Urban greenery, as a critical urban landscape component, plays an important role in improving the living environments’ and residents’ well-being. Previous studies have predominantly adopted satellite image-based vegetation measurements. This study aims to quantify pedestrian-perspective greenery visibility using Google Street View (GSV) images and to understand how greenery types and built environment characteristics influence the correlation between pedestrian and aerial greenery assessments. We collected GSV images located on 34,601 sampling points and applied the DeepLab v3+ deep learning model to quantify green view index (GVI) from the pedestrian perspective. We distinguished green vegetation view index (GVVI) and green terrain view index (GTVI) to differentiate vertical and horizontal greenery types. Normalized difference vegetation index (NDVI) was extracted from Sentinel-2 images using circular buffers of varying radii (10–200 m) centered on GSV sampling points. Sampling points were filtered based on the buffer distance to avoid overlapping NDVI pixels in neighboring sampling points. Spearman correlation analysis was conducted across different typologies (urban, intermediate, rural) to examine GVI-NDVI relationships. Street-level greenery exhibited substantial spatial heterogeneity across the whole of the study area (Basel, Switzerland). GVI vs. NDVI in buffers with different radii had strong positive correlations, with a maximum Spearman coefficient of 0.77 for the 15 m NDVI buffer. Correlation coefficients decreased progressively from urban (0.77) to intermediate (0.72) and rural (0.66) areas. Correlation coefficients strongly decreased with increasing buffer sizes. Analysis of GSV images with high NDVI but low GVI values indicates that greenery types and building distributions significantly affect the street-level visible greenery. This study links street-level greenery with features in the built environment by using different methods for assessing green exposure. The findings provide methodological insights for greenery exposure studies and inform evidence-based urban planning strategies for optimizing green visibility.
The regolith temperature of the Moon is strongly influenced by direct solar radiation and multiple-scattered thermal radiation from the surrounding terrains. Accurate simulation of these processes requires high-resolution topography and a thermophysical framework. To this end, we constructed a 1 m resolution digital elevation model (DEM) of the Banting (BT) crater by fusing Lunar Reconnaissance Orbiter Camera (LROC) imagery with Lunar Orbiter Laser Altimeter data using the Integrated Software for Imagers and Spectrometers and Ames Stereo Pipeline software. Based on this high-resolution DEM, a 1D transient heat-conduction model was solved using the finite volume method to simulate the temporal and spatial evolution of the regolith temperature. The simulated illumination patterns agree with the LROC observations, confirming DEM reliability, and the temperature results show strong consistency with Diviner brightness temperature data. Within this validated modeling framework, we further evaluate the contribution of scattered solar radiation and thermal emission from Earth (SSRTEE) to the surface thermal balance. The analyses indicate that SSRTEE contributes less than 0.1 K to the regolith temperature at the BT site, far weaker than direct solar or multiple-scattered radiation. This result quantitatively confirms that Earth-induced radiative terms can be safely neglected in regolith temperature simulations for mid- and low-latitude lunar regions. This study provides a validated methodological framework for high-resolution lunar thermophysical modeling, which can support future surface environment investigations and landing-site assessments.
پژوهش حاضر، عملکرد دادههای بازتحلیل در بازتولید پارامترهای دما و رطوبت طی دوره 1990 تا 2020 را در 9 ایستگاه جو بالا با دوبار گمانهزنی در روز ارزیابی میکند. پارامترهای مورد بررسی شامل دما، دمای نقطه شبنم، رطوبت ویژه، آهنگ کاهش دما ( در لایههای یک کیلومتری، سه کیلومتری و سه تا شش کیلومتری از سطح) و نسبت اختلاط در ارتفاعهای 100، 300 و 500 متری از سطح زمین بود. شاخصهای آماری اریبی، همبستگی، ریشه میانگین مربعات خطا و شاخص توافق استفاده شد. نتایج نشان داد که دادههای بازتحلیل دمای هوا در تراز 850 هکتوپاسکال را در شب/روز فراتخمین/فروتخمین کردهاند؛ اما در ترازهای بالاتر دقت و همبستگی بیشتری داشتند. دمای نقطه شبنم بازتحلیل در ترازهای 700 و 500 هکتوپاسکال در اغلب موارد بیشبرآورد شده بودند و میزان خطا در تراز 500 بیشتر از 700 هکتوپاسکال بود. دقت برآورد آهنگ کاهش دما در یک کیلومتر ابتدایی جو در اغلب ایستگاهها پایین بود و پدیدههایی، مانند، سرمایش سطحی و وارونگی دما به دقت بازیابی نشدند. پارامترهای رطوبتی نیز در سطوح پایین جو بهویژه در شب بیشبرآورد شدند. نسبت اختلاط در ارتفاعهای 100، 300 و 500 متری در تمامی ایستگاهها بهجز اهواز، بهویژه در شب بیشبرآورد شدند. ایستگاه اهواز با کمترین ارتفاع از سطح دریا، هم در شب و هم در روز فروتخمین نسبت اختلاط را تجربه کرد. نتایج نشان میدهد که در این منطقه عملکرد دادههای بازتحلیل در سطوح پایین جو نسبت به سطوح بالاتر از دقت کمتری برخوردار است و نیاز به اصلاح یا تلفیق با دادههای مشاهداتی دارد.
