Geolocated text data are a promising data source for spatial analyses in many fields, from disease surveillance to the spatial humanities. This study investigates the relationship between texts’ thematic categories and their likelihood of containing usable geolocation information by quantifying and modelling this relationship across seven diverse English text datasets of different types, including web forums, microblogs, news, and magazines. We find that the likelihood of geoinformation is highly variant, being high for the category ‘Travel, Tourism & Migration’ and low for ‘Private Life, Family & Relationships’. The rank-correlation of this likelihood between datasets is moderate to strong. These findings indicate that the topic plays a significant role in determining the frequency of geospatial references within the text, and that the effect is not entirely dataset-specific. This contributes to the empirical study of the concept of spatiality and provides valuable insights for bias mitigation in the increasing use of text as data for spatial analyses.

Semantic segmentation of remote sensing imagery plays an important role in applications such as environmental monitoring and disaster response. However, challenges such as complex spatial patterns of variable target objects, significant scale variations, and high inter-class similarity challenge accurate segmentation. Most existing methods based on convolutional neural networks (CNNs) and Transformers face limitations in modeling multi-scale global-local dependencies or often incur high computational costs. Therefore, we propose a multi-scale convolution and mamba fusion model (MF-Mamba) that integrates a CNN encoder with a Mamba-based decoder. The decoder incorporates a Global-Local State Space (GLSS) module with eight-directional selective scanning mechanisms and multi-kernel parallel convolutions to capture the rich global-local context. To enhance multi-scale feature representation, we developed a channel-spatial attention and dense multi-scale feature fusion (CSDF) module, which combines channel-spatial attention and atrous convolutions for multi-scale feature fusion. Additionally, a multi-scale lateral connection is developed to align encoder features for efficient integration. Experiments on the data sets of ISPRS Vaihingen, ISPRS Potsdam, and the Wuhan Dense Labeling Dataset (WHDLD) demonstrate the superior performance of MF-Mamba compared to existing state-of-the-art methods. It achieves Mean F1 scores of 86.71%, 90.70%, and 77.07%, respectively. The code is available at https://github.com/Mango-Mars/MF-Mamba.

Building instances play a pivotal role in understanding population distribution and assessing vulnerability in the face of potential risks. Building instance segmentation is a valuable technique for identifying individual structures, however, complex urban environments pose great challenges, especially in the informal areas. In this study, we utilize a building instance segmentation method specifically designed to discern single buildings in both formal and informal settlements. Employing a SkipFuse-UResNet34 model, we generate building instances for Medellín, Colombia, resulting in a more comprehensive building mask compared to conventional official data sources. This enhanced mask serves as a vital tool for estimating the population at risk, enabling a thorough comparison with official data and addressing current spatial knowledge gaps.

Monitoring and understanding human migration as triggered by a crisis is challenging. Combining spatial analysis with natural language processing when analyzing social media data helps to understand the mobility and the needs of migrants better. For this paper, we used geolocated Twitter data to analyze the mobility of and topics discussed by migrants of the Ukraine war in 2022. We removed bots, accounts showing implausible mobility, and automated text content from our dataset. Then, we applied a transformer-based multilingual topic modeling framework to identify the migrants’ discourses. We assessed the topics discussed by migrants before leaving Ukraine, after leaving Ukraine and after returning to Ukraine. Our results show that “Attack reports”, “politics”, “donations to Ukrainians”, “food export/production”, “humanitarian aid”, “nuclear threat”, “Ukrainian places”, “job search”, and “war journalism” were dominant topics before leaving from and after returning to Ukraine. “Food”, “social media”, “transport”, “art”, and “finance”, however, were important topics right after leaving the country. Overall, our results reveal plausible spatial patterns of migration, which are similar to those reported by official statistics (R2 = 0.89), showing the reliability of geotagged social media data to monitor human mobility. This information can complement official sources, adding first-hand information on the mobility and needs of migrants across space, time, topics, and languages. This is crucial to develop humanitarian response plans when time is of the essence.

Ethical issues in Earth Observation (EO) research have not received much attention in academic research thus far. However, they are becoming centrally relevant as a result of increasing satellite image resolution, the use of Artificial Intelligence or Machine Learning tools together with EO data, and an ever-expanding number of real-world use cases based on EO data or image analysis. In this short paper, we provide a summary of ethical issues linked to one key field of EO research, namely, slum mapping, to provide EO scientists some food for thought on how best to re-frame this field of research to avoid ethical pitfalls and maximize ethical opportunities.

