Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling
- In the first step, we investigate if the WSF3D dataset can be used to effectively identify and eliminate large industrial/commercial areas from the built-up environment, which in the past have been reported as major sources of under/overestimation errors in population modelling. To this end, we present a methodology that combines a Random Forest algorithm with a set of spatial metrics derived solely from the WSF3D dataset to predict the “Industrial” versus “Non-Industrial” class of built-up settlements across 38 countries located in Europe. Reference datasets to collect training data and validate our classification results are produced using the Urban Atlas 2018 dataset. Overall, the main objectives of this part of the research are to build an automatic classification model for each country, and to produce binary classification maps that can be used to refine population distribution datasets.
- In the second step, we evaluate the accuracy of population distribution maps produced on the basis of the new WSF3D data and the integration of information on industrial/non-industrial land use from step 1. For this assessment, we specifically employ the information of the WSF3D building fraction (BF) and building volume (BV) layers, downscaled to 12 m (see Section 2.2), to generate population distribution maps using a weighted dasymetric mapping approach together with 2020 census-derived population data. We then compared the outcomes to the results achieved with a binary settlement mask as input. Overall, the main objectives of this part of the research are to investigate if improvements can be gained from the inclusion of settlement information related to the use and/or volume of building structures, and to assess under which circumstances these improvements are more significative, and how they correlate with the quality of our classification maps.
2. Materials and Methods
2.1. Study Area
2.2. World Settlement Footprint 3D Dataset
- Building Height (BH): The 12 m BH layer represents a spatial disaggregation of the standard 90 m WSF3D BH layer, which was derived by measuring the height variations of vertical edges related to building edges (BE) in the 12 m TDX-DEM within the settlement areas defined by the WSF-Imp layer. The height is reported in meters (m) in the final product.
- Building Fraction (BF): This layer was produced by quantifying the built-up coverage at 12 m derived from the joint analysis of the WSF-Imp, TDX-AMP and BE. The values in the final product range from 0–100, measured in percentage.
- Building Area (BA): This layer was derived by multiplying the BF times the area of each ~12 m grid cell (~144 m2 at the equator). The area is reported in square meters (m2) in the final product.
- Building Volume (BV): This layer was derived by multiplying the BH with the area of the 12 m pixels. The total volume is expressed in cubic meters (m3) in the final product.
- Building Mask (BM): This layer represents the binary version of the BF layer, where all pixels PIS > 0 have been converted to values of 1.
2.3. European Urban Atlas Datazset
2.4. Population Data and Administrative Boundaries for 2020
2.5. Industrial and Non-Industrial Classification of Built-Up Settlements Using Random Forest
2.5.1. Derivation of Spatial Metrics
2.5.2. Interim and Reference Datasets
2.5.3. Automatic Training Data Collection
2.5.4. Model Training
2.5.5. Quantitative Accuracy Assessment
2.6. Population Modelling and Comparative Analyses
2.6.1. Top-Down Dasymetric Modelling
2.6.2. Quantitative Accuracy Assessment
- Firstly, for each country we calculated the proportion of industrial areas found within the L1 units according to the final binary classification maps. For all L1 units with the same amount of industrial presence, we then calculated the average REE produced by each gridded population map.
- Secondly, similar to , we grouped the L1 units into REE ranges of 25% according to the results produced by each gridded population map. For each country, we then calculated the percentage of each countries’ total population found in these units.
3.1. Industrial and Non-Industrial Binary Classification Maps
3.2. Population Modelling: Output Gridded Population Maps
3.3. Population Modelling: Quantitative Comparative Analyses
4.1. Industrial and Non-Industrial Classification of Built-Up Settlements Using Random Forest
- WSF3D: The quality and accuracy of the WSF3D in terms of settlement detection (building mask-BM) and the final derived spatial metrics, played a fundamental role in the final accuracies reported in this research. A thorough inspection of the classified maps revealed that in areas identified as “industrial” by the reference datasets, many pixels representing actual green areas or parking lots were detected as built-up in the BM layer. Considering their low spatial metrics, the FM-RF then predicted these pixels as “non-industrial” leading to errors of omission in the industrial class, and errors of commission in the non-industrial class as summarized in Figure 10. In this context, from the average 25% errors of omission reported at the Pan-European level for the industrial class (100–75%, PA2 = 25%), it was found that approximately 15% of the errors came from confusing class 2 for class 1.1, and 10% for class 1.2 (see Table 3) during the prediction process. While the classification of these pixels as “non-industrial” could be in reality “thematically correct”, for the purpose of population modelling the presence of these pixels are detrimental, as population counts are allocated within these areas. Therefore, to potentially reduce the misclassification caused by the false detection of settlement pixels, future research should explore improving the final BM layer by integrating thresholds in the BF layer (Section 2.2). Similarly, the integration of additional post-classification steps should also be considered, such as employing a broader number of window sizes for the extraction of spatial metrics as carried out in , or by reclassifying the pixels according to their RF-class probability as carried out in .
