OpenStreetMap (OSM) has transitioned from a pioneering volunteered geographic information project into a global, multi-disciplinary research nexus. This study presents a bibliometric and systematic analysis of the OSM research landscape, examining its development trajectory and key driving forces. By evaluating 1926 publications from the Web of Science (WoS) Core Collection and 782 State of the Map (SotM) presentations up to June 2024, we quantify publication growth, collaboration patterns, and thematic evolution. Results demonstrate simultaneous consolidation and diversification within the field. While a stable core of contributors continues to anchor OSM research, themes have shifted from initial concerns over data production and quality toward advanced analytical and applied uses. Comparative analysis of OSM-related research in WoS and SotM reveals distinct but complementary agendas between scholars and the OSM community. Building on these findings, we identify six emerging research directions and discuss how evolving partnerships among academia, the OSM community, and industry are poised to shape the future of OSM research. This study establishes a structured reference for understanding the state of OSM studies and offers strategic pathways for navigating its future trajectory.

Due to the reliability of data acquisition, synthetic aperture radar (SAR) sensors are fundamental for remote sensing applications with the need for flexibility and fast response. For urban applications, besides the analysis of salient point signatures, extracted height information allows to evaluate the state of buildings. Recently developed deep learning approaches enable height estimates in situations where only one SAR image of an area of interest is available. However, building shapes still exhibit low quality in the resulting digital surface models (DSMs). This paper presents how derived surface models from the SAR image can be refined with knowledge about the shape of buildings. For that purpose, building representations are learned with a neural network from optical images and CityGML models. The results demonstrate that our model not only effectively transfers knowledge to process DSMs from various data sources but also showcases the ability to generalize across different regions.

Unlike hard classification from medium-resolution sensors, spectral unmixing at sub-pixel level offers improved accuracy in estimating the total surface occupied by a material of interest within a given area. While in ideal cases imaging spectrometer data should be utilized for this purpose, we propose to use a limited number of fixed classes in order to perform spectral unmixing from multispectral data to address the specific challenge of estimating the surface area covered by floating plastic debris in inland waters. In that context, working with multispectral data is motivated by extended opportunities to identify and monitor narrow water channels for variable plastic appearances, in terms of extended coverage, spatial and temporal resolution.

The analysis of complex scattering patterns in SAR images of high-voltage power lines is important to comprehend the multireflection effects of these cylindrical shape structures. Such insights are invaluable for applications related to inspection management, particularly in safety analysis and ongoing monitoring. This letter introduces a novel point scattering tracing method to dissect the intricate multireflection effects of power lines in high-resolution X-band SAR data. The paths of multireflection events at the power lines are delineated through a proposed model, which leverages LiDAR point cloud data and SAR system parameters with subpixel precision. Under the condition of a parallel tangent line to the azimuth direction, the proposed quadratic polynomial model is solvable to pinpoint the centers of the single- and triple-reflection point signatures of power lines in SAR coordinate. The analysis of time-series SAR images reveals the activation of the double- and triple-reflection effects induced by water ripples or swinging lines, which is challenging to quantify. The proposed mathematical model is validated through testing with TerraSAR-X (TSX) data in two distinct cases, and the results are highly consistent. The new insights of both visible and invisible components of the double and triple reflections of power lines introduce new challenges to SAR research.

The retrieval of sun-induced fluorescence (SIF) from hyper-spectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach.

In many remote sensing applications the measured radiance needs to be corrected for atmospheric effects to study surface properties such as reflectance, temperature or emission features. The correction often applies radiative transfer to simulate atmospheric propagation, a time-consuming step usually done offline. In principle, an efficient machine learning (ML) model can accelerate the simulation step. This is the goal pursued here in the context of solar-induced fluorescence (SIF) emitted by vegetation around the O2-A band using the spaceborne DESIS and airborne HyPlant spectrometers. We present an ML simulator of at-sensor radiances trained on synthetic spectra and describe its performance in detail. The simulator is fast and accurate, constituting a promising alternative to a full-fledged, lengthy radiative transfer code for SIF retrieval in the O2-A band with DESIS and HyPlant.