In the past decade, the number of refugees and internally displaced people (IDP) has doubled. This prompted the construction of more refugee camps and the proliferation of existing camps with diverse structural morphologies. Satellite imagery and machine learning (ML) are increasingly utilized to map these camps. However, there exists no standardized inventory that systemizes the built-up structures of these camps. In this study, we conceptualize the settlement morphology of refugee and IDP camps from satellite images and create a structure catalogue. Using visual image interpretation (VII) of very-high-resolution and multitemporal imagery, we compile a global database of settlement structures from 285 camps across 1,053 observations. This catalogue is subsequently used to synthesize patterns in camp structures and temporal dynamics. The results show stark variations in settlement structures across camps. Despite some similar regional patterns, stark differences in morphologies are a testament to the global heterogeneous landscape of refugee and IDP camp structures. These findings highlight the importance of considering morphological differences in image analyses across camps in future designs of ML-based automated detection and monitoring efforts. Therein, the Structure Catalogue serves as an important foundation for future earth observation for humanitarian applications.

Small water bodies and their temporal changes are, especially in urban areas, closely related to the urban climate, people's daily life, among others. Mapping of small water bodies with optical remote sensing images in complex urban landscapes is challenging: that is to establish a balance between reducing incorrect water detection and increasing the integrity of water extraction. In this work we propose a spatial information-integrated small water bodies mapping (SWM) method to achieve a complete and accurate extraction and temporal change monitoring of small water bodies. The spatial contextual information is exploited by the proposed water index roughness feature to compensate for the indistinguishability of small water bodies in spectral information. Results using Landsat and Sentinel-2 data show that the proposed algorithm achieves better water extraction performance, i.e. higher completeness and less incorrect extractions. It proves the ability to observe the changes of surface water.

We present a classification postprocessing (CPP) technique based on fully convolutional neural networks (CNNs) for semantic remote sensing image segmentation. Conventional CPP techniques aim to enhance the classification accuracy by imposing smoothness priors in the image domain. Contrary to that, here, a relearning strategy is proposed where the initial classification outcome of a CNN model is provided to a subsequent CNN model via an extended input space to guide the learning of discriminative feature representations in an end-to-end fashion. This deep relearning CNN (DRCNN) explicitly accounts for the geospatial domain by taking the spatial alignment of preliminary class labels into account. Hereby, we evaluate to learn the DRCNN in a cumulative and noncumulative way, i.e., extending the input space based on all previous or solely preceding model outputs, respectively, during an iterative procedure. Besides, the DRCNN can also be conveniently coupled with alternative CPP techniques such as object-based voting (OBV). The experimental results obtained from two test sites of WorldView-II imagery underline the beneficial performance properties of the DRCNN models. They can increase the accuracies of the initial CNN models on average from 72.64% to 76.01% and from 92.43% to 94.52% in terms of$\kappa $statistic. An additional increase of 1.65 and 2.84 percentage points can be achieved when combining the DRCNN models with an OBV strategy. From an epistemological point of view, our results underline that CNNs can benefit from the consideration of preliminary model outcomes and that conventional CPP techniques can profit from an upstream relearning strategy.

Nighttime light (NTL) emissions are widely used across disciplines to map the spatial distribution of a variety of socioeconomic variables. For economic studies, NTLs allow to proxy for levels of economic indicators at the country level as well as the local level. Further, multi-temporal differences in NTL intensity are also related to GDP differences on the country level. In this study, we investigate if this relation in temporal differences also holds for the local level. We test this with DMSP as well as VIIRS NTLs data from 2010–2015 in Nigeria, Tanzania, and Uganda. Even though we successfully map local levels of socio-economic status with NTLs, we find multi-temporal changes in NTLs at this local level to be uncorrelated with socio-economic development over time. We conclude that luminosity values based on current DMSP and VIIRS sensors are no silver bullet in measuring local economic changes over time and should, if at all, only be used with caution for this.

How we develop and shape the space where we live and do business determines different living conditions. How ecologically, socially or economically reasonable and resilient these structures are, however, is not always easy to answer. Geodata are mandatory for an evaluation and assessment. Remote sensing with its diverse data allows us to record objects on the Earth's surface, the topography and the atmosphere. And with it, it becomes possible to describe different living environments in high spatial resolution and in a quantitative manner. Spatial indicators from these data can make developments tangible. In this paper, the focus is on what it means how we live with respect to land consumption. Based on a land-cover classification using Sentinel-2 data as well as on population data from the national census for Germany, it is evaluated what different spatial structures mean in terms of land consumption. Various scenarios are developed to evaluate the impact of settlement types on land consumption. In this sense the aim is to discuss how reasonable and resilient our way of living is.