Accurate estimation of regional rice yields is crucial for food security and efficient agricultural management. In this regard, the use of Unmanned Aerial Vehicles (UAVs) that have revolutionized crop monitoring by providing high-resolution images for precision agriculture, is beneficial. This study explores the potential of Segment Anything Model (SAM) for detecting rice seedlings, focusing on determining the optimal approach and prompt for this task. We examined three SAM scenarios: automatic mask generation, bounding box prompt, and point prompt. Our evaluation criteria included processing time, visual interpretation, and accuracy indexes. The results demonstrated the effectiveness of SAM in rice seedling detection, highlighting the importance of selecting the appropriate prompt for specific agricultural applications. Our findings reveal that the point prompt method emerges as the preferred choice for rice seedling detection, offering superior accuracy and reliability. Specifically, it achieved mIoU and mDice scores of 94.57 % and 0.97, respectively. While the bounding box approach showed promise, despite slightly lower precision, it may still be suitable depending on application-specific requirements. Conversely, the automatic mask generation scenario proved unsuitable for this task due to its low accuracy and inability to effectively detect rice seedlings. The outcomes of this study serve as a baseline for evaluating SAM prompts, guiding future improvements and refinements to enhance its performance in real-world agricultural applications.
Zehra Funda Akbulut, Taher A. Tawfik, Piotr Smarzewski
et al.
This research investigates the effects of steel (ST) and synthetic (SYN) fibers on the workability and mechanical properties of HPFRC. It also analyzes their influence on the material’s microstructural characteristics. ST fibers improve tensile strength, fracture toughness, and post-cracking performance owing to their rigidity, mechanical interlocking, and robust adhesion with the matrix. SYN fibers, conversely, mitigate shrinkage-induced micro-cracking, augment ductility, and enhance concrete performance under dynamic stress while exerting negative effects on workability. Hybrid fiber systems, which include ST and SYN fibers, offer synergistic advantages by enhancing fracture management at various scales and augmenting ductility and energy absorption capability. Scanning electron microscopy (SEM) has been crucial in investigating fiber–matrix interactions, elucidating the effects of ST and SYN fibers on hydration, crack-bridging mechanisms, and interfacial bonding. ST fibers establish thick interfacial zones that facilitate effective stress transfer, whereas SYN fibers reduce micro-crack formation and enhance long-term durability. Nonetheless, research deficiencies persist, encompassing optimal hybrid fiber configurations, the enduring performance of fiber-reinforced concrete (FRC), and sustainable fiber substitutes. Future investigations should examine multi-scale reinforcing techniques, intelligent fibers for structural health assessment, and sustainable fiber alternatives. The standardization of testing methodologies and cost–benefit analyses is essential to promote industrial deployment. This review offers a thorough synthesis of the existing knowledge, emphasizing advancements and potential to enhance HPFRC for high-performance and sustainable construction applications. The findings facilitate the development of new, durable, and resilient fiber-reinforced concrete systems by solving current difficulties.
High-accuracy Digital Elevation Models (DEMs) are critical for hydrological and ecological applications in low-relief arid basins, yet Interferometric Synthetic Aperture Radar (InSAR)-derived DEMs suffer from significant altitudinal errors due to temporal decorrelation and phase unwrapping artifacts, particularly in flat terrains. To address these limitations, we developed a novel machine learning framework that synergizes Sentinel-1 InSAR, UAV photogrammetry, Sentinel-2 spectral indices, and ALOS topographic features to enhance DEM accuracy. The approach was validated in Northwest China’s Taitema Lake basin across 13 sample plots covering diverse arid surface types (dunes, wetlands, playas). Four algorithms – Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Polynomial Regression (PR) – were rigorously evaluated. Without topographic data, SVM achieved the highest accuracy (test-set R2 = 0.8564). Integrating terrain features with RF further improved performance (R2 = 0.8634, MAE = 1.0683 m), reducing errors from approximately [−10, 27] m to predominantly ±6 m. The RF-corrected DEM exhibited a 42.8% decrease in standard deviation (2.60 m → 1.49 m) and a substantial R2 increase (16.4% → 89.1%). Shapley Additive exPlanations (SHAP) interpretability analysis identified slope and near-infrared reflectance as dominant error-correction features. The corrected DEMs demonstrate enhanced terrain continuity, minimized elevation noise, and offer a scalable, efficient solution for InSAR post-processing in ecologically sensitive arid regions.
