Texture Data Research Articles

Precision detection and identification method for apparent damage in timber components of historic buildings based on portable LiDAR equipment

For the purpose of preservation and restoration of historic timber buildings, it is critical that damage to timber components must be detected and identified accurately since proper detection and identification lay the foundation for scientific utilization and preservation of cultural heritage. Portable smart devices with integrated LiDAR sensor technology have significantly advanced, enabling them to be major pedestals for damage detection. An iPhone Pro based technique is therefore suggested for the precise detection and diagnosis of apparent damage to the timber components. The study consists of three major procedures. First, an apparent data collection of the timber components was conducted. A high-resolution triangular mesh model was utilized for precision detection and identification. Point cloud and texture data were the main constituents of this data collection. Second, a technique for image processing and apparent damage analysis was proposed for crack detection in historic timber buildings. This approach can be used to identify, recognize,and assess damage to timber components such as cracks, rotting, and knots. Finally, actual measurements of the timber components in Datong, Shanxi Province, at the Great Temple Corner, proved the effectiveness of the iPhone 12 Pro as a substitute approach to the conventional inspection equipment. The findings indicate that the iPhone 12 Pro, which has a built-in LiDAR sensor, performs exceptionally well in terms of effectiveness, accuracy, and graphical interpretation when identifying apparent damage to historic timber components. Moreover, it has the potential to substitute traditional measurement, 3D scanning, and other detection and identification techniques. Even though damage detection and identification of historical timber buildings has reached a mature stage in terms of portable, refined, and digital transformation, such technique can serve as a compatible addition as a safe, affordable, and paradigm shifting technical solution for detection and identification of historic building components.

Separation of Macro- and Micro-Texture to Characterize Skid Resistance of Asphalt Pavement.

The skid resistance of asphalt pavement is an important factor affecting road safety. However, few studies have characterized the contribution of the macro- and micro-texture to the skid resistance of asphalt pavement. In this paper, the generalized extreme studentized deviate (GESD) and neighboring-region interpolation algorithm (NRIA) were used to identify and replace outliers, and median filters were used to suppress noise in texture data to reconstruct textures. On this basis, the separation of the macro- and micro-texture and the Monte Carlo algorithm were used to characterize the skid resistance of asphalt pavement. The results show that the GESD method can accurately identify outliers in the texture, and the median filtering can eliminate burrs in texture data while retaining more original detail information. The contribution of the macro-texture on the skid resistance is mainly attributed to the frictional resistance caused by the adhesion and elastic hysteresis, and the main contribution of the micro-texture is a micro-bulge cutting part in the friction mechanism. This investigation can provide inspiration for the interior mechanism and the specific relationship between the pavement textures and the skid resistance of asphalt pavement.

Regulation of tapioca starch 3D printability by yeast protein: Rheological, textural evaluation, and machine learning prediction

This article investigated the effects of yeast protein (YP) on gel rheology, texture, and 3D printability of tapioca starch and the feasibility of Principal component analysis (PCA) and support vector machine (SVM) algorithms for classification and prediction of printability. The results indicated that increasing YP content enhanced the viscosity, storage and loss moduli, and hardness, thereby improving extrudability and supportability of 3D printing. The addition of 15% YP exhibited the best 3D printing performance, but excessively high YP addition hindered ink extrusion. PCA analysis based on rheological and texture indices categorized the ink's 3D printing performance into four classes: poor support and low printing accuracy; good support but low printing accuracy; good support and high printing accuracy; and non-smooth extrusion. Furthermore, SVM algorithm used texture data to predict printability classification, with the highest prediction accuracy (91.67%) achieved at polynomial kernel among four different kernel functions. These results confirm that YP can serve as a potential ink for 3D printing and underscore SVM's efficacy in predicting ink's 3D printing performance.

Application of benthic foraminiferal indices to infer the ecological quality status in the Sepetiba Bay (SE Brazil)