Agricultural activities cause rapid changes in vegetation development at local and regional scales. Those modifications affect the small-scale behavior of animals, like the foraging ground usage of breeding white storks. Only recently, a novel approach, that enables to quantify the relationship between mowing and harvesting activities and a prolonged foraging time of storks by combining remote sensing time series with GPS telemetry, has been proposed. This study examines the stability of this approach. We investigate two potential influencing factors: different vegetation indices and time lags over which vegetation dynamics were retrieved. Mostly independent from the vegetation index and time lag, we observed that storks spent large proportions of foraging time in areas characterized by a recent drop in vegetation indices, indicative for a preferred usage after harvesting and mowing events. This suggest that the proposed approach is relatively stable and hence, provides a reasonable basis to investigate the effects of anthropogenic vegetation alterations on animal behavior at small spatiotemporal scales.

Polycentricity refers to urban regions with more than one center. These additional (sub-) centers, e.g. spatial concentrations of jobs, are characteristic for the transformation of monocentric towards polycentric urban patterns. Frequently assessed with socioeconomic data, the phenomenon is also reflected in the built morphology of urban landscapes. Only recently, a methodology for large-scale morphological characterization of built-up structures in urban areas relying on TanDEM-X and Sentinel-2 data has been introduced. Thus, a new way to investigate morphologic polycentricity in and among cities is provided. Relying on this approach, we derive the distribution of urban mass concentrations in four city regions. We identify high urban mass concentrations - proxies for (sub-) centers - using a threshold approach. A comparison between the studied regions reveals that only one city tends to have a polycentric urban structure. Our study highlights a new and promising possibility to study the urban morphologic development at global scales.

The TanDEM-X mission (TDM) is a spaceborne radar interferometer which delivers a global digital surface model (DSM) with a spatial resolution of 0.4 arcsec. In this letter, we propose an automatic workflow for digital terrain model (DTM) generation from TDM DSM data through additional consideration of Sentinel-2 imagery and open-source geospatial vector data. The method includes the automatic and robust compilation of training samples by imposing dedicated criteria on the multisource geodata for subsequent learning of a classification model. The model is capable of supporting the accurate distinction of elevated objects (OBJ) and bare earth (BE) measurements in the TDM DSM. Finally, a DTM is interpolated from identified BE measurements. Experimental results obtained from a test site which covers a complex and heterogeneous built environment of Santiago de Chile, Chile, underline the usefulness of the proposed workflow, since it allows for substantially increased accuracies compared to a morphological filter-based method.

Land use reflects the interrelation between the physically built environment and the activity patterns of people. It is indispensable information for decision-makes, but up-to-date and accurate land use information is often absent. Unlike approaches that make use of remote sensing data, in this work, we are interested in a novel data source, tweets, and explore its potential for land use classification in urban areas. Specifically, we propose a general framework for classifying settlement land-use types by extracting location, time, quantity and text features of twitter data. To do so, we apply latent Dirichlet allocation (LDA) and long short-term memory (LSTM) and then combines those features with spatial-temporal feature using Fused SVM and a two-stream convolutional neural network (CNN) for classification. For the case of classifying individual tweets by the land-use classes relevant in this study - residential, non-residential and mixed usage -, we reach overall accuracy (OA), average accuracy (AA), and Kappa coefficient with 72.35%, 73.76%, and 58.43%, respectively. As for the case of classifying block settlement types, we reach 61.90%, 63.33%, and 42.84%, respectively.

We propose a novel cost-sensitive multitask active learning (CSMTAL) approach. Cost-sensitive active learning (CSAL) methods were recently introduced to specifically minimize labeling efforts emerging from ground surveys. Here, we build upon a CSAL method but compile a set of unlabeled samples from a learning set which can be considered relevant with respect to multiple target variables. To this purpose, a multitask meta-protocol based on alternating selection is implemented. It comprises a so-called one-sided selection (i.e., single-task AL selection for a reference target variable with simultaneous labeling of the residual target variables) with a changing leading variable in an iterative selection process. Experimental results are obtained for the city of Cologne, Germany. The target variables to be predicted, using features from remote sensing and a support vector machine framework, are “building type” and “roof type.” Comparative model accuracy evaluations underline the capability of the CSMTAL method to provide beneficial solutions with respect to a random sampling strategy and noncost-sensitive multitask active sampling.

