From Pixels to Planning: Large-scale Mapping of Urban Morphology and Population Distribution with the World Settlement Footprint 3D

Urban morphology and human population distribution are two interrelated aspects of our urbanization that play a critical role in shaping the sustainability, resilience and liveability of cities. In recent years, the advent of global datasets with 3D information derived from Earth Observation (EO) technologies has revolutionised our ability to study and analyse these two aspects of urbanisation, providing information that is essential for designing cities that can accommodate the needs of their residents while minimizing their environmental impact. One such dataset is the novel World Settlement Footprint 3D (WSF3D) produced by the German Aerospace Center (DLR). The WSF3D was the first global dataset providing detailed information of the fraction, area, average height and total volume of buildings, at unprecedented spatial resolution, coverage and consistency. Since its development, researchers from different organizations (e.g. WorldBank, United Nations, WorldPop) have employed the dataset as input data for large-scale studies in urban morphology and population distribution, with a level of detail that was previously impossible. In this paper we present a selection of WSF3D-driven applications with the objective of demonstrating how the new data can be used to support urban planning and management. First, the WSF3D has been employed to demonstrate how the four layers of the dataset can be used to determine a building's functional use, and how this information can be leveraged to improve large-scale models of population distribution at large-scale. Thereafter, the WSF3D has been used to determine the relationships among building height/volume, population density and income, which can provide insights into the efficient use of space (e.g. crowding vs layering) on the one hand, and shed light into infrastructure disparities and variations, on the other. With that being said, due to the global nature of the WSF3D dataset, the previous analyses were conducted from local to regional scales, which can also help identify opportunities for interventions that can be replicated across different locations. Overall, with the results of this research, the authors aim to provide planners and policy-makers with valuable insights into usability of the globally available WSF3D dataset. By demonstrating its potential as reliable and robust input data, this study seeks not only to empower evidence-based decision-making, but also to advocate for the widespread adoption of geospatial layers in the implementation of strategies towards sustainable development strategies of the built environment

From Pixels to Planning: Large-scale Mapping of Urban Morphology and Population Distribution with the World Settlement Footprint 3D

Urban morphology and human population distribution are two interrelated aspects of our urbanization that play a critical role in shaping the sustainability, resilience and liveability of cities. In recent years, the advent of global datasets with 3D information derived from Earth Observation (EO) technologies has revolutionised our ability to study and analyse these two aspects of urbanisation, providing information that iS essential for designing cities that can accommodate the needs of their residents while minimizing their environmental impact. One such dataset is the novel World Settlement Footprint 3D (WSF3D) produced by the German Aerospace Center (DLR). The WSF3D was the first global dataset providing detailed information of the fraction, area, average height and total volume of buildings, at unprecedented spatial resolution, coverage and consistency. Since its development, researchers from different organizations (e.g. WorldBank, United Nations, WorldPop) have employed the dataset as input data for large-scale studies in urban morphology and population distribution, with a level of detail that was previously impossible. In this paper we present a selection of WSF3D-driven applications with the objective of demonstrating how the new data can be used to support urban planning and management. First, the WSF3D has been employed to demonstrate how the four layers of the dataset can be used to determine a building's functional use, and how this information can be leveraged to improve large-scale models of population distribution at large-scale. Thereafter, the WSF3D has been used to determine the relationships among building height/volume, population density and income, which can provide insights into the efficient use of space (e.g. crowding vs layering) on the one hand, and shed light into infrastructure disparities and variations, on the other. With that being said, due to the global nature of the WSF3D dataset, the previous analyses were conducted from local to regional scales, which can also help identify opportunities for interventions that can be replicated across different locations. Overall, with the results of this research, the authors aim to provide planners and policy-makers with valuable insights into usability of the globally available WSF3D dataset. By demonstrating its potential as reliable and robust input data, this study seeks not only to empower evidence-based decision-making, but also to advocate for the widespread adoption of geospatial layers in the implementation of strategies towards sustainable development strategies of the built environment

Multi-target regressor chains with repetitive permutation scheme for characterization of built environments with remote sensing

Multi-task learning techniques allow the beneficial joint estimation of multiple target variables. Here, we propose a novel multi-task regression (MTR) method called ensemble of regressor chains with repetitive permutation scheme. It belongs to the family of problem transformation based MTR methods which foresee the creation of an individual model per target variable. Subsequently, the combination of the separate models allows obtaining an overall prediction. Our method builds upon the concept of so-called ensemble of regressor chains which align single-target models along a flexible permutation, i.e., chain. However, in order to particularly address situations with a small number of target variables, we equip ensemble of regressor chains with a repetitive permutation scheme. Thereby, estimates of the target variables are cascaded to subsequent models as additional features when learning along a chain, whereby one target variable can occupy multiple elements of the chain. We provide experimental evaluation of the method by jointly estimating built-up height and built-up density based on features derived from Sentinel-2 data for the four largest cities in Germany in a comparative setup. We also consider single-target stacking, multi-target stacking, and ensemble of regressor chains without repetitive permutation. Empirical results underline the beneficial performance properties of MTR methods. Our ensemble of regressor chain with repetitive permutation scheme approach achieved most frequently the highest accuracies compared to the other MTR methods, whereby mean improvements across the experiments of 14.5% compared to initial single-target models could be achieved