The Sepetiba Bay (SB, SE Brazil) is a highly anthropized and industrialized area that has experienced severe environmental degradation in recent decades. This study applies a multiproxy approach to document the response of living benthic foraminifera to environmental stress and to infer the Ecological Quality Status (EcoQS) in SB. Our methodology involved a comprehensive comparison of the density and percentage of benthic foraminiferal species with physicochemical, textural, and geochemical data, specifically the concentrations of potentially toxic elements (PTEs). We also statistically compared two geochemical indices, the pollution load index (PLI) and the potential ecological risk index (PERI), with two ecological indices, the Tolerant Species Index (TSI) and the Exp(H'bc).The TSI and the Exp(H'bc) indices are significantly correlated with the environmental stressors in Sepetiba Bay, namely the PTEs concentrations (As, Cd, Pb, and Zn). The most tolerant species to the enrichment of PTEs and organic matter are Ammonia tepida (Cushman, 1926), Elphidium excavatum (Terquem, 1875), Ammonia buzasi Hayward and Holzmann, 2021 and Ammonia rolshauseni (Cushman and Bermúdez, 1946). The Exp(H'bc) and TSI reveal that most stations located in the inner zone and near the margins of the bay have poor and bad EcoQS, which agrees with the distribution of the environmental stressors.Thus, the current environmental conditions of the inner area of Sepetiba Bay are of great concern. This work also shows that using the TSI and the Exp(H'bc) indices, it is possible to classify EcoQS in transitional coastal environments in the North and South Atlantic transitional waters. This work has relevant scientific and social implications due to its importance in biomonitoring and the management of the coastal regions.

Sensory profile of cream cheese and plant‐based analogues: an approach through flash‐profile, CATA and RATA tests

SummaryThe distinct techno‐functional and sensory attributes conferred by milk proteins and fat are fundamental in defining the structure, texture and flavour of dairy products. Thus, reproducing cheese‐like characteristics in plant‐based alternatives while ensuring consumer acceptance is a major challenge. This study aimed to evaluate the sensory profile of commercial cream cheese and plant‐based analogues, quantifying consumer perception and discrimination, and correlating with instrumental texture analysis. For that, two milk‐based (MB1 and MB2) and three plant‐based products (PB1, PB2 and PB3) were evaluated for their proximate composition, texture profile (spreadability and firmness) and sensory properties by combining Flash Profile method (n = 13), Check‐All‐That‐Apply (CATA) test, and Rate‐all‐that‐apply (RATA) test (n = 102) with global acceptance. Milk‐based cream cheeses (MB1 and MB2) did not differ significantly from each other and presented greater spreadability when compared to all plant‐based cream cheeses (PB1, PB2 and PB3). The texture parameters of the samples were inversely related: the greater the firmness, the lower the spreadability. All forty‐two sensory attributes allowed discrimination of the samples into three distinct clusters, with no difference between the milk‐based products. The results of the CATA test showed that the attributes most correlated with the sample MB1 were the most desirable for good acceptance of the product. In turn, PB3 showed lower acceptance scores when compared to the plant‐based samples PB1 and PB2, and the attributes rancid flavour, vegetable oil aroma and nut aroma mostly contributed to the lower acceptance of PB3. Milk‐based products were very close to the ideal product, presenting a creamy and spreadable texture as key attributes for product characterisation and acceptance. The results of RATA test showed a significant difference (P < 0.05) in the intensity of nineteen sensory attributes. Milk‐based cream cheeses were more accepted than their plant‐based counterparts. The instrumental assessments of firmness and spreadability exhibited a negative correlation, once they were inversely proportional and strongly correlated to sensory data of firm and spreadable texture, respectively. The sensory characteristics of cream cheese analogues may present a barrier to their acceptance by consumers. Attributes such as coconut and flour flavour, artificial cheese aroma and flavour, and spreadable texture played a key role in differentiating dairy products from plant‐based ones, contributing to the lower acceptance of the latter.

Pavement compactness estimation based on 3D pavement texture features

Understanding the correlation between pavement compactness and pavement texture features aids in determining the range of compaction passes. This paper proposes an estimation model for pavement compactness based on 3D pavement features. The compaction process was simulated in laboratory for four different gradation types with varying compaction passes while 3D texture data were obtained. Parameters were calculated to interpret texture features. The Random Forest model and the Shapley additive explanations approach were used to explore the contribution of different feature parameters to the compactness prediction model for the feature selection. A polynomial linear model was proposed to predict the compactness using five selected parameters, which showed a good fit. It was also observed that Da and Spk make a more substantial contribution to the model. Additionally, an optimal compaction pass range considering compactness, texture depth, and temperature drop was proposed to support the control strategies in road compaction construction sites.

Remote and Proximal Sensors Data Fusion: Digital Twins in Irrigation Management Zoning.

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R2 = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R2 = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R2 of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0-10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management.

