Search Results (43)

Historic building documentation requires both complete spatial coverage and reliable geometric detail, but a single surveying technique often cannot meet both requirements in complex heritage scenes. This study proposes a robustness-oriented TLS–UAV point cloud registration and fusion workflow for historic building documentation. The workflow combines feature-based coarse registration with an improved point-to-plane ICP strategy incorporating normal consistency, radiometric correspondence filtering, dynamic distance thresholds, and multi-resolution refinement. The method was evaluated using the Yao’an Lu Junmin Zongguan Fu, a timber–brick courtyard complex in Yunnan, China, under small, medium, and large initial perturbations. Under small and medium perturbations, Point-to-Plane ICP achieved lower RMSE values, while the proposed method produced comparable results. Under large perturbation, the proposed method achieved the highest success rate and the lowest RMSE of 119.0 cm, indicating stronger robustness under challenging initialization. The fused TLS–UAV model achieved checkpoint-based RMSE values of 1.73 cm horizontally and 0.75 cm vertically. Spatial deviation maps showed that residual errors were mainly concentrated around roof edges, eaves, wall corners, and roof–facade transition zones. Cross-scene validation on the Church of Agios Mamas dataset achieved a registration RMSE of 1.2 cm without parameter adjustment. The results show that the proposed workflow is suitable for offline conservation-oriented documentation where registration robustness, model completeness, and component-level geometric interpretation are required. Full article

Objectives: To assess the association of structural biomarkers derived from optical coherence tomography (OCT) and choriocapillaris (CC) flow information with point-wise retinal sensitivity (PWS) measured by microperimetry (MP) in intermediate age-related macular degeneration (iAMD). Methods: Patients with iAMD received imaging with spectral-domain (SD)-OCT (Spectralis, Heidelberg Engineering) and OCT-angiography (OCT-A) (PLEX Elite 9000, ZEISS). In addition, MP examinations in photopic setting (MP-3, NIDEK) and mesopic background illumination (MAIA2, ICare) were performed. The thickness of the ellipsoid-zone (EZ) and the outer nuclear layer (ONL), as well as the volume of drusen and HRF, were segmented using deep-learning (DL)-based approaches. CC flow deficit percentage (FD%) was extracted from OCT-A slabs using a novel binarization method. Semiautomatic co-registration of MP examinations, OCT-A slabs, and OCT volumes was performed. Three exploratory models were calculated using multivariable mixed-effects models: (1) structure–function (SF) using structural OCT biomarkers, (2) flow–function (FF) utilizing OCT-A derived flow information, and (3) structure–flow–function (SFF) incorporating both OCT and OCT-A data. Model performance was evaluated using AIC and BIC criterion. Results: 19 eyes of 19 patients were evaluated, totalling 3297 MP-stimuli, 1873 B-scans, and 19 OCT-A slabs. Mean (SD) age was 76 (7) years, and sensitivity was 26.0 (3.36) dB in the MP-3 and 22.42 (3.64) dB in the MAIA2. Mesopic MAIA2 examinations showed significantly lower PWS values (−3.56 to −3.63 dB; p < 0.001). Drusen and HRF volume decreased PWS (−0.6 [95% CI: −1.04; −0.16] dB/nL; p = 0.007 and −9.56 [95% CI: −12.86; −6.26] dB/nL; p < 0.001), while ONL was positively associated with PWS (0.06 [0.05; 0.07] at an eccentricity of 5.2°; p < 0.001) in the SF model. CC FD% was not significantly associated with PWS in the FF and the SFF model (p > 0.05 in both cases). In the SFF model drusen volume (−1.69 [95% CI: −2.09; −1.29] dB/nL; p < 0.001), EZ (0.04 [95% CI: 0.02; 0.06] dB/µm; p < 0.001), and ONL thickness (0.03 [95% CI: 0.02; 0.04] dB/µm; p < 0.001) were significant predictors for PWS. The SF model exhibited the lowest AIC and BIC indicating best model performance. Conclusions: Structural parameters derived from SD-OCT such as HRF, drusen volume, and outer retinal layer thickness may be more closely associated with PWS, with CC FD% as an OCT-A-derived metric contributing limited additional explanatory benefit in cross-sectional analyses. Full article

This study presents a modular diagnostic framework for evaluating thermal degradation in aging building envelopes by integrating infrared thermography, panoramic reconstruction, and deep learning-based semantic segmentation into a unified workflow. The methodology combines image registration, panoramic synthesis, façade component segmentation, and quantitative surface temperature analysis to provide scalable and reproducible diagnostics. By excluding fenestration zones—where infrared measurements are physically unreliable—the framework focuses on opaque wall regions and window surroundings to ensure physically meaningful evaluation. Field validation was conducted on a multi-story office building constructed in 1996. The diagnostic indicators revealed a mean wall surface temperature of 14.3 °C with a standard deviation of 5.6 °C, and a temperature factor ranging from 0.67 to 0.78 under measured conditions. The vulnerable area ratio reached 9.1% for walls, while window areas showed greater vulnerability at 12.74%, with anomalies concentrated at frame–glass interfaces and perimeter seals. These quantitative results confirmed the framework’s ability to detect thermal irregularities and visualize localized anomalies. More importantly, the contribution of this study lies in establishing a systematic and extensible diagnostic pipeline that advances building envelope analysis, supporting large-scale energy audits, retrofit prioritization, and sustainable building management. Full article