Automated change detection is certainly one of the most discussed applications of remote sensing. However, existing approaches rely on finely co-registered images, as otherwise differences in view point or illumination could lead to erroneous change detections. This is particularly true for very-high-resolution sensors, for which even minor differences might affect several resolution cells. In this paper, we make use of deep learning-based single image height prediction to transform highly non-similar synthetic aperture radar (SAR) images acquired from different viewing angles into homogeneous height maps. The change detection is then carried out in these height maps, thus mitigating any former geometric or radiometric differences of the imagery. An experiment with Capella data observing the city of Mariupol during the Russian war against Ukraine illustrates both the potential and possible risks of the approach.

Safety-critical interactive spaces for supervision and time-critical control tasks are usually characterized by many small displays and physical controls, typically found in control rooms or automotive, railway, and aviation cockpits. Using Virtual Reality (VR) simulations instead of a physical system can significantly reduce the training costs of these interactive spaces without risking real-world accidents or occupying expensive physical simulators. However, the user's physical interactions and feedback methods must be technologically mediated. Therefore, we conducted a within-subjects study with 24 participants and compared performance, task load, and simulator sickness during training of authentic aircraft cockpit manipulation tasks. The participants were asked to perform these tasks inside a VR flight simulator (VRFS) for three feedback methods (acoustic, haptic, and acoustic+haptic) and inside a physical flight simulator (PFS) of a commercial airplane cockpit. The study revealed a partial equivalence of VRFS and PFS, control-specific differences input elements, irrelevance of rudimentary vibrotactile feedback, slower movements in VR, as well as a preference for PFS.

This paper describes the contribution of the DLR team ranking 3rdin Track 1 of the 2020 IEEE GRSS Data Fusion Contest, with results ranking 2ndin Track 2 of the same contest being reported in a companion paper. The classifications are based on refinements of low-resolution MODIS labeling using available higher resolution Sentinel-1 and Sentinel-2 data. Results are initialized with a handcrafted decision tree integrating output from a random forest classifier, and subsequently boosted by detectors for specific classes.

This paper describes the contribution of the DLR team ranking 2ndin Track 2 of the 2020 IEEE GRSS Data Fusion Contest. The semantic classification of multimodal earth observation data proposed is based on the refinement of low-resolution MODIS labels, using as auxiliary training data higher resolution labels available for a validation data set. The classification is initialized with a handcrafted decision tree integrating output from a random forest classifier, and subsequently boosted by detectors for specific classes. The results of the team ranking 3rdin Track 1 of the same contest are reported in a companion paper.

The automatic matching of corresponding regions in remote sensing imagery acquired by synthetic aperture radar (SAR) and optical sensors is a crucial pre-requesite for many data fusion endeavours such as target recognition, image registration, or 3D-reconstruction by stereogrammetry. Driven by the success of deep learning in conventional optical image matching, we have carried out extensive research with regard to deep matching for SAR-optical multi-sensor image pairs in the recent past. In this paper, we summarize the achieved findings, including different concepts based on (pseudo-)siamese convolutional neural network architectures, hard negative mining, alternative formulations of the underlying loss function, and creation of artificial images by generative adversarial networks. Based on data from state-of-the-art remote sensing missions such as TerraSAR-X, Prism, Worldview-2, and Sentinel-1/2, we show what is already possible today, while highlighting challenges to be tackled by future research endeavors.

In contrast to the fields of computer vision and photogrammetry, multiple view geometry has not been extensively exploited in the remote sensing domain so far. Therefore, an empirical study is conducted based on multi view Pléiades data that depicts a scene from multiple orbits and multiple incidence angles. First, an accuracy analysis of the 2D and 3D geo-location performance is elaborated showing that ground control points can be modelled with a root mean square residual error below 30 cm in East, North, and height. Second, digital surface models are reconstructed from all possible stereo pairs and are additionally fused in the multiple view geometry sense. It is shown that employing more data increases the accuracy of the digital surface model while reducing the amount of the non-reconstructed regions.

