arXiv subjects

Stefan Auer

Publications and source records attributed to Stefan Auer.

17 recordsLinked to original sources

arXiv2026

A Deep Dive into OpenStreetMap Research Since its Inception (2008-2024): Contributors, Topics, and Future Trends

OpenStreetMap (OSM) has transitioned from a pioneering volunteered geographic information (VGI) 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 1,926 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.The data and code are available at https://github.com/ya0-sun/OSMbib.

cs.DL↗

arXiv2025

TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset

Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources. Creating UDTs involves challenges at multiple process stages, including acquiring accurate 3D source data, reconstructing high-fidelity 3D models, maintaining models' updates, and ensuring seamless interoperability to downstream tasks. Current datasets are usually limited to one part of the processing chain, hampering comprehensive UDTs validation. To address these challenges, we introduce the first comprehensive multimodal Urban Digital Twin benchmark dataset: TUM2TWIN. This dataset includes georeferenced, semantically aligned 3D models and networks along with various terrestrial, mobile, aerial, and satellite observations boasting 32 data subsets over roughly 100,000 $m^2$ and currently 767 GB of data. By ensuring georeferenced indoor-outdoor acquisition, high accuracy, and multimodal data integration, the benchmark supports robust analysis of sensors and the development of advanced reconstruction methods. Additionally, we explore downstream tasks demonstrating the potential of TUM2TWIN, including novel view synthesis of NeRF and Gaussian Splatting, solar potential analysis, point cloud semantic segmentation, and LoD3 building reconstruction. We are convinced this contribution lays a foundation for overcoming current limitations in UDT creation, fostering new research directions and practical solutions for smarter, data-driven urban environments. The project is available under: https://tum2t.win

cs.CV↗

arXiv2024

Retrieval of sun-induced plant fluorescence in the O$_2$-A absorption band from DESIS imagery

We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation ($r^2=0.6$) to high-quality airborne estimates of sun-induced fluorescence (SIF). SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESA's upcoming FLEX mission we develop a method for SIF retrieval in the O$_2$-A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based self-supervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of 0.78 mW / nm / sr / m$^2$.

cs.CV↗

arXiv2023

Towards Large-scale Building Attribute Mapping using Crowdsourced Images: Scene Text Recognition on Flickr and Problems to be Solved

Crowdsourced platforms provide huge amounts of street-view images that contain valuable building information. This work addresses the challenges in applying Scene Text Recognition (STR) in crowdsourced street-view images for building attribute mapping. We use Flickr images, particularly examining texts on building facades. A Berlin Flickr dataset is created, and pre-trained STR models are used for text detection and recognition. Manual checking on a subset of STR-recognized images demonstrates high accuracy. We examined the correlation between STR results and building functions, and analysed instances where texts were recognized on residential buildings but not on commercial ones. Further investigation revealed significant challenges associated with this task, including small text regions in street-view images, the absence of ground truth labels, and mismatches in buildings in Flickr images and building footprints in OpenStreetMap (OSM). To develop city-wide mapping beyond urban hotspot locations, we suggest differentiating the scenarios where STR proves effective while developing appropriate algorithms or bringing in additional data for handling other cases. Furthermore, interdisciplinary collaboration should be undertaken to understand the motivation behind building photography and labeling. The STR-on-Flickr results are publicly available at https://github.com/ya0-sun/STR-Berlin.

cs.CV↗

arXiv2023

Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI

In order to respond effectively in the aftermath of a disaster, emergency services and relief organizations rely on timely and accurate information about the affected areas. Remote sensing has the potential to significantly reduce the time and effort required to collect such information by enabling a rapid survey of large areas. To achieve this, the main challenge is the automatic extraction of relevant information from remotely sensed data. In this work, we show how the combination of drone-based data with deep learning methods enables automated and large-scale situation assessment. In addition, we demonstrate the integration of onboard image processing techniques for the deployment of autonomous drone-based aid delivery. The results show the feasibility of a rapid and large-scale image analysis in the field, and that onboard image processing can increase the safety of drone-based aid deliveries.

