Search Results (181)

Satellite-derived ground deformation monitoring has become an essential tool for infrastructure management; however, the spatial heterogeneity of Interferometric Synthetic Aperture Radar (InSAR) observations limits reliable assessment in regions with sparse measurement coverage. While spatial transfer learning offers a practical means of extending deformation predictions beyond well-observed areas, existing approaches primarily emphasise predictive accuracy and provide little indication of whether transferred predictions can be trusted during operational deployment. This study presents an evidence-based reliability assessment framework for spatial transfer learning using European Ground Motion Service (EGMS) observations. The framework accompanies every prediction with complementary evidence reliability, transfer reliability, and validation-calibrated expected prediction error. The methodology was evaluated within a single 100 km × 100 km EGMS tile in Eastern England, characterised by gradual, subsidence-type ground motion, using 815 EGMS observations and deployed to assess deformation risk for 140 road corridors. The proposed local–transfer fusion achieved a mean RMSE of 1.720 mm yr⁻¹, outperforming multi-source transfer strategies. Feature–space divergence exhibited a significant positive relationship with transfer prediction error ($\rho =0.515$, $p<0.001$), demonstrating that environmental similarity is a stronger indicator of transfer success than structural similarity within the investigated study area. The proposed framework extends conventional transfer learning beyond prediction accuracy and provides a practical foundation for risk-informed infrastructure monitoring using satellite-derived ground deformation observations. Full article

Ground-based optical remote sensing of aerial targets at kilometer-scale standoff distances requires accurate keypoint localization for six-degree-of-freedom (6-DoF) pose recovery under variable illumination, motion blur, and atmospheric degradation. Many lightweight detectors use fixed-kernel convolutions, whose spatially invariant sampling may limit adaptation to heterogeneous target geometries and spatially varying image degradation. We introduce GeoAdapt, a compact keypoint detection framework that inserts deformable convolution v2 (DCNv2) modules between the feature pyramid network (FPN) neck and the detection head. GeoAdapt also replaces the standard object keypoint similarity (OKS) loss with a combination of Wing Loss and Bone Loss. The complete model contains 5.95 M parameters, 47.9% fewer than the You Only Look Once version 8 small pose model (YOLOv8s-pose). On a synthetic ground-based optical remote sensing benchmark, GeoAdapt achieved a percentage of correct keypoints (PCK) at a threshold of 0.05 times the bounding-box diagonal (PCK@0.05D) of 89.3% and a rotation error of 11.6°, improving PCK by 11.7 percentage points over YOLOv8s-pose. Zero-shot evaluation on manually annotated real ScanEagle and Matrice 200 imagery showed consistent advantages over YOLOv8s-pose and YOLO11s-pose in all six test scenarios. A factorial ablation indicated a positive interaction between DCNv2 and the Wing+Bone loss. Full article

Traditional motion compensation (MoCo) methods for high-resolution synthetic aperture radar (SAR) rely on iterative processing between imaging and MoCo, incurring heavy computational overhead and barely meeting fast imaging requirements. To address this, this paper presents a non-iterative SAR autofocus method based on multi-subimage two-dimensional phase gradient autofocus (2D-PGA). Leveraging the parallel imaging capability of the ground Cartesian back-projection (GCBP) algorithm, we reveal an a priori 2D spatially variant spectral structure of GCBP subimage errors, then the proposed 2D-PGA method is utilized to precisely estimate subimage wavenumber-domain errors. Mapping between subimage offsets and linear phase errors is derived for cross-subimage error splicing. The spliced errors are compensated for corresponding subimages to realize autofocus and ensure coherent subimage fusion, enabling non-iterative parallel processing of SAR time-domain imaging and MoCo. Experiments with 0.03 m-resolution Ku-band microwave photonic SAR measured data verify the necessity and effectiveness of the proposed method. Full article

