Human settlement extent (HSE) information is a valuable indicator of world-wide urbanization as well as the resulting human pressure on the natural environment. Therefore, mapping HSE is critical for various environmental issues at local, regional, and even global scales. This paper presents a deep-learning-based framework to automatically map HSE from multi-spectral Sentinel-2 data using regionally available geo-products as training labels. A straightforward, simple, yet effective fully convolutional network-based architecture, Sen2HSE, is implemented as an example for semantic segmentation within the framework. The framework is validated against both manually labelled checking points distributed evenly over the test areas, and the OpenStreetMap building layer. The HSE mapping results were extensively compared to several baseline products in order to thoroughly evaluate the effectiveness of the proposed HSE mapping framework. The HSE mapping power is consistently demonstrated over 10 representative areas across the world. We also present one regional-scale and one country-wide HSE mapping example from our framework to show the potential for upscaling. The results of this study contribute to the generalization of the applicability of CNN-based approaches for large-scale urban mapping to cases where no up-to-date and accurate ground truth is available, as well as the subsequent monitor of global urbanization.

Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including the thermal characteristics of individual buildings, such as the construction type, construction period, and building geometries, can strongly support decision-making for local authorities to help them spatially localize buildings with high potential for thermal renovations. In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. The extracted buildings are used for bottom-up modeling of the residential building heat demand based on construction type and construction period. The results for DL-building extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. The modeled heat demands display a high level of agreement of R² 0.82 compared with reference data. Finally, we analyze various refurbishment scenarios for construction periods and construction types, e.g., revealing that the targeted thermal renovation of multi-family houses constructed between the 1950s and 1970s accounts for about 47% of the total heat demand in a realistic refurbishment scenario.

In this article, we present a novel hybrid framework, which integrates spatial-temporal semantic segmentation with postclassification relearning, for multitemporal land use and land cover (LULC) classification based on very high resolution (VHR) satellite imagery. To efficiently obtain optimal multitemporal LULC classification maps, the hybrid framework utilizes a spatial-temporal semantic segmentation model to harness temporal dependency for extracting high-level spatial-temporal features. In addition, the principle of postclassification relearning is adopted to efficiently optimize model output. Thereby, the initial outcome of a semantic segmentation model is provided to a subsequent model via an extended input space to guide the learning of discriminative feature representations in an end-to-end fashion. Last, object-based voting is coupled with postclassification relearning for coping with the high intraclass and low interclass variances. The framework was tested with two different postclassification relearning strategies (i.e., pixel-based relearning and object-based relearning) and three convolutional neural network models, i.e., UNet, a simple Convolutional LSTM, and a UNet Convolutional-LSTM. The experiments were conducted on two datasets with LULC labels that contain rich semantic information and variant building morphologic features (e.g., informal settlements). Each dataset contains four time steps from WorldView-2 and Quickbird imagery. The experimental results unambiguously underline that the proposed framework is efficient in terms of classifying complex LULC maps with multitemporal VHR images.

In this letter, we establish two sampling schemes to select training and test sets for supervised classification. We do this in order to investigate whether estimated generalization capabilities of learned models can be positively biased from the use of spatial features. Numerous spatial features impose homogeneity constraints on the image data, whereby a spatially connected set of image elements is attributed identical feature values. In addition to a frequent occurrence of intrinsic spatial autocorrelation, this leads to extrinsic spatial autocorrelation with respect to the image data. The first sampling scheme follows a spatially random partitioning into training and test sets. In contrast to that, the second strategy implements a spatially disjoint partitioning, which considers in particular topological constraints that arise from the deployment of spatial features. Experimental results are obtained from multi- and hyperspectral acquisitions over urban environments. They underline that a large share of the differences between estimated generalization capabilities obtained with the spatially disjoint and non-disjoint sampling strategies can be attributed to the use of spatial features, whereby differences increase with an increasing size of the spatial neighborhood considered for computing a spatial feature. This stresses the necessity of a proper spatial sampling scheme for model evaluation to avoid overoptimistic model assessments.

Every city is—quoting Plato—divided into two, one city of the poor, the other of the rich. In this study we test whether the economic urban divide is reflected in the digital sphere of cities. Because, especially in dynamically growing cities, ready-to-use comprehensive data sets on the urban poor, as well as on the digital divide, are not existent, we use proxies: we spatially delimit the urban poor using settlement characteristics derived from remote sensing data. The digital divide is targeted by geolocated Twitter data. Based on a sample of eight cities across the globe, we spatially test whether areas of the urban poor are more likely to be digital cold spots. Over the course of time, we analyze whether temporal signatures in poor urban areas differ from formal environments. We find that the economic divide influences digital participation in public life. Less residents of morphological slums are found to be digitally oriented (“are digitally left behind”) as compared to residents of formal settlements. However, among the few twitter users in morphological slums, we find their temporal behavior similar to the twitter users in formal settlements. In general, we conclude this discussion, this study exemplifies that the combination of both heterogeneous data sets allows for extending the capabilities of individual disciplines for research towards urban poverty.