Large and complex steel structures play a vital role in building construction. However, deviations between the design model and the actual construction state are inevitable, which seriously affects the quality and safety of building construction. In our study, an intelligent construction monitoring method for large and complex steel structures based on laser point cloud is proposed. Firstly, three-dimensional laser scanning technology is introduced to capture accurate and complete spatial information on steel structures. Then, considering the inconsistency of the coordinate system between the design model and the laser point cloud, the building information model (BIM) is converted into the point cloud model, and the datum unification of the two types of the point cloud is achieved by adopting a coarse-to-fine registration strategy. Finally, the spatial information of steel structures is extracted from the laser point cloud based on the as-designed model, and the distance deviation between the two models is analyzed to reflect the actual construction state. To demonstrate the applicability of the proposed method, the steel structures’ point cloud of the stadium and the high-speed railway station is captured by the terrestrial three-dimensional laser scanner. The experimental results demonstrate that the method can extract the deviation between the design model and the actual construction, to provide accurate data sources for the intelligent fine construction of steel structures.
The contribution presents the representative research progress on global static gravity field modeling, regional geoid/quasigeoid determination, vertical datum study, as well as the theory, algorithm and software for gravity field study in China from 2019 to 2023, which are the highlights of the chapter 6 “Progress in Earth Gravity Model and Vertical Datum” in the “2019—2023 China National Report on Geodesy” that submitted to the International Association of Geodesy(IAG). In addition, suggestions are proposed to promote the research in the fields of earth gravity field, geoid/quasigeoid and vertical datumin China according to trends of international geodesy and related disciplines.
Trends showing increase in the number of mobile device users, as well as the number of tourists, imply that more people rely on their smartphones when navigating in a new environment. Based on these facts, the idea for this experimental research appeared. That idea is applying the process of machine learning, more precisely, the implementation of a neural network, to investigate the possibility of improving the accuracy of smartphone navigation. The achieved results indicate that machine learning algorithms (neural networks) are a powerful tool that can also be applied to GNSS data collected by a smartphone device, in order to improve accuracy. Based on the collected data in the field, preprocessing and machine learning process, it is concluded that it is possible to improve the accuracy of mobile device navigation by up to 50%.
Remote sensing methods are a vital alternative for regional exploration surveys. Many ore deposits (e.g., epithermal, porphyry-related, volcanogenic massive sulphides, etc.) have distinct distribution patterns of alteration zones that can be used for recognizing this mineralization. Several known goldfields are distributed within the basement rocks of western Nigeria. The area of interest, Malumfashi Schist Belt, is located in North-Western Nigeria and is characterized by gneisses and metasediments that were intruded by Pan-African granitoids. Gold mineralization occurs as veins and veinlets that are associated with hydrothermal alteration zones (i.e., argillic, phyllic, and propylitic). Hence, the discrimination of these alteration zones is one of the key indicators for new prospective zones of gold mineralization in this metallogenic belt. In the present study, Landsat Enhanced Thematic Mapper+ (Landsat-7 ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were processed and integrated with the aim to identify possible locations for gold mineralization within the Malumfashi Schist Belt. For this purpose, the band ratio techniques and Principal Component Analysis (PCA) were applied to identify, enhance and map the different alteration types, while fractal analysis was utilized to quantify the degree of alteration within each processed image. Using the multi-criteria evaluation method, the discretized images obtained from the fractal analysis were weighted and integrated into an enhanced possible location for gold occurrences. A receiver operating curve/Area under curve analysis was then used to evaluate the reliability of the predictive model. Both spatial and GIS analyses indicate that gold mineralization displays a proximal relationship to hydrothermal alteration data. We can map sets of alteration minerals which mainly represent new and good ore prospects for the investigated area. A sensitivity analysis points out a predictive accuracy of 78%, which suggests the model is capable of predicting gold occurrences within the study region. Besides, the results showed that the prospective zones of gold accumulations mainly occur within metasedimentary units. It is recommended that the studied dataset provide a potential tool for mapping alteration minerals related to gold deposits that can be applied in other regions with analogous geological setting.