3D textured mesh scene data fusion method considering multiple scales and resolutions

ABSTRACT The fusion of 3D textured mesh scene data can effectively and seamlessly integrate multiple spatially related 3D geographical scene models into larger and more comprehensive datasets. Existing methods usually have different degrees of texture distortion and unreduced texture problems in the splicing area, and these methods do not consider the current situation of Level of Detail(LoD) data organisation in the 3D textured mesh model. We propose a 3D textured mesh scene data fusion method considering multiple scales and resolutions. First, seamless extraction of splicing areas was achieved based on spatial relationship constraints and orthogonal inverse projection principles. Second, the sparse scene structure concept was employed to generate sparse summary scene images. Finally, a dual simplification operator for both mesh and texture was proposed to construct multi-resolution 3D textured mesh models, simultaneously reconstructing triangular facets and textures within the splicing regions. We used multiple 3D textured mesh data constructed from aerial obliques to conduct data fusion experimental verification. The results indicate that our method exhibits minimal texture distortion during grid reconstruction in splicing areas and model simplification, effectively reducing the amount of simplified texture data. Additionally, our method adeptly addresses multilevel detailed 3D textured mesh models, demonstrating good feasibility and advancement.

GF-4 high-resolution texture and FY-4A multispectral data fusion: Two case studies for enhancing early convective cloud detection

Early detection of convective clouds is vital for minimizing hazardous impacts. Forecasting convective initiation (CI) using current multispectral geostationary meteorological satellites is often challenged by high false-alarm rates and missed detections caused by limited resolution. In contrast, high-resolution earth observation satellites offer more detailed texture information, improving early detection capabilities. The authors propose a novel methodology that integrates the advanced features of China's latest-generation satellites, Gaofen-4 (GF-4) and Fengyun-4A (FY-4A). This fusion method retains GF's high-resolution details and FY-4A's multispectral information. Two cases from different observational scenarios and weather conditions under GF-4's staring mode were carried out to compare the CI forecast results based on fused data and solely on FY-4A data. The fused data demonstrated superior performance in detecting smaller-scale convective clouds, enabling earlier forecasting with a lead time of 15–30 minutes, and more accurate location identification. Integrating high-resolution earth observation satellites into early convective cloud detection provides valuable insights for forecasters and decision-makers, particularly given the current resolution limitations of geostationary meteorological satellites.摘要对流云的早期探测对于降低天气致灾风险至关重要. 当前静止气象卫星受其分辨率的限制, 在预测对流初生 (CI) 时仍存在较高的虚警率和漏报率. 对比而言, 高分辨率的地球观测卫星能够提供更丰富的纹理细节, 有助于提升对流云早期探测的能力. 作者提出了一种创新性的方法, 融合GF-4的高分辨率细节和FY-4A的多光谱信息, 以优化对流云的早期识别和预测. 通过对GF-4凝视模式下两种不同天气条件和观测场景的案例分析, 融合数据在探测更小尺度的对流云方面具有明显优势, 一定程度解决漏报难题, 另一方面能够将对流初生的可探测时间提前, 更精确表现对流爆发位置和更准确刻画对流发展过程. 在当前静止气象卫星分辨率受限的条件下, 这种高分辨率地球观测卫星数据的融合应用能够为气象预报员和决策者提供更有价值的观测视角.

Selective conversion of spent cracking catalyst to faujasite-structured zeolites

Current recycling strategies for spent catalyst in Fluid Catalytic Cracking (FCC) focus on valuable rare-earth elements (REE) and sometimes include a reuse of spent materials by down-cycling, which hinders a circular economy. Herein, a new procedure for the selective conversion of spent FCC catalysts to FAU-type zeolite without secondary crystalline phases such as sodalite, zeolite P or zeolite A is reported. The steps include a leaching process of REE, followed by thermal activation with sodium hydroxide and acid treatment to get an aluminum-rich feed. The synthesis of FAU-type zeolites with varying Si/Al ratios uses the aluminum-rich catalyst feed and commercial water glass. The experiments show that incorporation of silicate from water glass into the zeolite structure is limited to zeolite products with Si/Al ratio ≤ 2.0. It indicates a flocculation of silicates in higher concentration in competition to the crystallization process of FAU-type zeolites. This is reinforced by the resulting crystals, covered and surrounded with small particles with morphology of silica. Moreover, a clear differentiation between zeolite Y and zeolite X is possible from materials properties with the definite evidence of a mixed phase. The kinetics of feed dissolution and incorporation of impurities, alumina and silica are controlled by the pre-treatment temperature, duration and silicon source. This enables flexible adjustment of the aluminum re-use from spent FCC catalyst, the amount of foreign elements (Fe, Ni, V), phase purity, Si/Al ratio, specific surface area and thermal stability of the final zeolite product. Structural and textural data of the resulting zeolites reveals high crystallinity > 80 %, Si/Al ratios of 1.6–2.0, and specific surface areas up to 780 m2/g.