- Automatic training data collection: Unlike some local-scale research where a manual collection of training samples allows for a visual qualitative assessment of the training data , in this research we relied on an automatic procedure that did not include performing any sort of quality control over the training datasets. In correlation with our previous point, in a few cases, this lack of assessment resulted in poorly heterogenous training samples among classes, which without a doubt lead to some misclassification errors. For example, by evaluating the training data of a FUA in Ireland that shows large differences between classes (see Figure 9), it was possible to observe that the pixels values for class 1.1 “High-dense residential” and class 2: “Industrial” were similar across the bands corresponding to the BA, BF, BH and BV as represented in Figure 16. Within the selected FUA, this homogeneity led to errors of omission in the “non-industrial” class close to 20%, which meant that many pixels were erroneously classified as “industrial”. In this framework, while the errors of omission in the non-industrial class at the Pan-European scale are considered low (Figure 10), these misclassification errors had repercussions on the final population datasets, as seen by the results of Figure 14. Therefore, while a manual collection of training samples within the extent of our study area would have translated into a time-consuming task, further research should focus on the implementation of automatic intra-class separability analyses like the one presented in , with the objective to produce more significative training datasets.
- Equal number of training samples per FUA, per class: With the aim of producing robust comparative analyses within a country, in this research, an equal sample size was kept among all FUAs so that the I-RF and E-RF models were trained under similar conditions. In this framework, while the less represented class for some FUAs would reach close to 30% of the total available built-up pixels, in many cases less than 5% of the available pixels per class were used for training. This under-representation, coupled with the limitation mentioned on our previous point, affected the classification accuracy, especially in areas with a high inter-class diversity. In light of this, to improve the classification accuracy, future research should consider the inclusion of additional re-sampling steps. Here, post-classification approaches such as the one presented in  could become beneficial, where resampling is carried out using the RF class probabilities to concentrate in areas with high model-uncertainty.
4.2. Population Modelling
- Weighted approaches perform better than binary approaches: As already demonstrated in many other studies [13,14,43,44], weighted dasymetric approaches produce higher accuracies than binary dasymetric approaches. First of all, as observed in Figure 11, from a qualitative point of view, the output population maps produced with the “density” layers (BF, BV and BV-IS) show a higher spatial correlation with the underlying rural-urban gradient in comparison to those produced with the binary layer (BM). The results of the quantitative assessment, further confirm that the spatial representations of the population distribution are not only more “realistic”, but also more accurate, as all “density” layers consistently reported better aggregated statistics (%MAE, MAE and RMSE values) compared with the binary layer. On this note, however, it is worth noticing, that at level of validation units (REE), the BM proxy layer is capable to outperform the results of the “density” layers, especially in areas with a large share of industrial areas (see Figure 14). This makes sense if one considers that the “density” layers (BF and BV) amplify the errors of overestimation in these areas, by erroneously allocating more population due to their weighting value.
- Building volume and settlement use information improve population estimates: Comparable to the conclusions reached in local- and national-scale studies [31,48,50], the inclusion of volume and settlement use information derived from the WSF3D dataset produced by far the best estimation accuracies across the majority of the countries. First, according to the results presented in Table 4 and Figure 12, the BV-IS proxy layer produced improvements over the BM, BF and BV proxy layers that reached %MAE values up to 30%. These were more frequently present in countries with “High” industrial coverage, where large errors were remarkably reduced as observed from Figure 14. Second, as observed in Figure 13, the BV-IS proxy layer remarkably reduced the differences between the MAE and RMSE metrics. This was, once again, correlated to the fact that large errors of overestimations were drastically reduced by the proxy layer, especially in areas with a high share of industrial land cover (Figure 14). In this context, the inclusion of settlement use information played a major role, as it allowed the BV-IS proxy layer to produce systematically more stable results across all countries, and across all validation units, while REE errors remained between −50% and 100% with the BV-IS layer for most countries, the BM, BF and BV proxy layers produced variable results that reached overestimations in the rage of 500% or even higher (close to 4500% for EST). Accordingly, at the Pan-European scale, the BV-IS proxy layer estimated a larger proportion of the total population with errors in ±25% in comparison to BM add BF proxy layer, which according to pre-established rankings of accuracy , can be considered as “accurately” estimated.
