Search Results (163)

Ground settlement is a multifaceted geological phenomenon driven by natural and man-made forces, posing a significant impediment to sustainable urban development. Thus, ground settlement susceptibility (GSS) mapping has emerged as a critical tool for understanding and mitigating cascading hazards in seismically active and anthropogenically modified sedimentary basins. Here, we develop an integrated framework for assessing GSS in the Pohang region, South Korea, by integrating Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR)-derived vertical land motion (VLM) data with seismological, geotechnical, and topographic parameters (i.e., peak ground acceleration (PGA), effective shear-wave velocity (V_s³⁰), site period (T_s), general amplification factor (A_F), seismic vulnerability index (K_g), soil depth, topographic slope, and landform classes) through ensemble machine learning models such as Random Forest (RF), XGBoost, and Decision Tree (DT). Analysis of 56 Sentinel-1 SLC images (2017–2023) revealed persistent subsidence concentrated in Quaternary alluvium, reclaimed coastal plains, and basin-fill deposits. Among the tested models, RF achieved the best performance and strongly agreed with field evidence of sand boils, liquefaction, and structural damage from the 2017 Pohang earthquake. The very-high-susceptibility zones exhibited mean subsidence rates of −3.21 mm/year, primarily within soft sediments (V_s³⁰ < 360 m/s) and areas of thick alluvium deposits. Integration of the optimal RF-based GSS index with regional building inventories revealed that nearly 65% of existing buildings fell within high- to very-high-susceptibility zones. The proposed framework demonstrates that integrating PSInSAR and ensemble learning provides a robust and transferable approach for quantifying ground settlement hazards and supporting risk-informed urban planning in seismically active and complex geological coastal environments. Full article

Simulation of damage scenarios is an important tool for seismic risk mitigation. While a detailed analysis of each building would be preferable to assess their vulnerability to seismic hazard, simplified yet robust methodologies are necessary at a large urban scale to overcome computational costs or data unavailability. Moreover, most damage assessments simulate single seismic shocks, though in many real sequences, with a series of aftershocks following the mainshocks, it is observed that buildings endure damage accumulation, which increases their vulnerability over time. The present study builds on a recently developed methodology for simulating urban-scale damage scenarios across seismic sequences, explicitly accounting for damage accumulation and the evolution of vulnerability. In particular, the availability of a dataset reporting the damage observed in the L’Aquila area (Italy) during the severe earthquake sequence of 2009, in combination with the georeferenced maps representing the spatial distribution of the ground motion, allows for the calibration of the methodology through the comparison between the simulations’ results and the sequence’s real data. Although calibrated on the L’Aquila dataset, the proposed procedure could also be applied to different urban areas, with both real and synthetic seismic sequences, enabling the forecasting of damage scenarios to support the development of effective strategies for seismic risk mitigation. Full article

Crack monitoring in concrete structures is essential to maintaining structural integrity. Therefore, this paper proposes a mobile ground robot equipped with a 2-DoF manipulator and stereo vision sensors for autonomous crack monitoring and mapping. To facilitate crack detection over large areas, a 2-DoF motorized manipulator providing linear and rotational motions, with a stereo vision sensor mounted on the end effector, was deployed. In combination with a manual rotation plate, this configuration enhances accessibility and expands the field of view for crack monitoring. Another stereo vision sensor, mounted at the front of the robot, was used to acquire point cloud data of the surrounding environment, enabling tasks such as SLAM (simultaneous localization and mapping), path planning and following, and obstacle avoidance. Cracks are detected and segmented using the deep learning algorithms YOLO (You Only Look Once) v6-s and SFNet (Semantic Flow Network), respectively. To enhance the performance of crack segmentation, synthetic image generation and preprocessing techniques, including cropping and scaling, were applied. The dimensions of cracks are calculated using point clouds filtered with the median absolute deviation method. To validate the performance of the proposed crack-monitoring and mapping method with the robot system, indoor experimental tests were performed. The experimental results confirmed that, in cases of divided imaging, the crack propagation direction was predicted, enabling robotic manipulation and division-point calculation. Subsequently, total crack length and width were calculated by combining reconstructed 3D point clouds from multiple frames, with a maximum relative error of 1%. Full article

