Search Results (1,174)

The external spatial form and skyline of high-density waterfront iconic building clusters in super-large cities are the most distinctive features of urban image. However, traditional static research methods (such as fixed-point photography) cannot capture the continuous visual experience of people in motion, thereby imposing obvious limitations. This study proposes a dynamic visual quantification method that constructs a linear observation path using the parametric platform Grasshopper. The method calculates two core parameters in real-time: the vertical perspective angle (θ, reflecting the building’s “sense of height”) and the horizontal perspective angle (β, reflecting the “sense of density” of the building cluster), so as to realize the dynamic and continuous quantification of the building cluster’s form. Using Shanghai Lujiazui as a case study, this paper validates the method’s effectiveness. The results show that the visual perception of buildings is not only determined by their absolute height but also influenced by the distance from the observation point and spatial relationships. Furthermore, through variance analysis and an annealing algorithm, this study can identify “visually stable points” (suitable for arranging core landmarks) and “optimal viewing points” (suitable for setting up urban viewing platforms). This method provides a reproducible quantitative tool and specific guidance for the optimization of waterfront building layouts and the planning of urban viewing platforms. Full article

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments. Full article

Validating satellite-derived sea surface temperature (SST) requires resolving spatial and vertical mismatches between remotely sensed measurements and traditional in situ observations. This study evaluates the bias between infrared-based satellite SST and high-resolution in situ measurements collected in the North Sea using the autonomous surface vehicle (ASV) HALOBATES. The ASV enables the direct sampling of the ocean skin layer via a rotating glass disc system, alongside near-surface layer (NSL, 1 m depth) measurements using a flow-through system. Across 37 missions conducted between 2022 and 2023, we quantified biases in our approach and performed match-ups with a level-4 SST product for the North and Baltic Seas. Satellite SST showed strong correlations with in situ observations (r > 0.98), with Deming regression slopes approaching unity for all platforms. Despite this agreement, satellite SST exhibited a consistent cold bias. The mean differences were −0.44 ± 0.60 °C for the skin layer and −0.40 ± 0.52 °C for the NSL. The RMSE values were 0.75 °C for the skin layer and 0.66 °C for the NSL, indicating that satellite SST more closely reflects temperatures at 1 m than those at the skin layer. These findings highlight the importance of depth-resolved in situ measurements for improving remote SST validation. Full article

Accurate deformation monitoring is essential for ensuring the stability of deep vertical shafts. In this study, a temperature-compensated fiber Bragg grating (FBG) sensing system was deployed in the 882 m deep Guotun Coal Mine shaft to measure circumferential and vertical strains at six depths. A site-specific mechanical model integrating stratigraphy, dual-layer concrete lining, and the influence radius was developed to analyze shaft wall stresses. The monitoring results reveal pronounced spatial anisotropy, with circumferential compressive and tensile strains at deeper levels nearly twice those at shallow levels. Strain variation also increases over time, reflecting the combined effects of groundwater fluctuations and overburden consolidation. The stresses inferred from measured strains agree well with the analytical solution in both magnitude and depth-dependent trend, with deviations remaining within a reasonable engineering margin. All stresses are below the strength limits of the C70/C50 concrete lining, confirming that the shaft is in a safe stress state. The proposed monitoring–analysis framework provides a reliable basis for evaluating shaft wall behavior under complex hydrogeological conditions. Full article

Autonomous mowers’ navigation pattern plays a crucial role in turfgrass quality, influencing both esthetic and functional performance. However, despite extensive research on mowing efficiency, the effects of different navigation patterns on turfgrass damage and visual quality remain inadequately investigated. This study aimed to evaluate the impact of three different autonomous mower navigation patterns (random, vertical, and chessboard) on operational performance and the effect of trampling activity on turfgrass. Each pattern was tested in terms of data on the number of passages, distance traveled (m), number of intersections and the percentage of area mowed using a remote sensing system and an updated custom-built software. Green coverage percentage was assessed weekly using image analysis (Canopeo app) to evaluate the turfgrass green coverage. The green coverage percentage, together with the number of passages, is analyzed and correlated. The random pattern generated the highest number of passages and intersections, leading to lower average green coverage (64%) compared with the chessboard (80%) and vertical (81%) patterns. Data of the green coverage percentage in the function of the average number of passages recorded using the custom-built software for each pattern fit the asymptotic regression model. The effective number of passages to reach 60% green cover (EP60) was 56.26, 87.30, and 155.32 for random, vertical, and chessboard, respectively. The model could be integrated into DSS, useful for the end user in turf management in order to maintain a high quality. Future studies should extend this approach to other species and environmental conditions, integrating the effective dose (in terms of passages) method for smart mowing management. Full article