This paper describes the winning contribution to the 2019 IEEE GRSS Data Fusion Contest Multi-view Semantic Stereo Challenge. In this challenge, a digital surface model (DSM) and a semantic segmentation should be derived from a large number of multi-spectral WorldView-3 images. Results from 50 stereo pairs matched using Semi-Global Matching (SGM) are fused into a DSM. Semantic segmentation is performed with an ensemble of FCN networks taking as input RGB, multi-spectral and height data. Their results are then merged with pixel-wise detectors for the classes water and high vegetation. Compared to the second and third placed teams (mIOU-3 scores of 0.73 and 0.7295), our contribution reached a significantly higher score of 0.745.

The fusion of heterogeneous image data, in particular optical images and synthetic aperture radar (SAR) images, is highly worthwhile in the context of remote sensing tasks as it allows to exploit complementary information - such as spectral and distance measurements or different observation perspectives - of the two data sources while diminishing their individual weaknesses (e.g. cloud cover, difficulty of image interpretation, limited sensor revisit). However, relating the heterogeneous data on the signal level requires a data alignment step, which cannot be realized without auxiliary knowledge. This paper addresses and discusses this fundamental fusion problem in remote sensing in the context of a framework named SimGeoI, which solves the multi-sensor alignment task based on geometric knowledge from existing digital surface models. Sections of optical and SAR images are related to individual objects using interpretation layers generated with ray tracing techniques. Results of SimGeoI are presented for a test site in London in order to motivate an object-related fusion of remote sensing images.

This paper presents the basics and functionality of SimGeoI, a simulation-based framework for the automated interpretation and alignment of optical and SAR remote sensing data. SimGeoI has been developed in order to align optical and SAR data based on given geometric information about objects represented by digital surface models. Thereby, the analysis of urban scenes is possible with independence of sensor type and perspective. After a brief introduction of the processor environment, possible applications of the framework are indicated with results of a case study for Istanbul (WorldView-2 and TerraSAR-X data). In this context, opportunities in the context of a joint analysis of high resolution optical and SAR data are addressed, i.e. concerning data fusion, change detection, and machine learning tasks.

A major research area in remote sensing is the problem of multi-sensor data fusion. Especially the combination of images acquired by different sensor types, e.g. active and passive, is a difficult task. Over the last years deep learning methods have proven their high potential for remote sensing applications. In this paper we will show how a deep learning method can be valuable for the problem of optical and SAR image matching. We investigate the possible of conditional generative adversarial networks (cGANs) for the generation of artificial templates. Contrary to common template generation approaches for image matching, the generation of templates using cGANs does not require the extraction of features. Our results show the possibility of realistic SAR-like template generation from optical images through cGANs and the potential of these templates for enhancing the matching of optical and SAR images by means of reliability and accuracy.

RaySAR, a 3D SAR simulator, has been made accessible to the SAR community in January 2016. It helps to understand and analyze reflection effects of radar signals at 3D object models. For instance, the nature of persistent scatterers or “ghost” scatterers at man-made structures or basic reflection effects at canonical shapes can be interpreted in more detail. The decision to provide RaySAR to the community was based on the persisting lack of a freely available SAR simulation package and the repetitive interpretation task related to the analysis of SAR imagery, especially in the context of high resolution data. Many signal reflection effects at objects remain to be discovered and explained. New interfaces between simulation techniques and SAR applications may be helpful. In this context, the paper provides a summary of the status-quo of RaySAR and addresses future directions that have not been followed so far.