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.

Morphological operators (MOs) and their enhancements such as morphological profiles (MPs) are subject to a lively scientific contemplation since they are found to be beneficial for, for example, classification of very high spatial resolution panchromatic, multi-, and hyperspectral imagery. They account for spatial structures with differing magnitudes and, thus, provide a comprehensive multilevel description of an image. In this paper, we introduce the concept of object-based MPs (OMPs) to also encode shape-related, topological, and hierarchical properties of image objects in an exhaustive way. Thereby, we seek to benefit from the so-called object-based image analysis framework by partitioning the original image into objects with a segmentation algorithm on multiple scales. The obtained spatial entities (i.e., objects) are used to aggregate multiple sequences obtained with MOs according to statistical measures of central tendency. This strategy is followed to simultaneously preserve and characterize shape properties of objects and enable both the topological and hierarchical decompositions of an image with respect to the progressive application of MOs. Subsequently, supervised classification models are learned by considering this additionally encoded information. Experimental results are obtained with a random forest classifier with heuristically tuned hyperparameters and a wrapper-based feature selection scheme. We evaluated the results for two test sites of panchromatic WorldView-II imagery, which was acquired over an urban environment. In this setting, the proposed OMPs allow for significant improvements with respect to classification accuracy compared to standard MPs (i.e., obtained by paired sequences of erosion, dilation, opening, closing, opening by top-hat, and closing by top-hat operations).

In this letter, we present an object-based postclassification relearning approach for enhanced supervised remote sensing image classification. Conventional postclassification processing techniques aim to enhance the classification accuracy by imposing smoothness priors in the image domain (based on, for example, majority filtering or Markov random fields). In contrast to that, here, a supervised classification model is learned for the second time, with additional information generated from the initial classification outcome to enhance the discriminative properties of relearned decision functions. This idea is followed within an object-based image analysis framework. Therefore, we model spatial-hierarchical context relations with the preliminary classification outcome by computing class-related features using a triplet of hierarchical segmentation levels. Those features are used to enlarge the initial feature space and impose spatial regularization in the relearned model. We evaluate the relevance of the method in the context of classifying of a high-resolution multispectral image, which was acquired over an urban environment. The experimental results show an enhanced classification accuracy using this method compared to both per-pixel-based approach and outcomes obtained with a conventional object-based postclassification processing technique (i.e., object-based voting).

The TanDEM-X mission (TDM) is a spaceborne radar interferometer which delivers a global digital surface model (DSM) with an unprecedented spatial resolution. This allows resolving objects above ground such as buildings. Extracting and characterizing those objects in an automated manner represents a challenging problem but opens simultaneously a broad range of large-area applications. In this paper, we discuss and evaluate the suitability of morphological filters (MFs) for the derivation of normalized DSMs from the TDM in complex urban environments and introduce a novel region-growing-based progressive MF procedure. This approach is jointly proposed and can be combined with a postclassification processing scheme to specifically allow for a viable reconstruction of urban morphology in a challenging terrain. The filter approach comprises a multistep procedure using concepts of morphological image filtering, region growing, and interpolation techniques. Therefore, it extends the idea of progressive MFs. The latter aim to identify nonground pixels in the DSM by gradually increasing the size of a structuring element and applying iteratively an elevation difference threshold. After the identification of initial nonground pixels, here, potential nonground pixels are identified within each iteration, and their similarity with respect to neighboring nonground pixels is assessed. Pixels are finally labeled as nonground if a constraint is fulfilled. The postclassification processing scheme adapts techniques of object-based image analyses to further refine regions of classified nonground pixels. Digital terrain models are subsequently generated by interpolating between identified ground pixels. Experimental results are obtained for settlement areas that cover large parts of the cities of Izmir (Turkey) and Wuppertal (Germany). They confirm the capability of the proposed approaches for a reduction of omission errors compared to basic MF-based methods when classifying ground pixels, which is favorable in a mountainous terrain with steep slopes.

The main goal of the TanDEM-X mission (TDM) is the generation of a global digital elevation model (DEM). The global SAR dataset, which is made available in the context of the TDM, is also used to create a global human settlement layer, the Global Urban Footprint (GUF). This paper presents a first large area cross comparison between the Global Urban Footprint and existing human settlement products, which shows promising results with an achieved confidence of 95.86% Overall, 71.15% Producer's and 85.22% User's accuracy.