cs.CY↗

arXiv2020

Generative Adversarial Networks for Synthesizing InSAR Patches

Generative Adversarial Networks (GANs) have been employed with certain success for image translation tasks between optical and real-valued SAR intensity imagery. Applications include aiding interpretability of SAR scenes with their optical counterparts by artificial patch generation and automatic SAR-optical scene matching. The synthesis of artificial complex-valued InSAR image stacks asks for, besides good perceptual quality, more stringent quality metrics like phase noise and phase coherence. This paper provides a signal processing model of generative CNN structures, describes effects influencing those quality metrics and presents a mapping scheme of complex-valued data to given CNN structures based on popular Deep Learning frameworks.

eess.SP↗

arXiv2014

Universality in the morphology and mechanics of coarsening amyloid fibril networks

Above a critical concentration a wide variety of peptides and proteins self-assemble into amyloid fibrils which entangle to form percolating networks called hydrogels. Such hydrogels have important applications as biomaterials and in nanotechnology, but their applicability often depends on their mechanical properties for which we currently have no predictive capability. Here we use a peptide model to simulate the formation of amyloid fibril networks, and couple these to elastic network theory to determine their mechanical properties. The simulations reveal that the time-dependence of morphological quantities characterizing the network length scales can be collapsed onto master curves by using a time scaling function that depends on the interaction parameter between the peptides. The same scaling function is used to unveil a universal, non-monotonic dependence of the shear modulus with time. The obtained insight into the structure-function relationship between the peptide building blocks, network morphology and network mechanical properties can aid experimentalists to design amyloid fibril networks with tailored mechanical properties.

physics.bio-ph↗

arXiv2010

Phase Phenomena in Supported Lipid Films under Varying Electric Potential

We model cyclic voltammetry experiments on supported lipid films where a non-trivial dependence of the capacitance on the applied voltage is observed. Previously, based on a mean-field treatment of the Flory-Huggins type, under the assumption of strongly screened electrostatic interactions, it has been hypothesized that peaks in the capacitance-vs-voltage profiles correspond to a sequence of structural or phase transitions within the interface. To examine this hypothesis, in this study we use both mean-field calculations and Monte Carlo simulations where the electrostatic effects due to the varying electric potential and the presence of salt are accounted for explicitly. Our main focus is on the structure of the film and the desorption-readsorption phenomena. These are found to be driven by a strong competition for the progressively charged-up (hydrophobic) surface between lipid hydrocarbon tails and the electrode counterions (cations). As the surface charge density is raised, the following phase phenomena within the interface are clearly observed: (i) a gradual displacement of the monolayer from the surface by the counterions, leading to complete monolayer desorption and formation of an electric double layer by the surface, (ii) a transformation of the monolayer into a bilayer upon its desorption, (iii) in the case of zwitterionic (or strongly polar) lipid head groups, the desorption is followed by the bilayer readsorption to the electrode via interaction with the electric double layer and release of the excess counterions into the bulk solution. We argue then that the voltammetry peaks are associated with a stepwise process of formation of layers of alternating charge: electric double layer - upon film desorption, triple or multi-layer - upon film readsoption.

cond-mat.soft↗

arXiv2010

Nucleation of amyloid fibrils

We consider nucleation of amyloid fibrils in the case when the process occurs by the mechanism of direct polymerization of practically fully extended protein segments, i.e. beta-strands, into beta-sheets. Applying the classical nucleation theory, we derive a general expression for the work to form a nanosized amyloid fibril (protofilament) constituted of successively layered beta-sheets. Analysis of this expression reveals that with increasing its size, the fibril transforms from one-dimensional into two-dimensional aggregate in order to preserve the equilibrium shape corresponding to minimal formation work. We determine the size of the fibril nucleus, the fibril nucleation work and the fibril nucleation rate as explicit functions of the concentration and temperature of the protein solution. The results obtained are applicable to homogeneous nucleation which occurs when the solution is sufficiently pure and/or strongly supersaturated.

q-bio.BM↗

arXiv2010

Insight into the correlation between lag time and aggregation rate in the kinetics of protein aggregation

Under favourable conditions, many proteins can assemble into macroscopically large aggregate's, Parkinson's and other neurological and systemic diseases. The overall process of protein aggregation is characterized by initial lag time during which no detectable aggregation occurs in the solution and by maximal aggregation rate at which the dissolved protein converts into aggregates. In this study, the correlation between the lag time and the maximal rate of protein aggregation is analyzed. It is found that the product of these two quantities depends on a single numerical parameter, the kinetic index of the curve quantifying the time evolution of the fraction of protein aggregated. As this index depends relatively little on the conditions and/or system studied, our finding provides insight into why for many experiments the values of the product of the lag time and the maximal aggregation rate are often equal or quite close to each other. It is shown how the kinetic index is related to a basic kinetic parameter of a recently proposed theory of protein aggregation.