This paper presents preliminary results of a workflow for generating facade reference data as a basis for evaluating the localization capability of 3D SAR algorithms (tomographic or radargrammetric). In this context, the required facade model is provided based on photogrammetric reconstruction and terrestrial surveys with cm-accuracy. The SAR simulator RaySAR is used to provide the phase center positions of prominent scatterers in the world coordinate system. Simulation results in 2D and 3D are presented for a facade located at the TUM campus in Munich to indicate the necessary level-of-detail for the modeling step.

The paper describes the direct retrieval of global coordinates of persistent scatterers (PS) including their secular displacement by plate tectonics from Synthetic Aperture Radar (SAR). For this purpose we combine stereo SAR methods, least squares parameter estimation, and geodetic observation corrections. The procedure is based on our previous research with TerraSAR-X and now applied for PS situated on the facade of a building of Technische Universität München (TUM). In order to verify the PS-based solution, we have created an accurate building model with global coordinates suitable for SAR simulations, and we use the results of permanent Global Navigation Satellite Systems (GNSS) to validate the displacement rates. Our preliminary results for the simulated phase centers and stereo SAR already indicate similar phase centers on the building facade, and the displacement rates could be retrieved with mm/year accuracy.

The grammar of facade structures is often related to regularly distributed signature patterns in high-resolution synthetic aperture radar (SAR) images. Given those patterns in the imagery, they should be used as a source of information for identifying changes related to the facade. This paper presents a method for characterizing the layover area pertinent to regularly arranged facade structures, formulated on a general basis for single azimuth/range SAR images and geocoded SAR images. The analysis follows assumptions on the intensity distribution, the linear arrangement, and the regularity of point-like signatures. Two case studies on facades are presented, which confirm the applicability of the method for different building types. Based on that, the potentials and limitations of the algorithm are discussed with respect to applications such as change detection and persistent scatterer interferometry.

Corner lines, line signatures related to building facades in synthetic aperture radar images, are commonly related to signal double reflections, either specular or diffuse. However, the scene properties, i.e., building orientation, building shape, and surface roughness, often do not correspond to this expectation. This letter presents a 3-D representative simulation case study, based on ray tracing and a detailed facade model, in order to analyze the origin of corner lines at facades. The simulation results indicate that the intensity of corner lines may be dominated by signal triple reflections of different types.

Change detection of two SAR images captured with different incidence angles is a difficult task but may be important in urgent situations like earthquakes. This paper presents a simulation based algorithm to detect negative changes of buildings in two high resolution SAR images captured with different incidence angles. The analysis is supported by LiDAR data where individual wall models are extracted and are simulated to predict their shape in the SAR images. Afterwards, point signatures within the layover areas are extracted, converted to the same geometry, and are compared with a buffer change detection algorithm. The proposed method is tested for several buildings (in Munich city center) imaged in TerraSAR-X spotlight mode.

Abstract We present an Eulerian method for the real‐time simulation of intrinsic fluid dynamics effects on deforming surfaces. Our method is based on a novel semi‐Lagrangian closest point method for the solution of partial differential equations on animated triangle meshes. We describe this method and demonstrate its use to compute and visualize flow and wave propagation along such meshes at high resolution and speed. Underlying our technique is the efficient conversion of an animated triangle mesh into a time‐dependent implicit representation based on closest surface points. The proposed technique is unconditionally stable with respect to the surface deformation and, in contrast to comparable Lagrangian techniques, its precision does not depend on the level of detail of the surface triangulation.

This paper presents a new algorithm for recognizing patterns pertinent to the layover of façades in very high resolution SAR images, serving the increasing demand for monitoring individual buildings. The grouping of signatures is based on the combination of a weighted Hough transform and the analysis of spectrum peaks. First results of pattern extraction are shown for an office building located in the Munich city area.

With the enhancing availability and variety of space borne Synthetic Aperture Radar (SAR) data and a growing number of analysis algorithms the need for a vital user community is increasing. Therefore the German Aerospace Center (DLR) together with the Friedrich-Schiller-University Jena (FSU) and the Technical University Munich (TUM) launched the education initiative SAR-EDU. The aim of the project is to facilitate access to expert knowledge in the scientific field of radar remote sensing. Within this effort a web portal will be created to provide seminar material on SAR basics, methods and applications to support both, lecturers and students.

