Sens. Environ. 209, 227–239 (2018).
Esch, T. et al. Where We Live—A Summary of the Achievements and Planned Evolution of the Global Urban Footprint. Remote Sens. 10, 895–913 (2018).
Foumelis, M. ESA SNAP Sentinel-1 Toolbox, http://eoscience.esa.int/landtraining2017/files/materials/D2P11_P.pdf (2017).
Zhu, Z., Wang, S. & Woodcock, C. E. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sens. Environ. 159, 269–277 (2015).
Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583–594 (2003).
Zhang, Y., Odeh, I. O. A. & Han, C. Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. Int. J. Appl. Earth Obs. 11, 256–264 (2009).
Varshney, A. Improved NDBI differencing algorithm for built-up regions change detection from remote-sensing data: an automated approach. Remote Sens. Lett. 4, 504–512 (2013).
Xu, H., Huang, S. & Zhang, T. Built-up land mapping capabilities of the ASTER and Landsat ETM+ sensors in coastal areas of southeastern China. Adv. Space Res. 52, 1437–1449 (2013).
Lu, D., Mausel, P., Brondizio, E. & Moran, E. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecol. Manag. 198(1–3), 149–167 (2004).
Rouse, J. W., Hass, R. H., Schell, J. & Deering, D. Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) symposium 1, 309–317 (1973).
Maesk, J. G., Lindsay, F. E. & Goward, S. N. Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations. Int. J. Remote Sens. 21(18), 3473–3486 (2000).
Schneider, A. Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sens. Environ. 124, 689–704 (2012).
Xu, H. Modification of Normalized Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. Int. J. Remote Sens. 27, 3025–3033 (2006).
McFeeters, S. K. The use of normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432 (1996).
Zhou, Y. et al. A new index for mapping built-up and bare land areas from Landsat-8 OLI data. Remote Sens. Lett. 5, 862–871 (2014).
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).
Wilson, M. F. J., O'Connell, B., Brown, C., Guinan, J. C. & Grehan, A. J. Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope. Mar. Geod. 30, 3–35 (2007).
Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45 (2007).
Abrams, M., Bailey, B., Tsu, H. & Hato, M. The ASTER Global DEM. Photogramm. Eng.Remote Sens. 76, 344–348 (2010).
Vapnik, V. Statistical Learning Theory (Wiley, 1998).
Cristianini, N. & Shawe-Taylor, J. An Introduction to Support Vector Machines. Cambridge (Cambridge Univ. Press., 2000).
Stone, M. Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc. 36, 111–147 (1974).
MathSciNet MATH Google Scholar
Kittler, J., Hater, M. & Duin, R. Combining classifiers. Proc.13th International Conference on Pattern Recognition 20, 897–901 (1996).
Yan, R., Liu, Y., Jin, R. & Hauptmann, A. On predicting rare classes with SVM ensembles in scene classification. IEEE International Conference on Acoustics, Speech, and Signal Processing 3, 21–24 (2003).
Chang, F., Chen, C.-J. & Lu, C.-J. A linear-time component-labeling algorithm using contour tracing technique. Comput. Vis. Image Underst. 93, 206–220 (2004).
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Esch, T. et al. Exploiting big earth data from space – first experiences with the timescan processing chain. Big Earth Data 2, 36–55 (2018).
Esch, T. et al. Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge. Int. J. Digit. Earth 11 (2018).
Fomferra, N., Böttcher, M., Zühlke, M., Brockmann, C. & Kwiatkowska, E. Calvalus: Full-mission eo cal/val, processing and exploitation services. Proc. IGARSS 2012, 5278–5281 (2012).
Strano, E., Simini, F., De Nadai, M., Esch, T. & Marconcini, M. Precise Mapping, spatial structure and classification of all the human settlements on Earth. Preprint at, https://arxiv.org/abs/2006.06584 (2020).
Marconcini, M. et al. Outlining where humans live - The World Settlement Footprint 2015. figshare https://doi.org/10.6084/m9.figshare.c.4712852 (2019).
Stehman, S. V. & Czaplewski, R. L. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sens. Environ. 64, 331–344 (1998).
Olofsson, P., Foody, G. M., Stehman, S. V. & Woodcok, C. E. Making better use of ac-curacy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens. Environ. 129, 122–131 (2013).
Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).
Story, M. & Congalton, R. Accuracy assessment: a user's perspective. Photogramm. Eng. Remote Sens. 52, 397–399 (1986).
Stehman, S. V. Impact of sample size allocation when using stratified random sampling to estimate accuracy and area of land-cover change. Remote Sens. Lett. 3, 111–120 (2012).
Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).
Congalton, R. G., Oderwald, R. G. & Mead, R. A. Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogramm. Eng. Remote Sens. 49, 1671–1678 (1983).
Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics 33, 159–74 (1977).
The Authors wish to acknowledge the European Space Agency (ESA), which supported the implementation and processing of the WSF2015 layer through the SAR4Urban and Urban-Thematic Exploitation Platform (U-TEP) projects, respectively.
German Aerospace Center (DLR), Wessling, Germany
Mattia Marconcini, Annekatrin Metz-Marconcini, Soner Üreyen, Daniela Palacios-Lopez, Wiebke Hanke, Felix Bachofer, Julian Zeidler & Thomas Esch
Google Switzerland, Zurich, Switzerland
Noel Gorelick
Google India, Hyderabad, India
Ashwin Kakarla
European Space Agency (ESA), Frascati, Italy
Marc Paganini
MindEarth, Biel/Bienne, Switzerland
Emanuele Strano
Mattia Marconcini
Annekatrin Metz-Marconcini
Soner Üreyen
Daniela Palacios-Lopez
Wiebke Hanke
Felix Bachofer
Julian Zeidler
Thomas Esch
M.M. coordinated the activity. He designed the whole processing system and validation protocol, as well as supervised and personally supported all the technical processing and quality check tasks. A.M-M., T.E. and M.P. provided technical and scientific feedback. U.S. coordinated the data processing with the support of J.Z. A.M-M., D.P.-L., W.H., F.B. and E.S. contributed to the extensive quality check. N.G. supported the S1 data processing. A.K. coordinated the crowdsourcing data collection. M.M. wrote the manuscript, which has been revised by A.M-M.
Correspondence to Mattia Marconcini.
The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
Marconcini, M., Metz-Marconcini, A., Üreyen, S. et al. Outlining where humans live, the World Settlement Footprint 2015. Sci Data 7, 242 (2020). https://doi.org/10.1038/s41597-020-00580-5
Ian McCallum
Christopher Conrad Maximillian Kyba
Steffen Fritz
Nature Communications (2022)
Linda See
Ivelina Georgieva
Scientific Data (2022)
High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation
Siddharth Joshi
Shivika Mittal
James Glynn
The agglomeration and dispersion dichotomy of human settlements on Earth
Filippo Simini
A review of scientific advancements in datasets derived from big data for monitoring the Sustainable Development Goals
Cameron Allen
Hayden Dahmm
Sustainability Science (2021)
Scientific Data (Sci Data) ISSN 2052-4463 (online)