Autonomous vehicle (AV) technologies have advanced rapidly in recent years, driving an increasing demand for large-scale, high-quality annotated data. However, collecting and annotating real-world pedestrian video datasets is time-consuming, costly, and often insufficient to cover rare and safety-critical scenarios. Recent world foundation models have demonstrated impressive capabilities in generating realistic videos, yet their suitability for safety-critical autonomous driving applications remains largely unexplored. In this work, we investigate whether current world foundation models can generate driving scenarios that are sufficiently realistic and behaviourally consistent for autonomous driving research. We conduct a case study centred on pedestrian–vehicle interactions captured from ego-vehicle dashcam viewpoints, where subtle behavioural and geometric errors can have significant safety implications. To support this investigation, we develop SynPeDAS, an open research framework comprising a collection of synthetic pedestrian-interaction videos, a reusable generation pipeline for transforming real-world driving footage into synthetic scenarios, an automated evaluation suite, and downstream demonstration code. Through quantitative evaluation and structured human assessment, we identify several recurring failure modes, including dynamic misalignment, depth drift, and object persistence inconsistencies. More importantly, we find that commonly used evaluation metrics frequently exhibit ceiling effects and weak alignment with human judgement, limiting their ability to detect safety-critical behavioural errors. These findings indicate that, despite high perceptual realism at the frame level, current generative world models and existing evaluation methodologies remain insufficient for capturing physically grounded motion and task-critical semantics. Consequently, significant challenges remain before world model-generated videos can be considered reliable for safety-critical autonomous driving applications. SynPeDAS provides an open platform for systematically studying these challenges and developing improved generation and evaluation methods. Full article

The assessment of seismic performance of existing buildings has traditionally focused on structural safety and damage limitation, often neglecting the explicit quantification of the associated economic consequences. In recent years, performance-based earthquake engineering (PBEE) frameworks have enabled a direct link between structural response and probabilistic loss estimation, allowing economic metrics to be integrated into seismic risk evaluation. Among these, the Expected Annual Loss (EAL) represents a comprehensive indicator that accounts for seismic hazard, structural vulnerability, and exposure over the building’s lifetime. This study presents a performance-based seismic loss assessment of an existing reinforced concrete building, adopting EAL as a global metric for seismic performance evaluation. The case study concerns an existing hospital building designed primarily for gravity loads and representative of a large portion of the Italian building stock. A detailed nonlinear numerical model is developed using OpenSees ver. 3.8.0, incorporating shear-critical behavior through nonlinear link elements. Structural performance is evaluated through modal analysis, pushover analysis, and nonlinear time-history analyses using a set of ground motions selected and scaled according to intensity-based criteria. Seismic losses are estimated following the FEMA P-58 methodology, implemented through the PACT software ver. 3.1.2, integrating structural response demands, component fragility functions, collapse probability, and seismic hazard curves. Probabilistic loss curves are derived, and the EAL is computed as a synthetic indicator of economic seismic performance. The results highlight the effectiveness of EAL in capturing the combined effects of seismic hazard and structural vulnerability, demonstrating its potential as a robust decision-support metric for seismic risk mitigation, retrofit prioritization, and insurance-related applications for existing buildings. Full article

Drone-to-drone (D2D) detection is a critical yet underexplored task in low-altitude intelligent perception, where UAV targets are often small, weakly textured, motion-affected, and disturbed by complex backgrounds and environmental changes. Existing cross-domain detection datasets mainly focus on ground objects or single-factor shifts, making them insufficient for evaluating D2D detection under coupled real-world variations. To address this gap, we present TriCross-D2D, an RGB air-to-air UAV detection dataset and benchmark with three explicit domain shifts: scene, viewpoint, and weather. Built from real flight videos and controlled synthetic fog, TriCross-D2D contains 13 RGB video sequences, 23,403 raw frames, 7045 benchmark images, and 9771 annotated UAV instances. It provides a fixed split of 4045 Source_train images, 2000 Target_train images, and 1000 Target_val images, supporting both unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA). The dataset is dominated by small objects, with extremely tiny, tiny, and small targets accounting for 73.8% of all instances. Benchmark results show that existing cross-domain detectors still perform limitedly on TriCross-D2D, especially under stricter localization and recall metrics. Single-factor analysis further reveals that the coupled scene–viewpoint–weather protocol is more challenging than isolated shifts, with viewpoint variation producing a particularly strong domain gap. As an exploratory enhanced baseline, SCOPE-DA-RTDETR improves DA-RTDETR from 28.63/13.12/22.39 to 29.94/13.71/23.40 in ${\mathrm{AP}}_{50}/{\mathrm{AP}}_{50–95}/\mathrm{AR}$, showing consistent but modest gains. These findings demonstrate that TriCross-D2D provides a challenging and discriminative benchmark for cross-domain D2D small-object detection. Full article