Abstract The Closest Point Method (CPM) is a method for numerically solving partial differential equations (PDEs) on arbitrary surfaces, independent of the existence of a surface parametrization. The CPM uses a closest point representation of the surface, to solve the unmodified Cartesian version of a surface PDE in a 3D volume embedding, using simple and well‐understood techniques. In this paper, we present the numerical solution of the wave equation and the incompressible Navier‐Stokes equations on surfaces via the CPM, and we demonstrate surface appearance and shape variations in real‐time using this method. To fully exploit the potential of the CPM, we present a novel GPU realization of the entire CPM pipeline. We propose a surface‐embedding adaptive 3D spatial grid for efficient representation of the surface, and present a high‐performance approach using CUDA for converting surfaces given by triangulations into this representation. For real‐time performance, CUDA is also used for the numerical procedures of the CPM. For rendering the surface (and the PDE solution) directly from the closest point representation without the need to reconstruct a triangulated surface, we present a GPU ray‐casting method that works on the adaptive 3D grid.

Abstract Previous terrain rendering approaches have addressed the aspect of data compression and fast decoding for rendering, but applications where the terrain is repeatedly modified and needs to be buffered on disk have not been considered so far. Such applications require both decoding and encoding to be faster than disk transfer. We present a novel approach for editing gigasample terrain fields at interactive rates and high quality. To achieve high decoding and encoding throughput, we employ a compression scheme for height and pixel maps based on a sparse wavelet representation. On recent GPUs it can encode and decode up to 270 and 730 MPix/s of color data, respectively, at compression rates and quality superior to JPEG, and it achieves more than twice these rates for lossless height field compression. The construction and rendering of a height field triangulation is avoided by using GPU ray‐casting directly on the regular grid underlying the compression scheme. We show the efficiency of our method for interactive editing and continuous level‐of‐detail rendering of terrain fields comprised of several hundreds of gigasamples.

When exploiting very high resolution SAR data stacks, the persistent scatterer interferometry algorithm may mistakenly localize scatterers below the ground level. This paper supports this assumption by a case study using the 3D SAR simulator RaySAR. For a specific urban scene, signal reflections between two adjacent buildings lead to the occurrence of ghost persistent scatterers related to reflection level 5-which do not represent the physical shape of the buildings. Eventually, simulation results confirm the strong appearance of the multiple reflected signals on high resolution TerraSAR-X images.

Persistent scatterer interferometry using stacks of very high resolution synthetic aperture radar (SAR) data reveals that single or even patterns of scatterers representing building structures may wrongly be localized below the ground level. In this letter, a case study on a test building model is presented using 3-D SAR simulation methods in order to explain the underlying localization problem. The case study indicates that Ghost-PSs are likely to be related to reflection levels that are higher than three. Moreover, the temporal stability of the amplitude of fivefold bounce signals is confirmed for a SAR data stack.

In this paper, a simulation concept is presented for creating synthetic aperture radar (SAR) reflectivity maps based on ray tracing. Three-dimensional models of man-made objects are illuminated by a virtual SAR sensor whose signal is approximated by rays sent through the model space. To this end, open-source software tools are adapted and extended to derive output data in SAR geometry followed by creating the reflectivity map. Rays can be followed for multiple reflections within the object scene. Signals having different multiple reflection levels are stored in separate image layers. For evaluating the potentials and limits of the simulation approach, simulated reflectivity maps and distribution maps are compared with real TerraSAR-X images for various complex man-made objects like a skyscraper in Tokyo, the Wynn Hotel in Las Vegas, and the Eiffel Tower in Paris. The results show that the simulation can provide very valuable information to interpret complex SAR images or to predict the reflectivity of planned SAR image acquisitions.