Described as the love child of Ben Folds and Tom Waits, with influences ranging from ethereal British pop, to down home roots.
The Ride is an auditory journey through a rich emotional landscape where the characters wear their hearts on their sleeves as they go through life's twists and turns. It starts with a rush with the upbeat metaphorical catastrophe of "Hurricane." Then by the 5th track it plunges into the depths of despair with the classically influenced "Safe." From there the album continues to explore the ups and downs of the human condition until the final title track "The Ride" rolls in like a roller coaster finishing its run - a reflective song about the journey of life. You'll be drawn into these dark yet hopeful tales by Dobb's unique and distinctive voice and a refreshing piano-driven sound. It's been a long journey to finish this record but it's well worth the wait. It really is "One Hell of a Ride."
"The Ride" by Cameron Dobb is a collection of inspiring adult alternative pop/rock songs with creative arrangements, lots of layers and strong vocals. The musicianship and production are totally professional, including outstanding vocals. Each song is a new experience, keeping the CD interesting throughout. The lyrics are strong and the choruses are definite sing-alongs. Highlights are "Hurricane," which is certainly a song that defines the sound on the CD. "Bee Sting" has a beautiful guitar progression, builds to power and features a string section. "Safe" is harrowing and features some gorgeous piano work. You will thoroughly enjoy this outstanding album if you enjoy bands like Train or Ben Folds.
RadioIndy is proud to present Cameron Dobb a GrIndie Award for their CD "The Ride." A GrIndie Award is RadioIndy's stamp of approval that this CD is an excellent quality CD. Please join us in congratulating this artist on this accomplishment.


The Rigg Design Prize takes place every three years at NGV Australia.
The Rigg Design Prize is a generous legacy of the late Colin Rigg (1895−1982), a former Secretary of the NGV's Felton Bequests' Committee, and is awarded to an Australian contemporary designer displaying outstanding creative achievements in design. The invitational prize was established in 1994 to recognise contemporary design practice in Victoria.
In 2018, for the first time in the award's twenty-year history, the revitalised Rigg Design Prize included ten shortlisted designers from across the country in response to Australia's burgeoning design culture. Award recipients Hecker Guthrie created a custom-made interior inspired by the one modestly unassuming object in our domestic lives that has an invisible gravitational pull, a pull that binds its occupants in space, our practice celebrates the 'table'.
"When deconstructed, the resulting fragments are elementary; a flat surface and one or more supporting structural components. Unintentionally, our domestic lives seem to almost orbit around this one singular object; it's the place we live (eat, drink, play, gather, converse, work, create). In all its forms, the table principally frames all our daily gestures.
As one of the collaborators working with the Hecker Guthrie design team, the studio responded to these ideas with a series of sculpted ceramic forms that act as symbols of the objects encountered in a domestic interior. A range of domestic forms were sculpted from Australian terracotta for the final room, all made to integrate with the raw oak tables made by fellow collaborator Lowe Furniture. Terracotta is a ubiquitous earthenware clay body that is accessed across the globe in interior spaces and the built environment. We encouraged the elemental beauty of the claybody to emerge through a process of simplification and abstraction in the resolution of the forms.
The Rigg Design Prize 2018 is generously supported by the Cicely and Colin Rigg Bequest. The exhibition is free and on display from 12 October 2018 – 24 February 2019 at the Ian Potter Centre: NGV Australia at Federation Square.