Reliable in situ quantification of interlayer mechanics in CRTS-II ballastless track slabs remains limited by the poor instrumentability of the CA mortar layer. This study implements a quasi-distributed fiber-optic sensing scheme by encapsulating FBGs in PVC conduits and embedding them within the CA mortar to track strain evolution under vertical loading. Four 1:3 scaled slabs were tested using stepwise load control (200 kN per step) to failure, and fiber measurements were cross-validated against conventional strain gauges on the reinforcement. The two systems showed consistent load–strain trends, while the fiber approach exhibited near-zero baseline offset and higher temporal resolution, enabling detection of small-amplitude strain changes that the gauges missed. The CA mortar displayed a clear tension-to-compression transition with increasing load; with two vertical rebars the ultimate load of the mortar layer reached 1400 kN, representing a 75% improvement over the rebar-free configuration and delaying compressive crushing through enhanced interlayer cooperation. Increasing the rebar diameter further restrained deformation and elevated the load level at which the transition occurred. The results demonstrate a practical interlayer monitoring route for CA mortar and quantify the strengthening role of vertical rebars, offering actionable guidance for design optimization and long-term condition assessment of CRTS-II slab tracks. Full article

The vertical attenuation coefficient of photosynthetically active radiation ($K_d\left(PAR\right)$) is essential for characterizing the underwater light field and for operational marine monitoring. Although there have been efforts to use the standard satellite light attenuation product at 490 nm ($K_d\left(490\right)$) to estimate ($K_d\left(PAR\right)$) over a decade, earlier approaches were constrained by limited data. This study used a globally representative robust database of in-situ and satellite observations spanning diverse marine optical conditions and applied rigorous quality control. Three empirical models (linear, power, and a higher-order polynomial) were developed using four $K_d\left(490\right)$ satellite variants validated against an independent dataset and benchmarked against six published algorithms (36 total approximations). Performance was assessed using a Model Performance Index (MPI), where values closer to 1 indicate a better model. The best model was a power regression driven by the standard satellite $K_d\left(490\right)$, which yielded an MPI of 0.8704, indicating a robust performance under a wide variability of marine optical conditions. These results highlight the value of multisensor products, which with a rigorous quality control protocol, could be used to estimate the $K_d\left(PAR\right)$ from the standard satellite $K_d\left(490\right)$. The objective of the proposed algorithm is to generate long-term $K_d\left(PAR\right)$ time series. This algorithm will be operational for implementation in marine ecosystem monitoring systems and can contribute to strengthening decision-making. Full article

This study presents a near-real-time water stress monitoring framework for tropical heterogeneous landscapes by integrating optical and radar remote sensing data within the Google Earth Engine platform. Five complementary indices, vertical transmit/vertical receive–vertical transmit/horizontal receive (VV/VH) ratio, Dual Polarimetric Radar Vegetation Index (DpRVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Ratio Drought Index (RDI), were analyzed across three contrasting agricultural systems: paddy, sugarcane, and rubber, revealing distinct phenological and water stress dynamics. Radar-derived structural indices captured patterns of biomass accumulation and canopy development, with VV/VH values ranging from 4.2 to 12.3 in paddy and 5.4 to 6.0 in rubber. In parallel, optical moisture indices detected crop physiological stress; for instance, NDMI dropped from 0.26 to 0.06 during drought in sugarcane. Cross-index analyses demonstrated strong complementarity; synchronized VV/VH and RDI peaks characterized paddy inundation, whereas lagged NDMI–VV/VH responses captured stress-induced defoliation in rubber trees. Temporal profiling established crop-specific diagnostic signatures, with DpRVI peaking at 0.75 in paddy, gradual RDI decline in sugarcane, and NDMI values of 0.2–0.3 in rubber. The framework provides spatially explicit, temporally continuous, and cost-effective monitoring to support irrigation, drought early warning, and agricultural planning. Multi-year validation and field-based calibration are recommended for operational implementation. Full article