High quality volume rendering of SPH data requires a complex order-dependent resampling of particle quantities along the view rays. In this paper we present an efficient approach to perform this task using a novel view-space discretization of the simulation domain. Our method draws upon recent work on GPU-based particle voxelization for the efficient resampling of particles into uniform grids. We propose a new technique that leverages a perspective grid to adaptively discretize the view-volume, giving rise to a continuous level-of-detail sampling structure and reducing memory requirements compared to a uniform grid. In combination with a level-of-detail representation of the particle set, the perspective grid allows effectively reducing the amount of primitives to be processed at run-time. We demonstrate the quality and performance of our method for the rendering of fluid and gas dynamics SPH simulations consisting of many millions of particles.

The side-looking geometry of SAR sensors hampers the interpretation of SAR images of urban areas. Simulation tools for illuminating 3D models of man-made objects by means of a virtual sensor support the interpretation of scattering effects by providing artificial images in the azimuth-range plane. In this paper, a simulation approach is presented which extends SAR simulation to three dimensions in order to focus detected intensity contributions in azimuth, range and elevation. Based on the simulation output, a concept for creating scatterer histograms displaying the number of scatterers within one resolution cell is introduced. Methods for analyzing simulated elevation data by means of selected slices are presented for an urban test site. Eventually, the number of scatterers extracted for a selected pixel by tomographic analysis, using a stack of spotlight TerraSAR-X images, is confirmed by results provided by the simulator.

Nanoparticles introduced in living cells are capable of strongly promoting the aggregation of peptides and proteins. We use here molecular dynamics simulations to characterise in detail the process by which nanoparticle surfaces catalyse the self-assembly of peptides into fibrillar structures. The simulation of a system of hundreds of peptides over the millisecond timescale enables us to show that the mechanism of aggregation involves a first phase in which small structurally disordered oligomers assemble onto the nanoparticle and a second phase in which they evolve into highly ordered as their size increases.

This paper presents an approach for using backward ray tracing for simulating radar reflectivity maps. After explaining the simulation concept which consists of three parts - modeling, sampling and image generation - two applications are presented for showing the performance of the simulator. At first reflection contributions like single or multiple bounce are simulated for a modeled building and compared to a real TerraSAR-X image. It shows how the capability of separating different bounce levels in different layers can support the interpretation of the SAR image. Afterwards reflection effects caused by quasi-perfect specular reflection are detected for another building model by means of geometrical analysis. This is thought as input for advanced Persistent Scatterer (PS) Analysis, since PS typically appear due to such reflection effects.

The presence of oligomeric aggregates, which is often observed during the process of amyloid formation, has recently attracted much attention because it has been associated with a range of neurodegenerative conditions including Alzheimer's and Parkinson's diseases. We provide a description of a sequence-indepedent mechanism by which polypeptide chains aggregate by forming metastable oligomeric intermediate states prior to converting into fibrillar structures. Our results illustrate that the formation of ordered arrays of hydrogen bonds drives the formation of beta-sheets within the disordered oligomeric aggregates that form early under the effect of hydrophobic forces. Individual beta-sheets initially form with random orientations and subsequently tend to align into protofilaments as their lengths increase. Our results suggest that amyloid aggregation represents an example of the Ostwald step rule of first-order phase transitions by showing that ordered cross-beta structures emerge preferentially from disordered compact dynamical intermediate assemblies.

This paper introduces a modularized tool for the processing of LIDAR data based on the analysis of neighbor relationships between LIDAR points with the goal to extract planes, lines, and points in 3D. The tool's functionalities will be exemplified by the application of reconstructing building roofs. Detecting buildings within digital surface models is one further step to enhance the results of fully- and semi-automatic software tools which handle huge LIDAR point clouds. The functionalities comprise the sorting of point coordinates to improve efficiency, the retrieval of LIDAR point topology by triangulation of points, the extraction of 3D planes by "plane growing" and the determination of lines, points and roof outlines based on the 3D planes by statistical estimation and hypothesis testing of their parameters.