The generation of synthetic seismic accelerograms is a critical problem in earthquake engineering, where the scarcity of strong-motion records, particularly for high-magnitude and near-fault scenarios, limits the reliability of structural analyses and probabilistic seismic hazard assessments. This paper presents a proof-of-concept wavelet-decomposed conditional Generative Adversarial Network (WD-cGAN) for the synthesis of seismic accelerograms that reproduce the physical and statistical properties of real ground-motion records. Unlike prior GAN-based approaches that rely on Fourier-domain decomposition, the proposed architecture decomposes each training signal into N wavelet sub-bands (experimentally $N=7$, six detail sub-bands D1–D6 and one approximation sub-band A6) using the Daubechies-4 (db4) discrete wavelet transform (DWT), assigning each sub-band to a dedicated discriminator. A novel energy-based weighting scheme ${\alpha }_i$ modulates the relative contribution of each discriminator to the total generator loss, ensuring that physically dominant, low-frequency bands, which carry the bulk of seismic energy, receive proportionally higher training emphasis. Seismic moment magnitude $M_w$ serves as the primary conditioning variable, enabling targeted synthesis for specific hazard scenarios. The model is implemented in Python v3.9 using PyTorch v.2.10 and trained on accelerograms drawn from the Italian INGV/ITACA v4.0 archive. Preliminary evaluation on 500 synthetic accelerograms across five magnitude classes provides evidence that the proposed wavelet-domain multi-discriminator scheme reproduces the essential spectral shape and non-stationary temporal structure of real ground-motion records within the considered magnitude range; full quantitative validation on a larger and more diverse corpus, rigorous comparison with competing methods, and extended multi-parameter conditioning are identified as the principal avenues for future work. Full article

Highway slope instability under coupled traffic and environmental loading poses critical threats to transportation safety in mountainous regions, where dynamic vehicular forces interact with complex geological conditions in ways that single-modality monitoring cannot fully resolve. This study proposes MMDF-DEWS, a multi-modal data fusion and deep learning-based early-warning system that, for the first time, treats quantified traffic-loading parameters as a first-class input modality alongside Interferometric Synthetic Aperture Radar (InSAR) displacement, Global Navigation Satellite System (GNSS) measurements, and embedded geotechnical sensor outputs. A hybrid Transformer–bidirectional LSTM backbone with hierarchical attention-guided fusion enables the model to capture both long-range temporal deformation trends and short-term dynamic responses triggered by heavy-vehicle passage. To guard against over-fitting on a limited number of instability events, we adopt chronological training/validation/test partitioning, five-fold cross-validation for hyper-parameter selection, stratified focal-loss training, and cross-dataset evaluation on two independent public benchmarks: the Three Gorges Reservoir Area Landslide Monitoring Dataset (TGRA-LMD) and the European Ground Motion Service Sentinel-1 (EGMS-S1) dataset. The framework outperforms six state-of-the-art baselines by 4.7–11.2% in F1-score, and ablation studies confirm that the explicit inclusion of traffic-loading features alone improves Warning-class recall by 6.3 percentage points, demonstrating a direct and physically grounded link between cyclic vehicular loading and slope-state prediction. The system satisfies operationally relevant engineering targets for warning lead time and false-alarm rate, and provides interpretable attention maps suitable for transportation-authority decision support. Full article

Collision detection in dynamic environments demands perception systems that are both computationally efficient and robust to diverse motion patterns. Biological vision systems, particularly those of insects, offer efficient neural architectures capable of rapid motion interpretation under strict resource constraints. This work presents a systematic comparative evaluation of three biologically inspired models: the Lobula Giant Movement Detector (LGMD), the Elementary Motion Detector (EMD), and a hybrid Spiking Neural Network (SNN) incorporating LGMD and EMD-derived motion processing pathways, evaluated on programmatically generated synthetic stimuli with frame-level ground truth. The hybrid SNN achieved an accuracy of 73–87% across stimulus types, consistently exceeding the 75.0% held-out test set baseline, with a precision of 1.0 throughout and a substantially lower runtime than the LGMD implementation. LGMD demonstrated rate-based sensitivity consistent with biological spike-frequency adaptation, while the EMD correctly produced near-zero responses to looming stimuli, confirming its role as a directional rather than collision detector. These results demonstrate that hybridising biologically inspired motion detectors within a trainable spiking framework produces a promising and reproducible approach to collision prediction, while identifying the sim-to-real generalisation gap as a key challenge for future deployment. Full article