Accurate satellite retrieval of oceanic particulate organic nitrogen (PON) vertical profile is essential for understanding global biogeochemical processes; however, no dedicated retrieval models currently exist. This study developed a novel PON profile retrieval model using the eXtreme Gradient Boosting (XGBoost) algorithm, based on a comprehensive global dataset that includes in situ PON measurements, MODIS-Aqua bio-optical data, and 3D reanalysis physical data. The XGBoost-retrieved PON profiles were compared with those derived from Copernicus particulate backscattering coefficient ($b_{\mathit{bp}}$) profiles and were further used to estimate the euphotic-zone PON stocks through an optimally performing regression model. The results showed that the proposed model significantly outperformed models constructed without physical inputs, achieving $R^2$ of 0.83, RMSE of 1.49 $\mathrm{mg} {\mathrm{m}}^{-3}$ and MAPE of 18.07%. Compared to the $b_{\mathit{bp}}$-based profiles, the XGBoost-retrieved profiles exhibited higher accuracy. The model also provided reliable estimates of euphotic-zone PON stocks, with $R^2$ of 0.76, RMSE of 200.31 mg ${\mathrm{m}}^{-2}$ and MAPE of 15.09%. These findings demonstrate the potential of the proposed retrieval model for investigating oceanic nitrogen dynamics and biogeochemical cycles. Full article

Recent advances in vibration-based pavement assessment have enabled the low-cost monitoring of road conditions using inertial sensors and machine learning models. However, most studies focus on isolated tasks, such as roughness classification, without integrating statistical validation, anomaly detection, or maintenance prioritization. This study presents a unified framework for road roughness severity classification and predictive maintenance using multi-axis accelerometer data collected from urban road networks in Pretoria, South Africa. The proposed pipeline integrates ISO-referenced labeling, ensemble and deep classifiers (Random Forest, XGBoost, MLP, and 1D-CNN), McNemar’s test for model agreement validation, feature importance interpretation, and GIS-based anomaly mapping. Stratified cross-validation and hyperparameter tuning ensured robust generalization, with accuracies exceeding 99%. Statistical outlier detection enabled the early identification of deteriorated segments, supporting proactive maintenance planning. The results confirm that vertical acceleration (accel_z) is the most discriminative signal for roughness severity, validating the feasibility of lightweight single-axis sensing. The study concludes that combining supervised learning with statistical anomaly detection can provide an intelligent, scalable, and cost-effective foundation for municipal pavement management systems. The modular design further supports integration with Internet-of-Things (IoT) telematics platforms for near-real-time road condition monitoring and sustainable transport asset management. Full article

The primary objective of layered settlement monitoring of deep soil is to obtain settlement data for both the soil and superstructure, enabling appropriate measures to be taken to ensure the structure’s safety and stability. Traditional deep soil monitoring technologies are either limited in the number of measurement points (e.g., fiber Bragg grating sensing technology) or exhibit low measurement accuracy (e.g., distributed fiber optic sensing technology). This study proposes a layered settlement monitoring technique for deep soil based on the ultra-weak fiber Bragg grating sensors. First, ultra-weak fiber Bragg grating strain sensors packaged by fiber-reinforced polymer (FRP) were developed, and experimental research on the sensors’ sensing and directional recognition characteristics was conducted. Subsequently, the sensors were deployed for ground subsidence monitoring in Gaoyang County, China, with investigations conducted on sensor installation techniques and long-term measurement data. Experimental and engineering test results demonstrate that the strain and temperature sensing coefficients of the sensors are 1.22 pm/με and 17.06 pm/°C, respectively. Sensors incorporating dual ultra-weak fiber Bragg grating arrays can simultaneously detect both vertical and lateral soil displacement. Long-term monitoring data effectively reflects subsidence changes in the Gaoyang region. Full article

Classical instrumental technologies still remain important among the geodetic methods of dam monitoring, but periodic observations are often insufficient for timely detection of hazardous deformations. Therefore, the integration of continuous and remote sensing technologies into a multi-level system of observation improves the assessment of a structural condition. This research work evaluates the integrated approach that combines the GNSS data, robotic total station measurements, and satellite radar data processed by the PSInSAR technique for detecting the cyclic thermal deformations of the Dniester HPP concrete dam. The dataset includes 185 ascending and 184 descending Sentinel-1A SAR images (2019–2025, 12-day repeat cycle). PSInSAR processing was performed using StaMPS, with validation through comparison of InSAR-derived vertical displacements and GNSS data from the stationary monitoring system of the dam. The GNSS and InSAR time series have revealed consistent seasonal patterns and a common long-term trend. Harmonic components with amplitudes of 4–5 mm, peaking in late summer and declining in winter, confirm the dominant influence of thermal processes. In order to reduce noise, Fourier-based filtering and approximation were applied, thus ensuring balance between accuracy and data retention. The combined use of GNSS, robotic total station, and InSAR has increased the density of reliable control points and improved the thermal deformation model. Maximum vertical displacements of 6–13 mm were observed on the horizontal sections most exposed to solar radiation. Full article