Theresa Foley, Ann Marie Wolf, Chloe Jackson
et al.
Fear of liability from the 1980 Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA or Superfund) has prompted developers to build preferentially upon undeveloped green space rather than potentially contaminated former industrial sites, leading to urban sprawl in the suburban areas while blighted properties in the urban core remain vacant. A brownfield is defined as a property in which the presence or potential presence of a hazardous substance or contaminant poses a barrier to development. Agencies often create brownfield inventories by performing a site suitability analysis, using distinguishing features such as ecologically and culturally significant areas or neighborhoods that need revitalizing. Pima County, Arizona and the Sonora Environmental Research Institute, Inc. (SERI) developed a brownfield inventory of the large, industrial area directly to the west of Davis-Monthan Air Force Base. Because the brownfield target area has few residential neighborhoods and lacks the distinguishing features usually used in a brownfield site suitability analysis, the county and SERI used the official tax assessor database and 11 federal, state and county environmental databases to develop a brownfield inventory. The goal of the project was to prioritize properties that stood to benefit from the grant funding. The final brownfield inventory contained 531 parcels.
Abstract Marine gravimeter has been proved to be the primary technique to efficiently obtain middle-to-short wavelength signals of the earth’s gravity field in geodesy, geodynamics and marine sciences research. In recent years, some prototypes of inertial platform and strapdown marine gravimeters have been developed, where the inertial platform gravimeter systems include CHZ-II and ZL11, and strapdown gravimeter systems include SAG-2M and SGA-WZ. In order to validate the performance of these marine gravimeter prototypes, a synchronous test with the widely used gravimeters GT-2M and LCR arranged on the same vessel was carried out in the north of South China Sea. All the data are processed according to the survey standard flow, and the performance is estimated by analyzing the errors of the repeat lines and the crossover points under the same environment. The compared results show that all the six gravimeters can meet the precision requirement of marine gravity survey. Meanwhile, the precision results of the improved gravimeters can get close to the precision of gravimeter GT-2M, higher than the precision gravimeter LCR.
Ivana Lukić Kristić, Maja Prskalo, Vlasta Szavits-Nossan
A numerical and an empirical method for calculating nonlinear load-settlement curves for shallow foundations on sand are examined and used in a new methodology. Both methods have merits and drawbacks. The drawbacks are overcome by the methodology proposed and verified in the paper. This methodology combines the merits of each method. For this purpose, a modification of the empirical method is proposed that accounts for the finite initial soil stiffness at very small strains. Computer programs with sophisticated nonlinear stress-strain relationships, such as Hardening Soil Small in Plaxis 2D, which are versatile in solving complex foundation problems, can cover strains from very small to large. When the foundation soil is layered, it is proposed to fit such a numerical load-settlement curve against the modified empirical relationship for each sand layer separately. This requires cone penetration tests, measurements of the shear wave velocity, and basic laboratory tests. The methodology is described and applied at two locations where load tests on footings were carried out. At one location there was a top layer of clay, which was also taken into account. The results of the application of the proposed methodology are very good.
Abstract Microwave electromagnetic signals from the Global Navigation Satellite System (GNSS) are affected by their travel through the atmosphere: the troposphere, a non-dispersive medium, has an especial impact on the measurements. The long-term variations of the tropospheric refractive index delay the signals, whereas its random variations correlate with the phase measurements. The correlation structure of residuals from GNSS relative position estimation provides a unique opportunity to study specific properties of the turbulent atmosphere. Prior to such a study, the residuals have to be filtered from unwanted additional effects, such as multipath. In this contribution, we propose to investigate the property of the atmospheric noise by using a new methodology combining the empirical mode decomposition with the Hilbert–Huang transform. The chirurgical “designalling of the noise” aims to filter both the white noise and low-frequency noise to extract only the noise coming from tropospheric turbulence. Further analysis of the power spectrum of phase difference can be performed, including the study of the cut-off frequencies and the two slopes of the power spectrum of phase differences. The obtained values can be compared with theoretical expectations. In this contribution, we use Global Positioning System (GPS) phase observations from the Seewinkel network, specially designed to study the impact of atmospheric turbulence on GPS phase observations. We show that (i) a two-slope power spectrum can be found in the residuals and (ii) that the outer scale length can be taken to a constant value, close to the physically expected one and in relation with the size of the eddies at tropospheric height.