This article presents a principled framework for selecting and tuning classical computer vision algorithms in the context of optical displacement sensing. By isolating key factors that affect algorithm behavior—such as feed window size and motion step size—the study seeks to move beyond intuition-based practices and provide rigorous, repeatable performance evaluations. Computer vision-based optical odometry sensors offer non-contact, high-precision measurement capabilities essential for modern metrology and robotics applications. This paper presents a systematic comparative analysis of three classical tracking algorithms—phase correlation, template matching, and optical flow—for 2D surface displacement measurement using synthetic image sequences with subpixel-accurate ground truth. A virtual camera system generates controlled test conditions using a multi-circle trajectory pattern, enabling systematic evaluation of tracking performance using 400 × 400 and 200 × 200 pixel feed windows. The systematic characterization enables informed algorithm selection based on specific application requirements rather than empirical trial-and-error approaches. Full article

Star trackers are critical electro-optical devices used for satellite attitude determination, typically tested using Optical Ground Support Equipment (OGSE). Within the POR FESR 2014–2020 program (funded by Regione Toscana), we developed MINISTAR, a compact electro-optical prototype designed to generate synthetic star fields in apparent motion for realistic ground-based testing of star trackers. MINISTAR supports simultaneous testing of up to three units, assessing optical, electronic, and on-board software performance. Its reduced size and weight allow for direct integration on the satellite platform, enabling testing in assembled configurations. The system can simulate bright celestial bodies (Sun, Earth, Moon), user-defined objects, and disturbances such as cosmic rays and stray light. Radiometric and geometric calibrations were successfully validated in laboratory conditions. Under the PR FESR TOSCANA 2021–2027 initiative (also funded by Regione Toscana), the concept was further developed into STARLITE (STAR tracker LIght Test Equipment), a next-generation OGSE with a higher Technology Readiness Level (TRL). Based largely on commercial off-the-shelf (COTS) components, STARLITE targets commercial maturity and enhanced functionality, meeting the increasing demand for compact, high-fidelity OGSE systems for pre-launch verification of attitude sensors. This paper describes the working principles of a generic system, as well as its main characteristics and the early advancements enabling the transition from the initial MINISTAR prototype to the next-generation STARLITE system. Full article

Air moving-target detection is crucial for national defense, civil aviation, and airspace supervision. Spaceborne synthetic aperture radar (SAR) provides high-resolution, continuous observations for this task, but faces challenges including target attitude variation-induced weak signals and Doppler defocusing from targets’ high-speed motion, which hinder target-background separation. To address this, we propose a novel method combining sub-aperture segmentation with GoDec+ low-rank decomposition to enhance signal-to-noise ratio and suppress defocusing. Critically, ADS-B flight data is integrated as ground truth for spatio-temporal validation. Experiments using Sentinel-1 SM mode SLC imagery across farmland, forest, and mountainous regions confirm the method’s effectiveness and robustness in real airspace scenarios. Full article

Groundwater level monitoring is crucial for assessing hydrological responses to climate change and human activities, which pose significant threats to the sustainability of semi-arid aquifers and the cultural heritage they sustain. This study presents an integrated remote sensing and transformer-based deep learning framework that combines diverse geospatial datasets to predict spatiotemporal variations across the plain near the Persepolis and Naqsh-e Rustam archaeological complexes—UNESCO World Heritage Sites situated at the plain’s edge. We assemble 432 synthetic aperture radar (SAR) scenes (2015–2022) and derive vertical ground motion rates greater than −180 mm yr⁻¹, which are co-localized with multisource geoinformation, including hydrometeorological indices, biophysical parameters, and terrain attributes, to train transformer models with traditional deep learning methods. A sparse probabilistic transformer (ConvTransformer) trained on 95 gridded variables achieves an out-of-sample R² = 0.83 and RMSE = 6.15 m, outperforming bidirectional deep learning models by >40%. Scenario analysis indicates that, in the absence of intervention, subsidence may exceed 200 mm per year within a decade, threatening irreplaceable Achaemenid stone reliefs. Our results indicate that attention-based networks, when coupled to synergistic geodetic constraints, enable early-warning quantification of groundwater stress over heritage sites and provide a scalable template for sustainable aquifer governance worldwide. Full article