Aiming at the problems of insufficient environmental perception capability of autonomous mobile robots and low multi-modal data fusion efficiency in the complex underground coal mine environment featuring low illumination, high dust, and dynamic obstacles, a reliable passable region identification method for autonomous mobile robots operating in underground coal mine is proposed in this paper. Through the spatial synchronous installation strategy of dual 4D millimeter-wave radars and dynamic coordinate system registration technology, it increases point cloud density and effectively enhances the spatial characterization of roadway structures and obstacles. Combining the characteristics of infrared thermal imaging and the penetration advantage of millimeter-wave radar, a multi-modal data complementary mechanism based on decision-level fusion is proposed to solve the perceptual blind zones of single sensors in extreme environments. Integrated with lightweight model optimization and system integration technology, an intelligent environmental perception system adaptable to harsh working conditions is constructed. The experiments were carried out in the simulated tunnel. The experiments were carried out in the simulated tunnel. The experimental results indicate that the robot can utilize the data collected by the infrared camera and the radar to identify the specific distance to obstacles, and can smoothly achieve the recognition and marking of passable areas. Full article

Urban heritage documentation often separates 3D data acquisition from immersive interaction, limiting both accuracy and user impact. This study develops and validates an end-to-end workflow that integrates UAV photogrammetry with terrestrial LiDAR and deploys the fused model in a VR environment. Applied to Piazza Vittorio Emanuele II in Rovigo, Italy, the approach achieves centimetre-level registration, completes roofs and upper façades that ground scanning alone cannot capture, and produces stable, high-fidelity assets suitable for real-time interaction. Effectiveness is assessed through a three-layer evaluation framework encompassing vision, behavior, and cognition. Eye-tracking heatmaps and scanpaths show that attention shifts from dispersed viewing to concentrated focus on landmarks and panels. Locomotion traces reveal a transition from diffuse roaming to edge-anchored strategies, with stronger reliance on low-visibility zones for spatial judgment. Post-VR interviews confirm improved spatial comprehension, stronger recognition of cultural values, and enhanced conservation intentions. The results demonstrate that UAV-enabled completeness directly influences how users perceive, navigate, and interpret heritage spaces in VR. The workflow is cost-effective, replicable, and transferable, offering a practical model for under-resourced heritage sites. More broadly, it provides a methodological template for linking drone-based data acquisition to measurable cognitive and cultural outcomes in immersive heritage applications. Full article

Approaches to magnetotelluric monitoring of variations in apparent resistivity and electromagnetic emission that may serve as earthquake precursors are considered. Monitoring of apparent resistivity is advised in the range 7–300 Hz, where natural electromagnetic fields exhibit stable behavior, while at lower frequencies the behavior of the electrotelluric and magnetic fields should be analyzed. We present results of studies aimed at identifying active faults and searching for stress–strain sensitive zones for installing measurement equipment based on the registration of tidal variations in apparent resistivity. The features of apparent resistivity anomalies preceding earthquakes in China based on direct current measurements are discussed. Based on the analysis of natural electromagnetic field monitoring in the ULF and ELF ranges in China, the anomalies recorded prior to several recent earthquakes are considered. Before the Yangbi earthquake (2017) and the series of Yangbi (2021) and Ninglang (2022) earthquakes, variations in apparent resistivity were observed that have a pulsed behavior and probably are manifestations of electromagnetic emission. Possible sources of these anomalies are active faults located near the monitoring stations. Full article

The proliferation of small, agile unmanned aerial vehicles (UAVs) has exposed the limits of single-sensor surveillance in cluttered airspace. We propose an Internet of Things-enabled integrated sensing and communication (IoT-ISAC) framework that converts cellular base stations into cooperative, edge-intelligent sensing nodes. Within a four-layer design—terminal, edge, IoT platform, and cloud—stations exchange raw echoes and low-level features in real time, while adaptive beam registration and cross-correlation timing mitigate spatial and temporal misalignments. A hybrid processing pipeline first produces coarse data-level estimates and then applies symbol-level refinements, sustaining rapid response without sacrificing precision. Simulation evaluations using multi-band ISAC waveforms confirm high detection reliability, sub-frame latency, and energy-aware operation in dense urban clutter, adverse weather, and multi-target scenarios. Preliminary hardware tests validate the feasibility of the proposed signal processing approach. Simulation analysis demonstrates detection accuracy of 85–90% under optimal conditions with processing latency of 15–25 ms and potential energy efficiency improvement of 10–20% through cooperative operation, pending real-world validation. By extending coverage, suppressing blind zones, and supporting dynamic surveillance of fast-moving UAVs, the proposed system provides a scalable path toward smart city air safety networks, cooperative autonomous navigation aids, and other remote-sensing applications that require agile, coordinated situational awareness. Full article