Andrii Evdokimov, Kostiantyn Dolia, Artur Rudomakha
et al.
The value of the integrated indicator of the united territorial communities land use is determined. An assessment of the integral indicator was carried out and directions for the development of methodological recommendations to improve the efficiency of land use of the united territorial communities were identified. A feature of the use of GIS for analysis and visualization of integrated indicators of land use of the united territorial communities is the development of a geoinformation analysis scheme. The developed scheme of geoinformation systems using for modelling, evaluation, and analysis of integrated indicators of the united territorial communities land use gives the opportunity to form information and analytical support of monitoring based on geospatial information and to create the basis for increasing the united territorial communities land use. The sequence obtained in the article ensures the monitoring of changes in the spatial characteristics of the lands of the united territorial communities in the region. The results of determining the integral indicators of land use of the united territorial communities obtained in the article make it possible to carry out geoinformation analysis and build a GIS map of the land use. The developed GIS map allows the formation of information and analytical monitoring support based on the values of integrated indicators of land use. Also, the data of the presented map allow to predict the directions of land use of the united territorial communities, to compare them by territorial features and features depending on changes of system spatial, urban, investment and ecological factors.
In this contribution, we assess, for the first time, the tightly combined real-time kinematic (RTK) with GPS, Galileo, and BDS-3 operational satellites using observations from their overlapping L1-E1-B1C/L5-E5a-B2a frequencies. First, the characteristics of B1C/B2a signals from BDS-3 operational satellites is evaluated compared to GPS/Galileo L1-E1/L5-E5a signals in terms of observed carrier-to-noise density ratio, pseudorange multipath and noise, as well as double-differenced carrier phase and code residuals using data collected with scientific geodetic iGMAS and commercial M300Pro receivers. It’s demonstrated that the observational quality of B1C/B2a signals from BDS-3 operational satellites is comparable to that of GPS/Galileo L1-E1/L5-E5a signals. Then, we investigate the size and stability of phase and code differential inter-system bias (ISB) between BDS-3/GPS/Galileo B1C-L1-E1/B2a-L5-E5a signals using short baseline data collected with both identical and different receiver types. It is verified that the BDS-3/GPS/Galileo ISBs are indeed close to zero when identical type of receivers are used at both ends of a baseline. Moreover, they are generally present and stable in the time domain for baselines with different receiver types, which can be easily calibrated and corrected in advance. Finally, we present initial assessment of single-epoch tightly combined BDS-3/GPS/Galileo RTK with single-frequency and dual-frequency observations using a formal and empirical analysis, consisting of ambiguity dilution of precision (ADOP), ratio values, the empirical ambiguity resolution success rate, and the positioning accuracy. Experimental results demonstrate that the tightly combined model can deliver much lower ADOP and higher ratio values with respect to the classical loosely combined model whether for GPS/BDS-3 or GPS/Galileo/BDS-3 solutions. The positioning accuracy and the empirical ambiguity resolution success rate are remarkably improved as well, which could reach up to approximately 10%∼60% under poor observational conditions.
El-Sayed ABDELRAHMAN, Mohamed GOBASHY, Eid ABO-EZZ
et al.
We have developed a simple method to determine completely the model parameters of a buried dipping fault from gravity data (depths to the centers of the upper and lower portions of the faulted thin slab, dip angle, and amplitude coefficient). The method is based on defining the anomaly values at the origin and at four symmetrical points around the origin on the gravity anomaly profile. By defining these five pieces of information, the dip angle is determined for each value of the depth of the lower portion of the faulted thin slab by solving iteratively one nonlinear equation of the form f(α) = 0. The computed dip angles are plotted against the values of the depth representing a continuous depth-dip curve. The solution for the depth to the lower portion of the faulted thin slab (down-thrown block) and the dip angle of the buried fault is read at the common intersection of the depth-dip curves. Knowing the depth to the center of the lower portion of the faulted layer and the dip angle, the problem of determining the depth to the center of the upper portion of the faulted slab (up-thrown block) is transformed into the problem of solving iteratively a nonlinear least-squares equation, f(z) = 0. Because the depths and the dip angle are known, the amplitude coefficient, which depends on the thickness and density contrast of the thin slab, is determined using a linear least-squares equation. The method is applied to theoretical data with and without random errors. The validity of the method is tested on real gravity data from Egypt. In all cases examined, the model parameters obtained are in good agreement with the actual ones and with those given in the published literature.