Enhanced real-time onboard orbit determination for low-Earth-orbit satellites is essential for autonomous spacecraft operations. However, the accuracy of such systems is often limited by signal propagation delays between GPS satellites and the user spacecraft. These delays, primarily due to Earth’s rotation and ionospheric effects become particularly significant in high-dynamic LEO environments, leading to considerable errors in range and range rate measurements, and consequently, in position and velocity estimation. To mitigate these issues, this paper proposes a real-time orbit determination algorithm that applies Earth rotation correction and dual-frequency (L1 and L2) ionospheric compensation to raw GPS measurements. The enhanced orbit determination method is processed directly in the Earth-centered Earth-fixed frame, eliminating repeated coordinate transformations and improving integration with ground-based systems. The proposed method employs a reduced-dynamic orbit determination strategy to balance model fidelity and computational efficiency. A predictive correction model is also incorporated to compensate for GPS signal delays under dynamic motion, thereby enhancing positional accuracy. The overall algorithm is embedded within an extended Kalman filter framework, which assimilates the corrected GPS observations with a stochastic process noise model to account for dynamic modeling uncertainties. Simulation results using synthetic GPS measurements, including pseudoranges and pseudorange rates from a dual-frequency spaceborne receiver, demonstrate that the proposed method provides a significant improvement in orbit determination accuracy compared to conventional techniques that neglect signal propagation effects. These findings highlight the importance of performing orbit estimation directly in the Earth-centered, Earth-fixed reference frame, utilizing pseudoranges that are corrected for ionospheric errors, applying reduced-dynamic filtering methods, and compensating for signal delays. Together, these enhancements contribute to more reliable and precise satellite orbit determination for missions operating in low Earth orbit. Full article

The spatial variation characteristics of near-fault velocity pulses lack in-depth understanding, and it is difficult to consider this feature in probabilistic seismic hazard analysis and the ground motion input for structural seismic analysis. Based on ground motion simulation, this study performs spatial characteristic analysis of velocity pulses. The Mw 6.58 strike-slip Imperial Valley and the Mw 6.8 dip-slip Northridge earthquakes are adopted as the cases, and the simulation method is validated by comparing synthetics with observations. The multi-component broadband ground motion fields are simulated, and the pulse parameters and the pulse area are extracted using the multi-component pulse identification method. The spatial characteristics of various pulse parameters are analyzed. The results show that for a single earthquake, the pulse period is a spatial variable related to source-to-site geometry, the pulse amplification factor has great spatial variation, and the orientation of the maximum pulse component is controlled by the radiation pattern. Finally, the influence of slip distribution on pulse is explored based on two earthquakes, in which the uniform slip, the random slip, and the hybrid slip are combined with different rupture directions. This study contributes to a more reasonable consideration of pulse-like ground motion in seismic risk assessment and earthquake response analysis. Full article

The effects of a synthetic M 7.1 strike lateral earthquake are evaluated at five sites in Michoacan state, western Mexico. In this work, the ground motion simulation was applied using the empirical Green’s function method proposed by Irikura (1986) by scaling the recordings of an M 5.1 left-lateral event to a hypothetical M 7.1 event assuming the same source mechanism. An M 4.3 was used as a Green’s function to generate an M 5.1 synthetic earthquake. Comparing the observed and synthetic M 5.1 earthquake, parameters were adjusted in order to scale the M 7.1 earthquake. Seven scenarios were tested for which the corresponding PGA and PGV were calculated. The results show that the maximum intensities at each station depend on the proposed rupture starting point. The highest Peak Ground Acceleration was 74.1 cm/s² corresponding to an intensity MMI of V at FMIR station located 60 km from the epicenter. The synthetic results constitute a useful input for seismic hazard studies in a state with poor instrumental deployment, such as Michoacan, and for technical standards for earthquake design that could be considered in the corresponding construction regulations. Full article