My heart is sad for Las Vegas. I have so many crazy happy memories of that place. I was married there! I got my first tattoo in Vegas. Chris and I shared a 99 cent half pound hot dog. We drank a fishbowl sized alcoholic beverage that smoked with dry ice. We played bingo with a bunch of old ladies and had a wonderful time. I'm just sick about the mass shooting that took place there last night and like many of us, I'm shocked and saddened that this is now a common occurrence in this country. We've made zero headway in preventing mass shootings. Shame on us and this government. But that is all I'm going to say about any of it. Instead, I'll turn my focus and maybe your's to something lighter and joyful. Something to take our minds off of tragedy.
Josephine's toys consists of stuffed animals that the Cabbage has won from a crane game, a female park ranger doll (also stolen from the Cabbage) and some small stuffed animals I got as swag from a science conference. She does have a fox and a little tube shaped dog that are actual pet store dog toys, but Josephine prefers the stuffed animals she's stolen from the Cabbage. I feel real bad for the park ranger doll. She's currently laying naked somewhere in the backyard. Josephine was real attached to a blob shaped stuffed creature that was supposed to represent an antibody. Antibody Annie was one of the swag items I brought home from ASCB last year. She had red troll doll like hair that Josephine would groom. Eventually Antibody Annie lost both of her arms and then last week I stepped out into the living room to see her bleeding stuffing out all over the living room floor.
We had a small funeral for Antibody Annie. It was not the first stuffed animal funeral that we've had to have this year. Josephine's lost a few toys. One of those toys, we just blatantly took away from her. The little red tube dog has a squeaker and Josephine would sit just out of reach and just chew on the squeaker. Squeak squeak squeak. She really liked to do this while we were trying to watch something on TV. Michael tried to dismantle the squeaker, but it still squeaks. The odd thing is, she has other toys with squeakers, but that is the only one she does this with. It is not even her go-to toy. She would much rather have you throw her knobby ball that has unpredictable landings. That poor ball is barely hanging on. It got run over by the lawn mower and has a big crack down one side. I tried finding a replacement and the closest thing I found was a spiky ball. She will roll her eyes and go after it if you throw it, but it's not her favorite. This is unfortunate, because knobby ball is not going to last much longer.
I was in Target on Saturday and I remembered Josephine's most recent loss. I thought that maybe it was time I bought her a new toy. Our target didn't have a large selection to choose from, but I did find this really cute alligator. The tag advertised that it squeaked at a level only dogs could hear and I was all "SOLD!" When I got home, I removed the tags and then handed it over to Josephine. She has a tendency to take all of her toys outside and then bring them back in once they've been rain soaked and crusty with dirt. So before I let go, I looked at her and said "let's keep this one inside." She went straight to her dog door with it. I then watched from the kitchen window as she took the alligator on a tour of the back yard. She shook him around just behind the house. Then she walked him over to the back corner where she sometimes watches the cat. Josephine introduced the alligator to the chickens and then she showed him the fire pit before bringing the alligator back inside.
We've named him Alfred and the two of them have been inseparable ever since. Actually, I've never seen her take to a toy as much as she has with Alfred. It's really very sweet.


Are you looking for an experienced and dedicated chiropractor Walsall?
We offer immediate appointments with flexible times that can suit everyone. Our Chiropractors are committed to spending time with you as a patient to help fully understand your symptoms and prevent them from reoccurring in the future.
We consider ourselves a unique practice in an already unique field of healthcare. Our experienced chiropractors offer a wide range of different chiropractic techniques, our diversity means we are able to treat a larger range of patients.
Are you interested in starting your journey to optimum health today?


organicbalancingoil controlsofteningcleansingNEWLY UPDATED FORMULA WITH DRY GRAPE SEED OIL! Stay bal..
Removes regular and waterproof makeup.Removes grime and air pollution.Retains the skin's natural moi..
The foaming cleanser comes in a compact squeeze tube.With a unique, integrated cleansing brush.Revit..
OLDER SKIN BECOMES YOUNGER LOOKING: moisturiser with 24 hour time release is a high grade moisturisi..
Facial cleanser naturally eliminates makeup and impuritiesLeaves face fresh and nourishedNatural bot..
Water Resistant - 80 minFragrance FreeNon-GreasyVitamin EnrichedBroad Spectrum UVA/UVBThis exclusive..
Formulated with mineral-rich lagoon waterProvides skin with lasting hydration and absolute comfortLo..
Features a rich non-greasy textureDeveloped with revolutionary CoIDNA Technology to enhance skin fir..
Formulated with exclusive Genesis Lift ingredient that targets the source of imperfectionsHelps redu..
An advanced rejuvenating face serumDeveloped with exclusive CINETIC TM technology to revive skinForm..
Works perfect for those with Acne Prone SkinExotic ingredients Great for before bedtimeOur Moisturiz..
Carole Franck Pure Clay Mask with Plant Extracts has the following features:Cleanses every type of s..
100% Genuine Product100% Authentic ProductLong lasting FragranceBrand New ItemWe do not sell knockou..
Carrot Glow Exfoliating Purifying Soap is a deep pore cleanser made for oily and acne-prone skin. Th..
Revitalizing and Anti Stress Peef Off Mask with Green Tea and Lavender.Mask for all skin types,espec..
Rose water skin tonicNormalize the PH of the skinDistilled from real rose petalsWith 260 years of ap..
WHY EGF SERUM It's clinically proven to reduce wrinkles below eyes and improve skin elasticity *DEEP..
Cattier Green Clay Mask uses All Natural Ingredients.Particularly recommended for combination to oil..
Effactively removes makeup and skin impurities with Organic Rose and AloeFormulated without alcohol ..
Caudalie Cleansers Gentle Cleanser-13.5 oz.New Look & Improved Formula! This milky cleanser effectiv..
© 2019 pilarprim.com, Inc. All Rights Reserved.