PointPCA: point cloud objective quality assessment using PCA-based descriptors

Point clouds denote a prominent solution for the representation of 3D photo-realistic content in immersive applications. Similarly to other imaging modalities, quality predictions for point cloud contents are vital for a wide range of applications, enabling trade-off optimizations between data quality and data size in every processing step from acquisition to rendering. In this work, we focus on use cases that consider human end-users consuming point cloud contents and, hence, we concentrate on visual quality metrics. In particular, we propose a set of perceptually relevant descriptors based on principal component analysis (PCA) decomposition, which is applied to both geometry and texture data for full-reference point cloud quality assessment. Statistical features are derived from these descriptors to characterize local shape and appearance properties for both a reference and a distorted point cloud. The extracted statistical features are subsequently compared to provide corresponding predictions of visual quality for the distorted point cloud. As part of our method, a learning-based approach is proposed to fuse these individual predictors to a unified perceptual score. We validate the accuracy of the individual predictors, as well as the unified quality scores obtained after regression against subjectively annotated datasets, showing that our metric outperforms state-of-the-art solutions. Insights regarding design decisions are provided through exploratory studies, evaluating the performance of our metric under different parameter configurations, attribute domains, color spaces, and regression models. A software implementation of the proposed metric is made available at the following link: https://github.com/cwi-dis/pointpca.

Comparative microbiological, chemical, and sensory traits of aron fermentation in Tengger and laboratory scales

Aron, a traditional fermented food of the Tengger community, is made from white corn grown in Bromo and has received limited microbiological, chemical, and sensory research attention to date. The research objective was to compare microbiological, chemical, and sensory traits between in situ-produced aron on the slopes of Mount Bromo and ex situ-produced aron in a laboratory in Surabaya. A specific strain of lactic acid bacteria was isolated using de Man, Rogosa, and Sharpe (MRS) Agar from samples of sun-dried white corn. This strain was identified as Gram-positive cocci. In contrast, a different strain of Gram-positive bacilli was found in corn-soaking water, both in situ and in laboratory-fermented aron, on days 0, 7, and 16. Chemical analysis revealed no significant differences in protein, fat, and ash content between the two variables. However, laboratory-fermented aron exhibited higher moisture content. This discrepancy significantly influenced the hedonic texture data for aron, with in situ aron being perceived as drier and more mouth-soluble, while laboratory-fermented aron had a softer texture. Furthermore, the high microbial growth during the fermentation process resulted in aron with a distinct and relatively strong aroma that was less favored by the panelists. The difference in the fermentation location of white corn did not affect the types of lactic acid bacteria that thrived, the nutritional compound content, and the appearance of aron. The variation in the aroma of aron is believed to be primarily influenced by the number of bacteria rather than their specific strains.

Volumetric and textural analysis of PET/CT in patients with diffuse large B-cell lymphoma highlights the importance of novel MTVrate feature.

To investigate the prognostic value of clinical, volumetric, and radiomics-based textural parameters in baseline [18F]FDG-PET/CT scans of diffuse large B-cell lymphoma (DLBCL) patients. We retrospectively investigated baseline PET/CT scans and collected clinical data of fifty DLBCL patients. PET images were segmented semiautomatically to determine metabolic tumor volume (MTV), then the largest segmented lymphoma volume of interest (VOI) was used to extract first-, second-, and high-order textural features. A novel value, MTVrate was introduced as the quotient of the largest lesion's volume and total body MTV. Receiver operating characteristics (ROC) analyses were performed and 24-months progression-free survival (PFS) of low- and high-risk cohorts were compared by log-rank analyses. A machine learning algorithm was used to build a prognostic model from the available clinical, volumetric, and textural data based on logistic regression. The area-under-the-curve (AUC) on ROC analysis was the highest of MTVrate at 0.74, followed by lactate-dehydrogenase, MTV, and skewness, with AUCs of 0.68, 0.63, and 0.55, respectively which parameters were also able to differentiate the PFS. A combined survival analysis including MTV and MTVrate identified a subgroup with particularly low PFS at 38%. In the machine learning-based model had an AUC of 0.83 and the highest relative importance was attributed to three textural features and both MTV and MTVrate as important predictors of PFS. Individual evaluation of different biomarkers yielded only limited prognostic data, whereas a machine learning-based combined analysis had higher effectivity. MTVrate had the highest prognostic ability on individual analysis and, combined with MTV, it identified a patient group with particularly poor prognosis.