Multi-temporal synthetic aperture radar interferometry (MT-InSAR) has evolved from a niche research technique into a powerful global monitoring tool. With the launch of nationwide and continent-wide ground motion services (GMSs), freely available deformation data can now be analyzed on a large scale. However, their applicability for monitoring critical infrastructure, such as dams, has not yet been thoroughly assessed, and several challenges have hindered the integration of MT-InSAR into existing monitoring frameworks. These challenges include technical limitations, difficulties in interpreting deformation results, and the rigidity of existing safety protocols, which often restrict the adoption of remote sensing techniques for operational dam monitoring. This study evaluates the effectiveness of persistent scatterer (PS) data from the German ground motion service (Bodenbewegungsdienst Deutschland, BBD) in complementing time-consuming in situ techniques. By analyzing a gravity dam in Germany, BBD time series were compared with in situ pendulum data. We propose a two-stage assessment procedure: First, we evaluate the dam’s suitability for PS analysis using the CR-Index to identify areas with good radar visibility. Second, we assess the interpretability of BBD data for radial deformations by introducing a novel index that quantifies the radial sensitivity of individual PS points on the dam. This index is universally applicable and can be transferred to other types of infrastructure. The results revealed a fair correlation between PS deformations and pendulum data for many PS points (up to $R^2$ = 0.7). A priori feasibility assessments are essential, as factors such as topography, land cover, and dam type influence the applicability of the PS technique. The dam’s orientation relative to the look direction of the sensor emerged as a key criterion for interpreting radial deformations. For angle differences (${\Delta }_{\mathrm{RAD}}$) of up to 20° between the true north radial angle of a PS point and the satellite’s look direction, the line-of-sight (LOS) sensitivity accounts for approximately 50 to 70% of the true radial deformation, depending on the satellite’s incidence angle. This criterion is best fulfilled by dams aligned in a north–south direction. For the dam investigated in this study, the LOS sensitivity to radial deformations was low due to its east–west orientation, resulting in significantly higher errors (6 mm $\le RMSE\le 43$ mm) compared to in situ pendulum data. Eliminating PS points with an unfavorable alignment with the sensor should be considered before interpreting radial deformations. For implementation into operational monitoring programs, greater effort must be spent on near-real-time updates of BBD datasets. Full article

Ground-based global navigation satellite system (GNSS) and remote sensing interferometric synthetic aperture radar (InSAR) techniques have proven to be very useful for deformation monitoring. GNSS provides high-precision data but only at a limited number of points, whereas InSAR allows for a much denser distribution of measurement points, though only in areas with high and consistent signal backscattering. This study aims to integrate these two techniques to overcome their respective limitations and explore their potential for effective monitoring of critical infrastructure, ensuring the protection of people and the environment. The proposed approach was applied to monitor deformations of the shoulder structures of the MOSE (MOdulo Sperimentale Elettromeccanico) system, the civil infrastructure designed to protect Venice and its lagoon from high tides. GNSS data were collected from 36 continuous GNSS (CGNSS) stations located at the corners of the emerged shoulder structures in the Treporti, San Nicolò, Malamocco, and Chioggia barriers. Velocities from February 2021/November 2022 to June 2023 were obtained using daily RINEX data and Bernese software. Three different processing strategies were applied, utilizing networks composed of the 36 MOSE stations and eight other continuous GNSS stations from the surrounding area (Padova, Venezia, Treviso, San Donà, Rovigo, Taglio di Po, Porto Garibaldi, and Porec). InSAR data were sourced from the European ground motion service (EGMS) of the Copernicus program and the Veneto Region database. Both services provide open data related to the line of sight (LOS) velocities derived from Sentinel-1 satellite imagery using the persistent scatterers interferometric synthetic aperture radar (PS-InSAR) approach. InSAR velocities were calibrated using a reference CGNSS station (Venezia) and validated with the available CGNSS data from the external network. Subsequently, the velocities were compared along the LOS at the 36 CGNSS stations of the MOSE system. The results showed a strong agreement between the velocities, with approximately 70% of the comparisons displaying differences of less than 1.5 mm/year. These findings highlight the great potential of satellite-based monitoring and the effectiveness of combining GNSS and InSAR techniques for infrastructure deformation analysis. Full article