Cubic Zirconia Sterling Silver Clear Diamond 5.4 Carat Clear Diamond Center Stone 10.0 Total Carat Weight 100% permanent non-tarnishing precious metal rhodium protection .925 Sterling Silver 20 18-point diamond cubic zirconia accents This delightful creation offers a brilliant Russian Formula CZ Master Stone of 5.40 carats. The 20 fully diamond faceted accent stones, also our exclusive Russian Formula creation, total 1.98 carats for a total carat weight of 7.28 carats and as comfortable to wear as it is beautiful.
Description: Cubic Zirconia Sterling Silver Clear Diamond 5.4 Carat Clear Diamond Center Stone 10.0 Total Carat Weight 100% permanent non-tarnishing precious metal rhodium protection .925 Sterling Silver 20 18-point diamond cubic zirconia accents This delightful creation offers a brilliant Russian Formula CZ Master Stone of 5.40 carats. The 20 fully diamond faceted accent stones, also our exclusive Russian Formula creation, total 1.98 carats for a total carat weight of 7.28 carats and as comfortable to wear as it is beautiful.


text:
#UK Christmas ad about lonely grandpa has Germans tearing up
BERLIN (AP) — A German supermarket chain's holiday TV ad has prompted floods of tears with its story about an old man whose children never visit, not even at Christmas.
His children finally return home after being told of grandpa's death only to discover that he didn't die after all.
The video from the EDEKA supermarket has racked up almost 20 million views online in five days, with « can't stop crying » among the typical comments.
Elke Schilling says Wednesday the ad addresses a sensitive topic in Germany, where an estimated 8 million people over 60 suffer from loneliness.
Schilling, a retiree who is launching a hotline for lonely older Germans, says using the issue to promote a supermarket was « a bit grotesque » but hopes the chain will support her cause.
Video: https://www.youtube.com/watch?v=V6-0kYhqoRo
from Business Insider http://ift.tt/1O3XBdF

redpajama_set_name: RedPajamaCommonCrawl


text:
News Real Madrid
Royston Drenthe: "At Real Madrid, Robinho turned his basement into a nightclub"
By Colin Millar - 21 May 2019, 14:11
Royston Drenthe has detailed his time at Real Madrid including describing how teammate Robinho "turned his basement into a nightclub."
The Dutchman spent three seasons at the Bernabeu after joining from Feyenoord in 2007 but surprisingly quit football altogether in 2016, aged just 29.
Diario AS cite an interview the former winger conducted with FourFourTwo in which he spoke of his decision to join Madrid.
"I heard that 16 clubs were interested in me, I spoke with the president of Barcelona, (Joan) Laporta, and with (Pedja) Mijatovic, the sports director of Real Madrid.
"It felt as if he had met almost all the top managers. Of all the clubs, most of them offered me a five-year contract with more or less the same conditions, and as soon as I learned that Real Madrid was interested, I decided to go there.
"When I was a kid, I was fascinated by everything that surrounded Madrid, and the glamour surrounding the club, like when I saw the movie Goal, it was something I really wanted to be a part of.
"Immediately I felt at home, everyone gave me a warm welcome, Guti was great with me immediately, and he was a 'man' of Madrid, a legend.
"I developed a good relationship with Robinho shortly after I sometimes went to his house, where he had turned his basement into a nightclub."
Colin Millar
Tags Royston Drenthe
Ex-Real Madrid star returns to Spanish football
Former Real Madrid star declared bankrupt
Drenthe: I quit because I was unhappy