World Settlement Footprint 3D - A first three-dimensional survey of the global building stock

Settlements, and in particular cities, are at the center of key future challenges related to global change and sustainable development. Widely used indicators to assess the efficiency and sustainability of settlement development are the compactness and density of the built-up area. However, at global scale, a temporally consistent and spatially detailed survey of the distribution and concentration of the building stock – meaning the total area and volume of buildings within a defined spatial unit or settlement, commonly referred to as building density – does not yet exist. To fill this data and knowledge gap, an approach was developed to map key characteristics of the world’s building stock in a so far unprecedented level of spatial detail for every single settlement on our planet. The resulting World Settlement Footprint 3D dataset quantifies the fraction, total area, average height, and total volume of buildings for a measuring grid with 90 m cell size. The World Settlement Footprint 3D is generated using a modified version of the World Settlement Footprint human settlements mask derived from Sentinel-1 and Sentinel-2 satellite imagery at 10 m spatial resolution, in combination with 12 m digital elevation data and radar imagery collected by the TanDEM-X mission. The underlying, automated processing framework includes three basic workflows: one estimating the mean building height based on an analysis of height differences along potential building edges, a second module determining the building fraction and total building area within each 90 m cell, and a third part combining the height information and building area in order to determine the average height and total built-up volume at 90 m gridding. Optionally, a simple 3D building model (level of detail 1) can be generated for regions where data on the building footprints is available. A comprehensive validation campaign based on 3D building models obtained for 19 regions (~86,000 km2) and street-view samples indicating the number of floors for >130,000 individual buildings in 15 additional cities documents that the novel World Settlement Footprint 3D data provides valuable and, for the first time, globally consistent information on key characteristics of the building stock in both, large urban agglomerations as well as small-scale rural settlements. Thus, the new dataset represents a promising baseline dataset for a wide range of previously impossible environmental, socioeconomic, and climatological studies worldwide

Large-scale 3D Modelling of the Built Environment - Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data

Continental to global scale mapping of the human settlement extent based on earth observation satellite data has made considerable progress. Nevertheless, the current approaches only provide a two-dimensional representation of the built environment. Therewith, a full characterization is restricted in terms of the urban morphology and built-up density, which can only be gained by a detailed examination of the vertical settlement extent. This paper introduces a methodology for the extraction of three-dimensional (3D) information on human settlements by analyzing the digital elevation and radar intensity data collected by the German TanDEM-X satellite mission in combination with multispectral Sentinel-2 imagery and data from the Open Street Map initiative and the Global Urban Footprint human settlement mask. The first module of the underlying processor generates a normalized digital surface model from the TanDEM-X digital elevation model for all regions marked as a built-up area by the Global Urban Footprint. The second module generates a building mask based on a joint processing of Open Street Map, TanDEM-X/TerraSAR-X radar images, the calculated normalized digital surface model and Sentinel-2 imagery. Finally, a third module allocates the local relative heights of the normalized digital surface model to the building structures provided by the building mask. The outcome of the procedure is a 3D map of the built environment showing the estimated local height for all identified vertical building structures at 12 m spatial resolution. The results of a first validation campaign based on reference data collected for the seven cities of Amsterdam (NL), Indianapolis (US), Kigali (RW), Munich (DE), New York (US), Vienna (AT), and Washington (US) indicate the potential of the proposed methodology to accurately estimate the distribution of building heights within the built-up area

Multi-Output Regression: On the Impact of Individual Model Parameters for Built-Up Height and Density Prediction

Urbanisation is an ongoing process and will gain importance in the future. It comes with multiple challenges as inhabitants are dependent on water and energy supply, a functioning street network and health care system | all these require a deliberate management. However, this is not an easy demand as administrative areas can cover several thousands of square kilometers. Therewith, remote sensing methods constitute a reliable source to observe large areas as cities. To observe the growth of cities, variables as built-up height and built-up density have emerged as reliable attributes that characterize well the urban morphology. They can be obtained by integrating remote sensing data from optical and other sensors such as synthetic aperture radar (SAR). The application of machine learning algorithms makes it feasible to interpret the large amount of data generated in remote sensing. This study focuses on the optimization of machine learning algorithms for predicting builtup height and built-up density in four German major cities based on remote sensing data, by integrating so-called multi-output regression (MOR) methods. Instead of processing and predicting each target variable independently, MOR methods incorporate all target variables into one process which, in the best case, increases the accuracy of predictions. Recent literature highlights the benefit of exploiting possible correlations between target variables. In this work, four methods are applied and modified according to state-of-the-art models: multi-target stacking (MTS), multi-target regressor chains (MTRC), multi-target regressor chains without repetitive permutation (MTRC-nrp) and single-target stacking (STS). Each method is used with four different regression models, namely random forest (RF), Gaussian process (GP), support vector regression (SVR) and neural networks (NN). Additionally, the impact of different stacking options as well as the impact of the feature space is evaluated. The extensive and systematic evaluation of the aforementioned parameters provides several insights. It shows, that all models (MTS, MTRC, MTRC-nrp, STS) outperform models that do not use multi-target stacking or chaining or single-target stacking. Furthermore, it shows that MOR models behave differently depending on which regression model is used for the prediction. Finally, it gives recommendations on which MOR methods and which additional parameters are suitable for particular use cases similar to those evaluated in this study and discusses possibilities for future research