Significant morphological transformations resulting from open-pit mining activities always present major problems with site safety and slope stability. This study investigates an active marble quarry in Dinar, Türkiye by combining geospatial analysis and photogrammetry based on unmanned aerial vehicles (UAV). Acquired in 2024 and 2025, high-resolution images were combined with dense point clouds produced by Structure from Motion (SfM) methods. Iterative Closest Point (ICP) registration (RMSE = 2.09 cm) and Multiscale Model-to-Model Cloud Comparison (M3C2) analysis was used to quantify the surface changes. The study found a volumetric increase of 7744.04 m³ in the dump zones accompanied by an excavation loss of 8359.72 m³, so producing a net difference of almost 615.68 m³. Surface risk factors were evaluated holistically using a variety of morphometric criteria. These measures covered surface variation in several respects: their degree of homogeneity, presence of any unevenness or texture, verticality, planarity, and linearity. Surface variation > 0.20, roughness > 0.15, and verticality > 0.25 help one to identify zones of increased instability. Point cloud modeling derived from UAVs and GIS-based spatial analysis were integrated to show that morphological anomalies are spatially correlated with possible failure zones. Full article

The periodic rotation of the Ocean Color and Temperature Scanner (OCTS) introduces jitter errors in the HaiYang-3A (HY-3A) satellite, leading to internal geometric distortion in optical imagery and significant registration errors in multispectral images. These issues severely influence the application value of the optical data. To achieve near real-time compensation, a novel jitter error estimation and correction method based on multi-source attitude data fusion is proposed in this paper. By fusing the measurement data from star sensors and gyroscopes, satellite attitude parameters containing jitter errors are precisely resolved. The jitter component of the attitude parameter is extracted using the fitting method with the optimal sliding window. Then, the jitter error model is established using the least square solution and spectral characteristics. Subsequently, using the imaging geometric model and stable resampling, the optical remote sensing image with jitter distortion is corrected. Experimental results reveal a jitter frequency of 0.187 Hz, matching the OCTS rotation period, with yaw, roll, and pitch amplitudes quantified as 0.905”, 0.468”, and 1.668”, respectively. The registration accuracy of the multispectral images from the Coastal Zone Imager improved from 0.568 to 0.350 pixels. The time complexity is low with the single-layer linear traversal structure. The proposed method can achieve on-orbit near real-time processing and provide accurate attitude parameters for on-orbit geometric processing of optical satellite image data. Full article

Mechanically scanning LiDAR imaging sensors are abundantly used in applications ranging from basic safety assistance to high-level automated driving, offering excellent spatial resolution and full surround-view coverage in most scenarios. However, their complex optomechanical structure introduces limitations, namely limited mounting options and blind zones, especially in elongated vehicles. To mitigate these challenges, we propose a distributed Time-of-Flight (ToF) sensor system with a flexible hardware–software architecture designed for multi-sensor synchronous triggering and fusion. We formalize the sensor triggering, interference mitigation scheme, data aggregation and fusion procedures and highlight challenges in achieving accurate global registration with current state-of-the-art methods. The resulting surround view visual information is then applied to Spiking Neural Network (SNN)-based object detection and probabilistic occupancy grid mapping (OGM) for enhanced environmental awareness. The proposed system is demonstrated on a test vehicle, achieving coverage of blind zones in a range of 0.5–6 m with a scalable and reconfigurable sensor mounting setup. Using seven ToF sensors, we can achieve a 10 Hz synchronized frame rate, with a 360° point cloud registration and fusion latency below 40 ms. We collected real-world driving data to evaluate the system, achieving 65% mean Average Precision (mAP) in object detection with our SNN. Overall, this work presents a replacement or addition to LiDAR in future high-level automation tasks, offering improved coverage and system integration. Full article