q-bio.BM↗

arXiv2010

Phase Diagram of alpha-Helical and beta-Sheet Forming Peptides

The intrinsic property of proteins to form structural motifs such as alpha-helices and beta-sheets leads to a complex phase behavior in which proteins can assemble into various types of aggregates including crystals, liquidlike phases of unfolded or natively folded proteins, and amyloid fibrils. Here we use a coarse-grained protein model that enables us to perform Monte Carlo simulations for determining the phase diagram of natively folded alpha-helical and unfolded beta-sheet forming peptides. The simulations reveal the existence of various metastable peptide phases. The liquidlike phases are metastable with respect to the fibrillar phases, and there is a hierarchy of metastability.

q-bio.BM↗

arXiv2010

A Condensation-Ordering Mechanism in Nanoparticle-Catalyzed Peptide Aggregation

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 beta-sheets as their size increases.

q-bio.BM↗

arXiv2009

Generic Mechanism of Emergence of Amyloid Protofilaments from Disordered Oligomeric aggregates

The presence of oligomeric aggregates, which is often observed during the process of amyloid formation, has recently attracted much attention since it has been associated with neurodegenerative conditions such as 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 how 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. Initially individual beta-sheets form with random orientations, which subsequently tend to align into protofilaments as their lengths increases. 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.

q-bio.BM↗

arXiv2009

Self-Templated Nucleation in Peptide and Protein aggregation

Peptides and proteins exhibit a common tendency to assemble into highly ordered fibrillar aggregates, whose formation proceeds in a nucleation-dependent manner that is often preceded by the formation of disordered oligomeric assemblies. This process has received much attention because disordered oligomeric aggregates have been associated with neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Here we describe a self-templated nucleation mechanism that determines the transition between the initial condensation of polypeptide chains into disordered assemblies and their reordering into fibrillar structures. The results that we present show that at the molecular level this transition is due to the ability of polypeptide chains to reorder within oligomers into fibrillar assemblies whose surfaces act as templates that stabilise the disordered assemblies.

q-bio.BM↗

arXiv2007

Importance of Metastable States in the Free Energy Landscapes of Polypeptide Chains

We show that the interplay between excluded volume effects, hydrophobicity, and hydrogen bonding of a tube-like representation of a polypeptide chain gives rise to free energy landscapes that exhibit a small number of metastable minima corresponding to common structural motifs observed in proteins. The complexity of the landscape increases only moderately with the length of the chain. Analysis of the temperature dependence of these landscapes reveals that the stability of specific metastable states is maximal at a temperature close to the mid-point of folding. These mestastable states are therefore likely to be of particular significance in determining the generic tendency of proteins to aggregate into potentially pathogenic agents.

cond-mat.soft↗

arXiv2004

Homogeneous nucleation of colloidal melts under the influence of shearing fields

We study the effect of shear flow on homogeneous crystal nucleation, using Brownian Dynamics simulations in combination with an umbrella sampling like technique. The symmetry breaking due to shear results in anisotropic radial distribution functions. The homogeneous shear rate suppresses crystal nucleation and leads to an increase of the size of the critical nucleus. These observations can be described by a simple, phenomenological extension of classical nucleation theory. In addition, we find that nuclei have a preferential orientation with respect to the direction of shear. On average the longest dimension of a nucleus is along the vorticity direction, while the shortest dimension is preferably perpendicular to that and slightly tilted with respect to the gradient direction.

cond-mat.soft↗

arXiv2004

Crystal Nucleation of Colloidal Suspensions under Shear

We use Brownian Dynamics simulations in combination with the umbrella sampling technique to study the effect of shear flow on homogeneous crystal nucleation. We find that a homogeneous shear rate leads to a significant suppression of the crystal nucleation rate and to an increase of the size of the critical nucleus. A simple, phenomenological extension of classical nucleation theory accounts for these observations. The orientation of the crystal nucleus is tilted with respect to the shear direction.

cond-mat.soft↗