In video synthetic aperture radar (Video SAR), target motion causes defocusing, making it impossible to determine the target’s real-time position using reflected echoes. However, the shadows formed by the target occluding ground reflections can accurately characterize the target’s real-time position. To address challenges such as varying shadow scales, low contrast with the moving background, and susceptibility to clutter interference, this paper proposes a shadow detection network called DSENet to enhance the detail and semantic features of shadows. First, to enhance shadow features and reduce sampling loss during backbone network feature extraction, we design a detailed information enhancement (DIE) module to achieve lossless downsampling and effectively preserve the detailed features of the shadowed target. Second, we propose a semantic spatial feature aggregation (SSFA) module to enhance global semantic space feature extraction, improve the contextual feature representation of the target’s shadow region, and provide robust semantic space prior information for the model. Finally, we designed a detailed semantic fusion (DSF) module to improve the neck network’s ability to fuse shadow details and semantic features in video SAR images, further enhancing the model’s localization performance for target shadow features and achieving accurate localization of moving targets in video SAR. Comparative and ablation experiments validate the effectiveness and superiority of the proposed method. Experimental results on the Sandia National Laboratories (SNL) public dataset demonstrate that DSENet is efficient and performs excellently, achieving a P of 92.4% and an F1 score of 83.1%. Full article

Top-heavy industrial towers, which carry large, concentrated masses of equipment at upper levels and feature open lower stories, are vertically irregular by design and tend to amplify seismic displacement and acceleration demands near the tower top. Although tuned mass dampers (TMDs) have been studied extensively for buildings, bridges and chimneys, their application to this particular class of slender industrial towers—where production-equipment vibration tolerance, retrofit accessibility and limited downtime drive the design—has received little dedicated attention. This paper reports a focused numerical investigation of seismic response mitigation for a 101.2 m molten-asphalt granulation tower retrofitted with a single pendulum-type TMD. A three-dimensional coupled finite element (FE) model was constructed in ABAQUS using C3D8R solid elements for the reinforced-concrete shaft and T3D2 truss elements for the embedded reinforcement; modal analysis returned a fundamental frequency of 0.912 Hz and a torsional-to-translational period ratio of 0.65, indicating a translational-mode-dominated response. Elastic time-history analyses under the El Centro and Taft records together with a code-spectrum-compatible synthetic accelerogram show that a pendulum TMD with mass ratio μ = 2.5%, tuning frequency offset Δf = 5% and damping ratio ξ = 10%—installed at the uppermost equipment level guided by the modal-displacement criterion—reduces the peak top displacement, peak top acceleration and peak base shear by roughly 23%, 23% and 22%, respectively, in both principal directions. The controlled top acceleration falls comfortably below the 2.94 m/s² operational tolerance of the on-tower melting equipment. To address the rationality of the chosen TMD parameters, a single-variable parametric sensitivity study spanning μ ∈ [1%, 5%], ξ ∈ [5%, 15%] and Δf ∈ [0%, 10%] is performed on an equivalent reduced model that captures the qualitative parameter-response trends; the chosen baseline values lie inside a stable performance plateau and are shown to be a balanced compromise among the three response measures. The principal contribution of the work is, therefore, (i) a complete TMD retrofit framework—modal-based placement, parameter design, coupled FE assembly and multi-record verification—adapted to top-heavy industrial towers, and (ii) qualitative evidence, supported by a sensitivity scan, with a robust proposed parameter set for small-to-moderate detuning. The study is restricted to elastic time-history analyses under frequent-earthquake-level excitation, three ground-motion records and a fixed-base assumption; nonlinear response, larger record sets and soil–structure interaction effects are explicitly identified as scope limitations and are left for follow-up work. Full article

Earthquake Early Warning Systems (EEWS) represent one of the most effective technological solutions for mitigating the impacts of strong ground motion in seismically active regions. This study presents the design, implementation, and comprehensive evaluation of a real-time earthquake early warning system for Izmir-a region in Western Anatolia characterized by complex tectonic structures and high seismic hazard-using multi-station seismic acceleration data. The proposed framework integrates multi-threaded data acquisition, signal preprocessing, Min-Max normalization, and Euclidean distance-based similarity analysis to enable rapid detection of anomalous seismic patterns during the early P-wave phase. The system architecture consists of distributed sensor inputs, centralized real-time processing, similarity-based anomaly detection, and user-oriented visualization and alerting modules. The performance of the system was evaluated using both real and synthetic seismic datasets. Instrumental earthquake catalog from the 12 June 2017 Karaburun (Mw 6.2) and 30 October 2020 Samos (Mw 6.6) earthquakes demonstrate that the system can generate early warnings 18 s and 13 s prior to strong ground shaking, respectively. In addition, synthetic seismic scenarios were employed to assess system robustness under varying noise levels, station configurations, and signal conditions. The results indicate that the proposed framework maintains stable detection performance and low false-positive rates across diverse operational scenarios. The methodology emphasizes computational efficiency and inter-station waveform coherence analysis, providing a lightweight alternative to conventional magnitude-based approaches. By avoiding computationally intensive source inversion, the system achieves low-latency performance while preserving detection reliability. The proposed EEWS demonstrates strong generalization capability, scalability, and practical applicability for real-time deployment in earthquake-prone urban environments. Full article