The Lord spake this parable: "There was a certain householder, which planted a vineyard, and hedged it round about, and digged a winepress in it, and built a tower, and let it out to husbandmen, and went into a far country: and when the time of the fruit drew near, he sent his servants to the husbandmen, that they might receive the fruits of it. And the husbandmen took his servants, and beat one, and killed another, and stoned another. Again, he sent other servants more than the first: and they did unto them likewise. But last of all he sent them his son, saying, 'They will reverence my son.' But when the husbandmen saw the son, they said among themselves, 'This is the heir; come, let us kill him, and let us seize on his inheritance.' And they caught him, and cast him out of the vineyard, and slew him. When the lord therefore of the vineyard cometh, which will he do unto those husbandmen?" They say unto Him, "He will miserably destroy those wicked men, and will let out his vineyard unto other husbandmen, which shall render him the fruits in their season." Jesus saith unto them, "Did ye never read in the Scriptures, 'The Stone which the builders rejected, the same is become the head of the corner: this is the Lord's doing, and it is marvellous in our eyes'?" (St. Matthew 21:23-42).
Thus, in the parable of the prodigal son, who squandered the father's inheritance, the Lord shows God's love for sinful man; in the parable of the Good Samaritan, He shows who our neighbor is and what kind of love we should have for him. The Gospel narrative of this Sunday offers us the parable of the hired vineyard, which showed audacity and ingratitude to its master (Matthew 21:33-42).
A certain householder planted a vineyard and hedged it round about, and digged a winepress in it, and built a tower, and let it out to husbandmen, and went into a far country. He fully entrusted them with his property in the hope that they, working it, would gather the harvest and give it to him. But the husbandmen acted differently.
When the time of the fruit drew near, he sent his servants to the husbandmen, that they might receive the fruits of it.
In our life there are unusual events: deliverance from certain death, the resolution of difficult situations in life, success in business, and much else that demonstrates God's providence for us. Sometimes one becomes a witness of a true miracle, of healing from a mortal disease. In all these situations we in fact experience the closeness of Christ. But how do we perceive Him?
It would seem that what we have experienced should prompt us to feats of love. But it is astonishing how quickly man calms down and forgets the help that the Lord has given him, continuing to live as the sovereign "master" of his own life. How quickly, and with what astonishing nonchalance, do we return to the same vices that had nearly ruined our body and soul! We live our life from day to day, leaving no place for Christ in our heart.
When the lord therefore of the vineyard cometh, which will he do unto those husbandmen?" this question is addressed to all the descendents of Adam.
In the parable of the husbandmen, Christ tells of His own voluntary death on the Cross for us: For God so loved the world, that He gave His only-begotten Son, that whosoever believeth in Him should not perish, but have everlasting life (John 3:16). This Sunday is special, for it precedes the Feast of the Elevation of the Lord's Cross. Our unrighteousness and unwillingness to bear our cross makes us look like these husbandmen, about which the parable speaks. Having been baptized, one should no longer live for himself, but rather for the meaning of Christianity: For whosoever will save his life shall lose it: but whosoever will lose his life for my sake, the same shall save it. For what is a man advantaged, if he gain the whole world, and lose himself, or be cast away (Luke 9:24-25). The Cross is the symbol of victory over sin and the overcoming of self-love.
The Christian's way of salvation is the narrow path of bearing one's cross on Golgotha, which leads to eternal life with Christ: If any man will come after Me, let him deny himself, and take up his cross daily, and follow Me (Luke 9:23).