The German TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) mission (TDM) collects two global data sets of very high resolution (VHR) synthetic aperture radar (SAR) images between 2011 and 2013. Such imagery provides a unique information source for the identification of built-up areas in a so far unique spatial detail. This letter presents the novel implementation of a fully automated processing system for the delineation of human settlements worldwide based on the SAR data acquired in the context of the TDM. The proposed Urban Footprint Processor (UFP) includes three main processing stages dedicated to: i) the extraction of texture information suitable for highlighting regions characterized by highly structured and heterogeneous built-up areas; ii) the generation of a binary settlement layer (built-up, non-built-up) based on an unsupervised classification scheme accounting for both the original backscattering amplitude and the extracted texture; and iii) a final post-editing and mosaicking phase aimed at providing the final Urban Footprint (UF) product for arbitrary geographical regions. Experimental results assess the high potential of the TDM data and the proposed UFP to provide highly accurate geo-data for an improved global mapping of human settlements.

The German earth observation mission TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) is collecting a total of two global coverages of very high resolution (VHR) synthetic aperture radar (SAR) X-band data with a spatial resolution of around three meters in the years 2011 and 2012. This paper outlines the capabilities of this data set in terms of supporting the analysis and monitoring of global human settlement patterns. The basic methodology for a fully-operational detection and delineation of built-up areas from VHR SAR data is presented along with a description of the resulting urban footprint (UF) masks and the operational processing environment for the UF production. The preliminary results of the global UF generation indicate the high potential of the TanDEM-X mission (TDM) with respect to the mapping of urbanization patterns as a basis for the analysis of urban sprawl, peri-urbanization or population estimation.

The global process of urbanization is associated with various ecological, social and economic changes in both the built-up area and the adjacent natural or cultivated landscape. To manage the effects and impacts of this development, effective urban and regional planning requires accurate and up to date information on the urban dynamics. This paper introduces a methodology to automatically detect human settlements and then further characterize the identified built up areas in terms of the building density based on VHR SAR data. The SAR imagery is acquired by the German satellite system TerraSAR-X. Regarding the delineation of the built-up area in the region of Munich we achieved an overall accuracy of 94 % and a Kappa of 0.86. The estimation of building density showed a coefficient of determination (r2) of up to 0.74. The mean absolute error of the modeled building densities was 5%.

Assessing the accuracy of land-cover classifications is a major challenge in remote sensing. This is mostly due to the absence of geometrically and thematically highly resolved, reliable, area wide, and up-to-date reference data. This study focuses on a multifaceted accuracy assessment of an urban footprint classification derived from a single-polarized TerraSAR-X image in stripmap mode for the city of Padang in Indonesia. For this purpose, a pixel-based approach was used to identify the urbanized and nonurbanized areas. As reference, a geometrically and thematically highly resolved, accurate, and detailed 3-D city model is available. Based on this data, the classification result is assessed by basic methodologies-square measures and error matrix. Beyond that, the accuracy of the urban footprint classification is analyzed in dependence of the physical structure of the complex urban landscape-defined by built-up density and building volumes. Results reveal that the accuracy of classification results varies in dependence of the structural characteristics of the particular urban environment. Furthermore, the study shows what is thematically mapped by an urban footprint classification.

Recently a growing number of investigations is dealing with the characterization and partitioning of urban agglomerations into urban structure types (USTs) based on remote sensing data. Since the USTs of interest are usually chosen with respect to the research question, application and type of urban agglomeration there is a need for a flexible and adaptable approach for automatic UST classification. In this study we identify the commonalities of published approaches and derive requirements and tasks to deal with in UST classification. Based on this, we focus on the development of a UST classification system that is highly automated, flexible and adaptable to enable a wide applicability.

City morphology not only reveals spatial distribution of diverse physical parameters but also of diverse socio-economic characteristics. Because of this, spatial structure or zoning in urban spaces is a key variable for inferring information valuable for assessment, planning, and management purposes. The presented methodology shows a mathematical approach to derive homogeneous zones from a solely remote sensing land-cover classification result. By Savitzky-Golay filtering and a subsequent curve-sketching approach, an interpreter-independent differentiation within a city is computed. The classification shows the result of the arrangement of urban zoning without any ancillary data