Dynamic rupture simulations of earthquakes offer crucial insights into the physical mechanisms of driving fault slip and seismic hazards. By incorporating non-planar fault models that accurately represent subsurface structures, this study provides a realistic depiction of the rupture processes of the 2020 Mw 6.8 Elazığ, Türkiye earthquake, influenced by geometric complexities. Initially, we determined its coseismic slip on the non-planar fault using near-field strong motion and InSAR observations. Subsequently, we established the heterogeneous initial stress on the fault plane based on the coseismic slip and integrated it into the dynamic rupture modeling to assess physics-based ground motion and seismic hazards. The numerical simulations utilized the curved grid finite-difference method (CGFDM), which effectively models rupture dynamics with heterogeneities in fault geometry, initial stress, and other factors. Our synthetic surface deformation and seismograms align well with the observational data obtained from InSAR and seismic instruments. We observed localized occurrences of supershear rupture during fault propagation. Furthermore, the intensity distribution we simulated closely aligns with the actual observations. These findings highlight the critical role of source heterogeneity in seismic hazard assessment, advancing our understanding of fault dynamics and enhancing predictive capabilities. Full article

In dual-channel synthetic aperture radar (SAR) systems, the estimation of the four-dimensional motion parameters of the ground maneuvering target is a critical challenge. In particular, when spatial degrees of freedom are used to enhance the target’s output signal-to-clutter-plus-noise ratio (SCNR), it is possible to have multiple solutions in the parameter estimation of the target. To deal with this issue, a novel algorithm for estimating the motion parameters of ground moving targets in dual-channel SAR systems is proposed in this paper. First, the random sample consensus (RANSAC) and modified adaptive 2D calibration (MA2DC) are used to prevent the target’s phase from being distorted as a result of channel balancing. To address range migration, the RFRT algorithm is introduced to achieve arbitrary-order range migration correction for moving targets, and the generalized scaled Fourier transform (GSCFT) algorithm is applied to estimate the polynomial coefficients of the target. Subsequently, we propose using the synthetic aperture length (SAL) of the target as an independent equation to solve for the four-dimensional parameter information and introduce a windowed maximum SNR method to estimate the SAL. Finally, a closed-form solution for the four-dimensional parameters of ground maneuvering targets is derived. Simulations and real data validate the effectiveness of the proposed algorithm. Full article

Feature point detection in inverse synthetic aperture radar (ISAR) images of space targets is the foundation for tasks such as analyzing space target motion intent and predicting on-orbit status. Traditional feature point detection methods perform poorly when confronted with the low texture and uneven brightness characteristics of ISAR images. Due to the nonlinear mapping capabilities, neural networks can effectively learn features from ISAR images of space targets, providing new ideas for feature point detection. However, the scarcity of labeled ISAR image data for space targets presents a challenge for research. To address the issue, this paper introduces a self-supervised feature point detection method (SFPD), which can accurately detect the positions of feature points in ISAR images of space targets without true feature point positions during the training process. Firstly, this paper simulates an ISAR primitive dataset and uses it to train the proposed basic feature point detection model. Subsequently, the basic feature point detection model and affine transformation are utilized to label pseudo-ground truth for ISAR images of space targets. Eventually, the labeled ISAR image dataset is used to train SFPD. Therefore, SFPD can be trained without requiring ground truth for the ISAR image dataset. The experiments demonstrate that SFPD has better performance in feature point detection and feature point matching than usual algorithms. Full article