A mechanical structure of a fiber Fabry–Perot interferometric vibration sensor for monitoring oil and gas pipelines has been proposed, and design analysis research on performance improvement has been carried out. By designing a serpentine beam structure, the mechanical sensitivity of the sensor is enhanced. Meanwhile, by designing a vertically symmetrical gravity-sensing structure, the cross-axis sensitivity of the sensor is reduced. The results of simulation analysis show that the mechanical sensitivity of the proposed design structure is 89.20 μm/g, which is 32.44 times that of the conventional structure. Moreover, due to the design of low cross-axis sensitivity, the optical sensitivity of the vibration sensor will not be degraded because of its installation status on the pipeline. The proposed mechanical structure provides a design reference for the application of the fiber Fabry–Perot interferometric vibration sensor on oil and gas pipelines, and offers potential for the development of a high-performance comprehensive safety risk monitoring system for oil and gas pipelines. Full article

The target detection method based on a constant false alarm rate (CFAR) and feature space is commonly used in remote sensing for detecting maritime targets within sea clutter. However, the performance of traditional CFAR techniques heavily relies on the signal-to-clutter ratio (SCR) in a single observational channel, while feature space methods are overly sensitive to the number of pulse accumulations and rigidly apply outlier classifiers to define detection regions, without theoretical derivation. To address these limitations, this paper proposes a two-dimensional (2D) CFAR target detection method based on echo data from dual-polarization observational channels. First, statistical models of the amplitude distribution for horizontal–horizontal (HH) and vertical–vertical (VV) polarization sea clutter radar echoes are validated under identical observation conditions using measured data, and their correlations are analyzed. Then, the Copula function is introduced as a theoretical foundation to rigorously derive and extend the cell-averaging CFAR detector through strict mathematical formulations, transitioning from single statistics to 2D detection statistics. This leads to the proposed target detection method. Testing with measured data from publicly available datasets demonstrates that the proposed method effectively achieves adaptive false alarm control and significantly improves the detection performance compared to existing single-pulse one-dimensional CFAR detection methods. Full article

The development of hydrogen energy is advancing rapidly, while the progress of hydrogen sensors has been relatively lagging behind and cannot meet the stringent performance requirements of hydrogen energy applications. WO₃ has attracted significant attention due to its highly complementary optical and electrical responses to hydrogen. In this study, hydrogen-sensitive WO₃ thin films characterized by vertically aligned crystallites were fabricated by modulating the substrate temperature and oxygen pressure during pulsed laser deposition. Building upon this foundation, a comprehensive investigation into surface modification strategies for enhancing sensitivity was undertaken. The surface modifications encompassed eight distinct metals and four different metal oxides. Among the metal-modified samples, palladium (Pd) Pd exhibited a markedly enhanced electrical response, defined as the ratio of the resistance in hydrogen-free air to that in hydrogen, of 1022, corresponding to ~45 times higher than the value of 22.4 achieved for Pt-modified films and 120 times higher than the value of 8.4 for Au-modified films. In addition, Pd/WO₃ films showed a measurable optical transmittance change of 9.7%, while all other metal-modified samples exhibited negligible optical responses (<1%). This enhancement is attributable to the catalytic and electronic sensitisation effects of Pd. Conversely, metals such as platinum (Pt), gold (Au), and silver (Ag) elicited negligible optical responses, suggesting minimal catalytic activity. The electrical response in these cases was primarily governed by electronic sensitization effects related to the work function of the metal, with higher work function values correlating with more pronounced sensitization. Regarding metal oxide modifications, the sensitization effect was more substantial when the disparity in work function between the oxide and WO₃ was greater, and this enhancement was found to be independent of the charge carrier type of the modifying oxide. These results offer significant insights into the design principles underlying high-performance WO₃-based hydrogen sensors and underscore the pivotal influence of surface modification in governing their sensing characteristics. Full article