text:
Mission Plantation : 4 CRORE – MyGov Maharashtra
July 1, 2016 was an important day in the history of Maharashtra. On this day, the State not only unleashed a mission, but also got an entry in the Limca Book of World records. The day saw a momentous campaign led by the people of Maharashtra of planting trees on a huge scale. Setting a huge record, the people planted 2.82 crore trees on a single day, giving hope to challenges like degradation of natural resources and habitats, pollution and climate change and boosting the morale of Maharashtra's Forest Department.
For the Government of Maharashtra, the record was the first milestone in the path of State's mission of 33 per cent green cover. The resolve for '2 crore Tree Plantation' aimed by the State Government last year was made possible with the great support of people, NGOs, spiritual institutions, farmers and school/college students, who came together to plant around 2.82 crore trees in a day.
In order to increase the green cover of Maharashtra further, it is essential for citizens to come together, plant trees and make collective efforts for their survival. At present, the forest area of Maharashtra stands at 20 per cent of its total area. For this to reach 33 per cent, it is essential to plan and run a perfect tree plantation programme. Irrespective of whether these saplings would be planted on forest lands, boundaries of agriculture fields, campuses of schools and colleges, lands belonging to railways and State Transport Corporation, hillocks as well as areas under the Ministry of Defence and National Highways, the plantation drive will be as eventful as last year, assures the State Government.
A memorandum of understanding (MoU) has been signed between the Ministry of Railways and State Forest Department on planting trees on the railway land in Maharashtra. The process of establishing an Eco Battalion has been accelerated. The citizens can become a member of the Green Army through the website, http://www.greenarmy.mahaforest.gov.in.
The department has started a helpline (1926) to encourage people to share information about forests and wildlife and incidents of forest fire and illegal cutting of trees, besides registering other complaints. The forest area of Marathwada is just 4.5 per cent of its geographical area. The forest area of a district like Latur is even meagre at 0.76 per cent. Due to changes in climate globally, the State has to face calamities such as droughts, floods and hailstorms on and off. Last year, Latur city had to be supplied with water through the Jaldoot Express (water express trains). As a solution to the persisting problem, the State must widen its forest area and accelerate the concept of Vanyukta Shivar Abhiyan. Under the leadership of Minister for Forests Sudhir Mungantiwar, the Forest Department is eminently striding ahead towards Green Maharashtra. The mega tree plantation event last year was specially admired by Prime Minister Narendra Modi in his 'Mann Ki Baat'. Eminent film actor Amitabh Bachchan has also appealed to people for participation in the tree plantation campaign.
"For planting 4 crore trees, drive should become a mass movement" or the success of Maharashtra's plantation drive, the number of hands planting trees should be increased and we must build an army of tree lovers," said Minister of Finance, Planning and Forests Sudhir Mungantiwar, addressing a review meeting of afforestation drive in Pune Revenue Division. He said, for afforestation, awareness should be created and scientific planning must be done. The Forests Minister added that cooperation of people is the need of the hour. The target for Maharashtra's plantation drive next year is to plant 13 crore trees. "Bamboo is a kalpavriksha (wishfulfilling tree). We should explain qualities of other trees as well to people. Along with plantation, nurturing trees is also important. We have resolved to increase State's forest cover to 33 per cent," he said.
Underlining the fact that the protection of environment is everyone's responsibility, he encouraged people to come together to accomplish objectives like the tree plantation drive. Afforestation should not remain the responsibility of the government only, it should become a mass movement, as global warming is matter of concern for every living being on the planet. "Protection of environment is also a matter of happiness. We all must participate in the drive, plant trees and spread prosperity and happiness all around," he said.
A planned initiative A programme can only be successful with excellent and complete planning. This year too, the State Forest Department has planned the 4 crore tree plantation programme with precision. A 33-point programme has been chalked out by the department. Various works are planting trees on both sides of the road, greening of hillocks, plantation on the sides of canals and campuses, making available manpower, finalisation of the locations for tree plantation, digging, making available quality saplings and others. A crash programme for making the saplings ready has been taken up on the basis of 'Nursery in every village'. Saplings have been provided a number through a computer software and accounts are being maintained for plant species, production and sales. Tree Plantation Committees have been set up at State, district, taluka and village levels. The volunteers and coordination officers at these have been decided. To receive information on every decision at every level, real time reporting arrangements have been made through computer software under the supervision of Additional Chief Conservator of Forests (Information Technology). Every step of planning this year's tree plantation and the work carried out so far is being uploaded on the website of Forest Department. Whenever a work is carried out with dedication, it brings success for sure. Last year, the campaign was implemented with the concept of 'Only goal: 2 crore tree plantation,' and the same was eminently successful with immense participation of people. This year, the department is working on 'Only goal: 4 crore tree plantation'. With the onset of monsoon in sight, all the works connected to tree plantation have been going with full steam. Now it is necessary to be fully prepared for the period of seven days from July 1 to 7 to carry out the plantation of 4 crore trees. We also have the duty to keep these plants alive.
–Dr. Surekha M. Mulay, Departmental Liasioning Officer
Last Updated:July 2, 2017 at 11:37 am -blog-166