Responding to cost inefficiency in the Indonesian cadastral mapping system, this study aimed to analyze the implementation of integrated mapping activities, namely complete systematic land registration, assessing land value zones, and regional land stewardship balance. This study employed an operational management system, particularly focusing on financial aspects, using data envelopment analysis (DEA), a non-parametric technique for evaluating the relative efficiency of decision-making units. These approaches are rarely explored in cadastral mapping. DEA was used to analyze the efficiency of seven aspects: aerial mapping, office supplies, meetings, consumption, transportation, capital expenses, and socialization. Content analysis was used to identify integration parameters derived from operational management-based integration. Cronbach’s alpha was used for the reliability test. The Way Sulan sub-district of South Lampung Regency in Lampung Province, Indonesia, was selected as the study area due to its complete mapping activities. The findings suggested that applying operational management for integrated cadastral mapping is effective. However, contrary to expectations, efficiency was lower in dense urban areas, where costs tend to be cheaper, while efficiency was higher in agricultural areas, where expenses were much greater. Based on this study, an operational management approach to integrated cadastral mapping is recommended to improve budget efficiency and general standards of land management, especially in areas with complex land use. Full article

Background: Pharmacoresistant epilepsy affects approximately one-third of all epilepsy patients, and resective surgery may offer favorable outcomes for carefully selected patients with focal epilepsy. The accurate identification of the epileptogenic zone (EZ) is essential for successful surgery, particularly in cases where non-invasive diagnostics are inconclusive. Invasive diagnostics with stereoelectroencephalography (SEEG) offer a reliable approach to localizing the EZ, especially in MRI-negative cases. Methods: This retrospective study analyzed the data of 22 patients with pharmacoresistant epilepsy who underwent frameless stereotactic SEEG electrode implantation with automated CT-based registration between September 2016 and November 2024. For measuring accuracy, Euclidean distance, radial deviation, angular deviation, and depth deviation were calculated for each electrode. Results: A total of 153 depth electrodes were implanted, targeting various cortical regions. The median Euclidean distance at the entry point was 1.54 mm (IQR 1.31), with a radial deviation of 1.33 mm (IQR 1.32). At the target level, the median Euclidean distance was 2.61 mm (IQR 1.53), with a radial deviation of 1.67 mm (IQR 1.54) and depth deviation of 0.95 mm (IQR 2.43). Accuracy was not significantly affected by electrode order, anatomical location, skull thickness, or intracranial length. Conclusions: These findings demonstrate that frameless stereotactic SEEG electrode implantation is safe and feasible for identifying the EZ. The integration of automatic intraoperative CT-based registration ensures precision. While maintaining workflow efficiency, it achieves accuracy comparable to frame-based methods. Further studies with larger cohorts are warranted to validate these results and assess their impact on surgical outcomes. Full article

The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing—questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been the focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UASs), are providing more and more reliable tools that can be applied to achieve better and higher-quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study follows the understanding that correct SUHI research depends on a proper understanding of the urban geosystem, its spatial and structural heterogeneity, and its functional systems, which in turn can only be achieved by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using local climate zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria’s capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024. Full article

Background: Epilepsy surgery for extratemporal lobe epilepsy (ETLE) is challenging, particularly when MRI findings are non-lesional and seizure patterns are complex. Invasive diagnostic techniques are crucial for accurately identifying the epileptogenic zone and its relationship with surrounding functional tissue. Microscope-based augmented reality (AR) support, combined with navigation, may enhance intraoperative orientation, particularly in cases involving subtle or indistinct lesions, thereby improving patient outcomes and safety (e.g., seizure freedom and preservation of neuronal integrity). Therefore, this study was conducted to prove the clinical advantages of microscope-based AR support in ETLE surgery. Methods: We retrospectively analyzed data from ten patients with pharmacoresistant ETLE who underwent invasive diagnostics with depth and/or subdural grid electrodes, followed by resective surgery. AR support was provided via the head-up displays of the operative microscope, with navigation based on automatic intraoperative computed tomography (iCT)-based registration. The surgical plan included the suspected epileptogenic lesion, electrode positions, and relevant surrounding functional structures, all of which were visualized intraoperatively. Results: Six patients reported complete seizure freedom following surgery (ILAE 1), one patient was seizure-free at the 2-year follow-up, and one patient experienced only auras (ILAE 2). Two patients developed transient neurological deficits that resolved shortly after surgery. Conclusions: Microscope-based AR support enhanced intraoperative orientation in all cases, contributing to improved patient outcomes and safety. It was highly valued by experienced surgeons and as a training tool for less experienced practitioners. Full article

An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high complexity of detection results, low computational efficiency, and high load in traditional obstacle detection methods. Firstly, an NDT registration method which uses the likelihood function as the optimal value of the registration score function to calculate the registration parameters is designed to match the scanning point cloud and the target point cloud. Secondly, a target reduction method combined with threshold judgment and the binary tree search algorithm is designed to filter the point cloud of non-road obstacles to improve the processing speed of the computing platform. Meanwhile, KD-tree is used to speed up the clustering process. Finally, a vehicle remote control simulation platform with the combination of a cloud platform and mobile terminal is designed to verify the effectiveness of the strategy in practical application. The results prove that the proposed obstacle detection method can improve the efficiency and accuracy of detection. Full article