Accurate absolute radiometric calibration is critical for ensuring the data quality of spaceborne Synthetic Aperture Radar (SAR) systems and supporting quantitative remote sensing applications. Absolute radiometric calibration generally relies on ground reference targets with known radar cross-section (RCS) deployed at dedicated calibration sites. Such ground-based calibration methods are costly and time-consuming, and calibration frequency is constrained by the distribution of calibration sites and the satellite revisit cycles. Additionally, for specialized SAR missions, such as deep space exploration, deploying calibration equipment on the observed extraterrestrial surface is infeasible. This study proposes a space-based absolute calibration concept using a small calibration satellite carrying a well-characterized reference (e.g., a passive reflector or an active transponder) and flying in formation with the SAR satellite. The relative motion ensures a side-looking acquisition geometry, enabling the SAR to image the accompanying target and derive calibration factors. The overall calibration process is divided into two stages: determination of an in-orbit calibration factor using the calibration satellite, followed by its transformation to accommodate ground imaging conditions. This method effectively isolates the radar system gain to characterize the intrinsic hardware response. Furthermore, by operating entirely in space, it avoids atmospheric and ground-clutter distortions, ensuring a fully space-based, end-to-end calibration process dominated primarily by sensor systematic errors. Moreover, it allows for more frequent and flexible calibration, eliminating reliance on ground calibration sites and infrastructure. The feasibility and advantages of the proposed concept are demonstrated through comprehensive simulations, covering orbit analysis, echo simulation, and image processing. Full article

The advancement of on-orbit servicing and space debris removal missions has established high-precision visual perception for non-cooperative spacecraft as a key research focus. However, the availability of high-quality, diverse spacecraft image datasets is severely limited due to extreme on-orbit imaging conditions, data confidentiality, and morphological diversity of targets, significantly constraining the advancement of data-driven algorithms in this domain. To address this challenge, we propose a relative orbital motion-guided framework for generating multimodal visual data of spacecraft. The proposed method integrates an orbital dynamics model into the synthetic data generation pipeline to simulate typical relative motion patterns between the camera and the target in a realistic orbital environment, thereby generating image sequences characterized by continuous spatiotemporal evolution. Targeting four representative spacecraft—Tiangong, Spacedragon, ICESat, and Cassini—this work simultaneously generates a dataset comprising 8000 samples, each containing four strictly aligned modalities: RGB images, instance segmentation masks, depth maps, and surface normal maps, along with precise 6-degree-of-freedom (6-DoF) pose ground truth. Furthermore, an end-to-end physical image degradation model is developed to accurately simulate the complete imaging chain—from optical diffraction and aberrations to sensor sampling and noise—thereby effectively narrowing the domain gap between synthetic and real data. By addressing three key aspects—physical motion modeling, synchronous multimodal ground truth, and imaging degradation simulation—this work provides a crucial data foundation for training, testing, and validating data-driven on-orbit perception algorithms. Full article

This study examines residual low-order (linear and constant) phase errors in interferometric synthetic aperture radar (InSAR) when compact, high-frequency radar sensors are mounted on commercial uncrewed aerial vehicles (UAVs). Although higher carrier frequencies and shorter standoff ranges enable fine-resolution interferometry, the same characteristics—together with UAV platform instability—make the system highly vulnerable to motion-induced phase errors, which can significantly degrade or even invalidate DEM reconstruction. This paper first quantifies the admissible motion-error bounds for reliable multi-baseline phase-gradient estimation, and then introduces a post-autofocus correction scheme that estimates the residual linear term from the interferometric fringe frequency and refines it via an FFT-based correlation objective, while the constant term is calibrated using ground control points (GCPs). The method is validated through simulations of a 24 GHz UAV demonstrator. To the best of our knowledge, this work provides the first post-autofocus demonstration of linear-and-constant residual-error mitigation for UAV-based high-frequency multi-baseline InSAR. In the considered K-band setting, the proposed approach reduces the DEM error from 42 m to 0.2 m (≈98% improvement). Full article