text:
Home » Net Worth » Business » Designers » Anna Delvey Net Worth 2022: Biography Career Income News
Anna Delvey Net Worth 2022: Biography Career Income News
Updated on: August 9, 2022 by Nora
Name: Anna Delvey
Salary: $6 Million +
Monthly Income: $0.5 Million +
Date of Birth: January 23, 1991
Profession: Model, investor, interior designer
Nationality: Russian
1. Anna Delvey Net Worth
2. Anna Delvey Assets
3. Anna Delvey Net Worth Growth
4. Anna Delvey Biography
5. Anna Delvey: Career and Awards
Anna Delvey Net Worth, Biography, Age, Wife, Height, Weight, income, and many more details can be checked from this page. (Original Name is Anna Sorokin) Anna Delvey is a Model, interior designer who has a Net Worth of $25 Million. Anna Delvey is a German woman. She started her career as a Model getting quite a decent success and popularity. Being successful in modeling, she earned a healthy amount of wealth as well. Later, Anna Delvey moved to the Real estate business, and she became a very successful interior designer.
Anna managed to make herself a massive wealth and prosperity. Although, things became a bit difficult for her when she was accused of fraud. Anna Delvey deceived everyone as a very rich German socialite whose intentions had always been to have more. In her search for more and desire to achieve something huge, she landed herself in very big trouble.
Anna caught the eye of police and Investigating agency when she shifted to Paris from New York. She was caught in possession of heroin, later getting accused of fraud and being sentenced. Also check Tessa Thompson Net Worth.
Anna Delvey Net Worth
Between 2013 to 2017, Anna Sorokin (Known as Anna Delvey) duped dozens of friends and companies into giving her money to finance her extravagant lifestyle by falsely stating that she could pay them back with a net worth of more than $65 million offshore. The German national often told people that she was an heiress to her father's oil empire. In reality, her father was a working-class truck driver.
Anna Delvey is a very wealthy person who has always been in the spotlight because of her extremely extravagant lifestyle and her unreal spending. Although the moment she got caught by police for fraud and Felony charges, most of her wealth got seized.
Currently, she can barely use any of her wealth, which is a shame for women that are rich. Anna Delvey has fooled many personals over the course of time, and her actual wealth is not known. Though, there is an estimated amount equivalent to $25 million. You may also like Ali Wong Net Worth.
Name Anna Sorokin
Profession Model, interior designer
Monthly Income And Salary $120,000 +
Yearly Income And Salary $1.8 Million +
Anna Delvey Net Worth Summary
Anna Delvey Biography
Anna Delvey Assets
Home: Anna Delvey used to be a very rich and wealthy personality of Germany, who started her work in America. Although, the recent charges against her of fraud and felony landed her in jail and seized all her properties and money. Anna still has the ownership of her properties. She owns a house in New York, Los Angeles, Beverly Hills, etc.
Car collection: Anna Delvey has a very beautiful car collection, which includes some of the best and most luxurious ones. Delvey owns a Range Rover, Mercedes, Audi, and a few more. You may alo check Cynthia Bailey Net Worth.
Anna Delvey Net Worth Growth
Net Worth in 2022 $25 Million
Anna Delvey is a false name of Anna Sorokin, which she has used very successfully to deceive a massive crowd and do fraud. Anna was born in Domodedovo, Russian SFSR, Soviet Union, on 23 January 1991. She was raised in Russia for most of her childhood when she used to live with her father, who was a truck driver, and her mother owned a small convenience store. She shifted to Germany at the age of 16 and started taking German classes, and English classes are well.
She was also very fond of modeling from a very young age, and she used to follow vogue and various other magazines. She went to London to study further, though she dropped out very soon. Later, she moved to Paris and started her modeling career with the name Anna Delvey. Also read Suzanne Somers Net Worth.
Real Name/Full Name Anna Sorokin
Nick Name/Celebrated Name: Anna Delvey
Birth Place: Domodedovo, Russian SFSR, Soviet union
Date Of Birth/Birthday: 23 January 1991
In Feet and Inches – 5'5"
Parents Name: Father – Vladim Sorokin
Mother – N/A
Siblings: N/A
School: Episcopal School
College: Central Saint Martin's
Social media Accounts: Instagram
Boyfriend: N/A
Husband/Spouse Name: No
Kids/Children Name: No
Anna Delvey: Career and Awards
Anna Delvey started her career as a model who used to live in Paris. She used to take part in several fashion weeks and ceremonies in 2013; when she came to New York from Paris, she found working in New York much easier and less expensive.
She was working with Purple, and she transferred her office to New York. Later, Anna quit the company and came up with the idea of the Anna Delvey Foundation. She then pretended to be a very rich German in front of everyone.
In 2017, Anna was finally arrested for fraud and felony. She used fake accounts and other stuff to cheat many banks, hotels, etc. She was sentenced to at least 4 to 12 years in prison. Currently, she is in New York, and all her details are seized. In 2022, Netflix released a series based on her story named Inventing Anna.
Anna Delvey started her early education at Episcopal School, which was a catholic school located in Germany. She later went to London to pursue further education at Central Saint Martin's. Although, she dropped in the first year. She later moved to Paris and started working. Check this Rene Russo Net Worth.
Anna Delvey, or Anna Sorokin, is a fraud and very mean woman, as described by her friends and those who used to know her. She has always lived in a masquerade, hiding behind the name of a rich German, and pretending to be a millionaire.
She committed some serious crimes and is currently in custody and will going to remain for a long time. Most of her assets and accounts are seized. Netflix used the opportunity to make a series of her story, making her popular.
What is the net worth of Anna Delvey?
Anna Delvey's total net worth is around $25 Million.
What is the age of Anna Delvey?
Currently, Anna Delvey is 31 years old (23 January 1991).
What is the Salary of Anna Delvey?
Anna Delvey earns an estimated salary of $6 Million + Per Year.
What is the Height of Anna Delvey?
The Height of Anna Delvey is 1.65 M.
What is the name of Anna Delvey's husband?
Anna Delvey is currently single and unmarried.
Nina Dobrev Net Worth
Teresa Giudice Net Worth
Keke Wyatt Net Worth
Macy Gray Net Worth
Christina Haack Net Worth
Ash Kash Net Worth
Categories Designers Business Celebrity Models Net Worth Women


text:
Kathryn Graber Named 2019 James Wischer 'Child Advocate of the Year' by Manatee Children's Services
Graber has had a long career in child advocacy.
Kathryn Graber
Image: Courtesy Photo
Kathryn Graber has been named the 2019 James Wischer "Child Advocate of the Year" by Manatee Children's Services. This is the third annual award, which also coincides with Child Abuse Prevention Month.
To be chosen, the recipient is required to perform dedicated service for a minimum of two years; make a difference in the lives of children; and demonstrate a positive impact on the community.
Graber has a long career in child advocacy, working works closely with Child Protection Services as a behavioral health consultant with Centerstone.
hello nurse
Local Nurse Wins Prestigious International Award
01/23/2023 By Isaac Eger
Megan Howell Gives Unhoused People With Mental Illness a Chance to Get Back on Their Feet
01/09/2023 By Laura Paquette
A Sarasota Magazine Unity Award Winner and Editor Discuss the Battle for the Right to Vote
Hoppily Ever After
99 Bottles Has Received National Recognition for Its Stellar Beer Selection
01/11/2023 By Lauren Jackson