- The input and validation units influence accuracy results: It is important to note that the maps evaluated in this research have been produced with the coarser administrative population units for each country (national scale). In some countries, where the BV-IS did not report the same systematic improvements compared to the other proxy layer, this characteristic might have influenced the accuracy results. For example, in the case of ALB, EST and TUR (Figure 14), many validation units that reported high coverage in industrial areas still reported large errors of overestimation with the BV-IS layer. This is, because even when industrial areas were successfully identified, the very low population counts of the validation units (sometimes less than 100) were difficult to match from a national-scale disaggregation. In this context, it can be expected for maps produced with the highest level of administrative units to be more accurate than the maps presented here. This assumption is supported by the large amount of research that has already proven that population maps produced with the finest administrative units produces the most accurate population maps [14,31,48,80]. However, considering that this only affected a few countries, also indicates that BV-IS proxy layer is capable to produce more accurate population maps than the rest of the proxy layers, when high-resolution input population data is not available.
- The relative effectiveness of the BV-IS proxy layer is heavily dependent on the quality of the classified maps: While the BV-IS produced consistently great improvement of the BM and BF proxy layers, in a few cases the performance of the proxy layer was improved by the BM and BV proxy layers, respectively. For example, as observed from Figure 14, in the majority countries the BV proxy layer produced better results than the BV-IS layer in validation units with industrial share below 20% (or 80% non-industrial). In these units, errors of were ~50% higher (mostly overestimations) with the BV-IS layer, which suggest that a number of non-industrial built-up settlements pixels were erroneously removed, causing an allocation of a larger population in the remaining pixels. In this context, as expressed in the previous section, these results correlate to the difficulties of accurately classifying built-up settlements pixels in complex urbans settings, where high-rise buildings are mixed with industrial areas. Here, improvements in the classification process should then reflect in improvements in the population distribution.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Country Name||ISO||No. FUAs||Country Name||ISO||No. FUAs||Country Name||ISO||No. FUAs||Country Name||ISO||No. FUAs|
|Bos. and Her||BIH||5||Un. King||GBR||40||Macedonia||MKD||4||Slovenia||SVN||2|
|ISO Code||Census Year||UN2020 Estimation||L1-unit/Count||ISO Code||Census Year||UN2020 Estimation||L1-Unit/Count|
|Class||Major Classes||Urban Atlas Codes||Sub-Classes|
|1||Non-industrial||111: 11100||1.1 High-dense residential|
|112: 11210, 11220, 11230, 11240|
121: 12100 (area < 10 km2)
122: 12210, 12220, 122230
Level 1: 2, 3, 4, 5
|1.2 Low-dense residential + Small non-residential|
|2||Industrial||121: 12100 (area >= 10 km2)|
122: 12300, 12400
|ISO||Av. Pop||BM||BF||BV||BV-IS||Ind. Level||ISO||Av. Pop||BM||BF||BV||BV-IS||Ind. Level|
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Palacios-Lopez, D.; Esch, T.; MacManus, K.; Marconcini, M.; Sorichetta, A.; Yetman, G.; Zeidler, J.; Dech, S.; Tatem, A.J.; Reinartz, P. Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling. Remote Sens. 2022, 14, 325. https://doi.org/10.3390/rs14020325
Palacios-Lopez D, Esch T, MacManus K, Marconcini M, Sorichetta A, Yetman G, Zeidler J, Dech S, Tatem AJ, Reinartz P. Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling. Remote Sensing. 2022; 14(2):325. https://doi.org/10.3390/rs14020325Chicago/Turabian Style
Palacios-Lopez, Daniela, Thomas Esch, Kytt MacManus, Mattia Marconcini, Alessandro Sorichetta, Greg Yetman, Julian Zeidler, Stefan Dech, Andrew J. Tatem, and Peter Reinartz. 2022. "Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling" Remote Sensing 14, no. 2: 325. https://doi.org/10.3390/rs14020325