SILVER SPRING, Maryland — Officials at the U.S. Food and Drug Administration say you might want to put down your teething necklace and bracelet for good.
This comes after the FDA released reports of death and serious injuries to infants and children. One report mentioned a 7-month old who choked on the beads of a wooden teething bracelet while under supervision. Another involved an 18-month-old child who strangled to death by his amber teething necklace during a nap.
This warning goes for young children who use the jewelry for teething pain as well as children with autism who use it for sensory stimulation, the FDA said.
In addition to choking and strangulation, the FDA said the jewelry could increase risk of infection if a piece of it irritates or pierces the child's gums. Amber necklaces also contain a substance called succinic acid, which manufacturers say helps relieve pain. The FDA has not tested these claims.
The FDA continues to recommend avoiding teething creams, benzocaine gels, sprays and other products for pain relief as they can create deadly conditions.


text:
Outgoing New York Rep. Kathleen Rice says she warned Democratic leaders that the party would 'lose' Long Island in the midterms
Rep. Kathleen Rice of New York.
AP Photo/J. Scott Applewhite, File
Rep. Kathleen Rice said she warned Democratic leaders of electoral losses on Long Island before the midterms.
During a Politico interview, Rice said leaders didn't heed her call and pointed to Biden's win in her district.
Rep. Lee Zeldin produced strong coattails for down-ballot GOP candidates in his gubernatorial bid.
For much of the 20th Century, Long Island was a well-known Republican stronghold, serving as a counterbalance to the strongly Democratic politics of New York City.
From the 1990s through much of the past decade, Democrats made major gains in Nassau and Suffolk Counties, winning congressional districts and local offices that for years had favored Republican politicians.
But in recent years, Republicans have clawed back some of their strength, winning the county executive and district attorney races in Nassau in November 2021, while also winning the district attorney race in Suffolk that year. While former President Donald Trump lost New York State resoundingly in both 2016 and 2020, he won Suffolk each time.
In the November midterms, despite Democratic overperformance in suburbs across the country, Republicans had a major year in New York State, winning every congressional district anchored on Long Island — despite Democrats holding a party registration edge in both Nassau and Suffolk and controlling districts in the Hudson Valley and Upstate.
And outgoing four-term Democratic Rep. Kathleen Rice, who will be succeeded by GOP Rep.-elect Anthony D'Esposito next week, said in a recent Politico interview that she sounded the alarm to party leaders regarding her intuition about the state of play on Long Island ahead of the midterms.
Rice, a moderate who represents the Long Island-based 4th Congressional District and previously served as the Nassau County district attorney for nearly a decade, told the news outlet that her warnings about the dire prospects of Democrats in the suburbs of New York City went unheeded.
"This is a blue, blue state, and it's the reason why we lost the majority," Rice said. "We lost four House seats. I said [to party leaders], 'You guys, don't understand, we're gonna lose Long Island. And we're gonna lose some other seats, further up.' They said, 'No, no, no, your seat was a Biden +13.'"
"When we had elections in November of 2021, every single Democrat on Long Island lost. Not because they weren't popular and not because people didn't like them. But they wanted to send a message to Washington," she added.
While President Joe Biden easily won Rice's district in 2020, his standing among many Independents has taken a hit since then, despite the party holding its own in the midterms. Faced with the prospect of electing a swath of GOP candidates who rejected bipartisanship and continued to deny Biden's 2020 win, voters backed Democratic candidates in such dramatic fashion that the GOP severely underperformed nationally.
In November, the GOP gubernatorial nominee was Lee Zeldin, a retiring Long Island congressman who lost a competitive race against Democratic Gov. Kathy Hochul. Zeldin whittled down the normally-massive Democratic edge statewide as his coattails helped elect D'Esposito, along with GOP Reps.-elect Nick LaLota and George Santos, to Long Island-anchored districts. (Santos in recent days has come under fire for a growing list of false claims about his background after a New York Times investigation pointed out inconsistencies in his work history and finances.)
In a state that voted for Biden by a whopping 23 points (61%-38%) in 2020, GOP victories in New York State — fueled by a focus on economic and public safety issues — delivered the party a razor-thin US House majority.
Democrats, who still maintain control of the Governor's Mansion and the state legislature, along with holding both US Senate seats, remain the dominating force in state politics.
However, the midterms proved that while the Republican brand on Long Island may have faltered in the 1990s and 2000s, that is certainly not the case today.
← Минобороны РФ заявило об окончании осеннего призыва → 23 totally accurate and sane Jewish pop culture predictions for 2023
Минкультуры Кыргызстана потребовало от издания Kloop удалить статью, где упоминается глава пресс-службы президента. Редакция отказалась February 2, 2023
Home » Audio Sources - Full Text Articles » Outgoing New York Rep. Kathleen Rice says she warned Democratic leaders that the party would 'lose' Long Island in the midterms