Precision mapping of snail habitat in lake and marshland areas: Integrating environmental and textural indicators using Random Forest modeling

Schistosomiasis japonica continues to pose a significant public health challenge in China, primarily due to the widespread distribution of Oncomelania hupensis, the sole intermediate host of Schistosoma. This study aims to address the constraints of existing remote sensing analyses for identifying snail habitats, which frequently neglect spatial scale and seasonal variations. To this end, we adopt a multi-source data-driven Random Forest approach that integrates bottomland and ground-surface texture data with traditional environmental variables, enhancing the accuracy of snail habitat assessments. We developed four distinct models for the lake and marshland areas of Guichi, China: a baseline model incorporating ground-surface texture, bottomland variables, and environmental variables; Model 1 with only environmental variables; Model 2 adding ground-surface texture and environmental variables; and Model 3 integrating bottomland with environmental variables. The baseline model outperformed the others, achieving a true skill statistic of 0.93, an accuracy of 0.97, a kappa statistic of 0.94, and an area under the curve of 0.99. Our analysis pinpointed critical high-risk snail habitats distributed in a belt-like pattern along major water bodies, near the Yangtze River, QiuPu River, and around Shengjin Lake, Jiuhua River, and Qingtong River. These insights can aid local health authorities in more efficiently allocating limited resources, developing effective snail surveillance and control strategies to combat schistosomiasis. Additionally, this approach can be adapted to localize other endemic hosts with similar ecological characteristics.

The inversion of arid-coastal cultivated soil salinity using explainable machine learning and Sentinel-2

The escalating salinization of cultivated soil poses a significant threat to the ecological environment. It is imperative to establish a monitoring system and mitigate the spread of salinization in arid and coastal areas through remote sensing, incorporating high-precision cross-regional models for soil salt content inversion. This study focuses on typical saline-alkali soils in arid and coastal regions of China. Using Sentinel 2 data (including 6 bands and 27 spectral indices), along with soil texture, moisture content, temperature, precipitation, and digital elevation model (DEM) data to establish an arid-coastal salinity inversion model. Variable selection methods such as pearson correlation coefficient (PCC), variable importance in projection (VIP), gray relational analysis (GRA), and gradient boosting machine (GBM) were used, while using 9 models including adaptive boosting (Adaboost), extremely randomized trees (ERT), and light gradient boosting machine (LightGBM). The best model was further elucidated using the Shapley additive explanations method. Results indicate that the common sensitive characteristic variables of arid-coastal areas were spectral indices and soil properties in PCC, the spectral variable bands and indices in VIP, and all variables in GRA and GBM. The best inversion model GBM-ERT (R2 = 0.91, RMSE = 1.06) in arid areas exhibited higher accuracy than the best inversion model GBM-Adaboost (R2 = 0.77, RMSE = 1.74) in coastal areas. The arid-coastal inversion model PCC-LightGBM demonstrated the best inversion performance (R2 = 0.64, RMSE = 2.29) and simulation performance in arid (R2 = 0.67) and coastal areas (R2 = 0.63). Dead fuel index (DFI) had the most significant impact on model prediction (0.89) and the second ratio index (RI2) contributed the highest relative importance (18 %) to the model. Our analysis indicates that the arid-coastal model of PCC-LightGBM established using common characteristic variables, can effectively monitor large-scale soil salinity.

A noise-reduction algorithm for raw 3D point cloud data of asphalt pavement surface texture

High-precision 3D point cloud data have various analyses and application use cases. This study aimed to achieve a more precise noise reduction of the raw 3D point cloud data of asphalt pavements obtained using 3D laser scanning. Hence, a noise-reduction algorithm integrating improved Gaussian filtering and coefficient of variation was developed. A portable laser scanner was used to collect raw, high-precision 3D point cloud data of surface textures from pavement slab samples prepared with three different types of asphalt mixtures: AC-13, SMA-13, and OGFC-13, as well as asphalt from the test sections of the Yakang Expressway. An improved Gaussian filtering and Gaussian filtering that extracts noise using the coefficient of variation were used to filter out the obvious outlier noise and small-scale burr noise, respectively. Finally, the filtering effect of the proposed algorithm, Gaussian filtering, median filtering, and mean filtering on raw 3D point cloud data of pavement textures was evaluated through subjective visual quality and objective index evaluations. The results showed that the proposed algorithm filters out noise while preserving the micro-texture structure information, outperforming Gaussian filtering, median filtering, and mean filtering.

Field Study of Asphalt Pavement Texture and Skid Resistance under Traffic Polishing Using 0.01 mm 3D Images

Pavement texture and skid resistance are pivotal surface features of roadway to traffic safety, especially under wet weather. Engineering interventions should be scheduled periodically to restore these features as they deteriorate over time under traffic polishing. While many studies have investigated the effects of traffic polishing on pavement texture and skid resistance through laboratory experiments, the absence of real-world traffic and environmental factors in these studies may limit the generalization of their findings. This study addresses this research gap by conducting a comprehensive field study of pavement texture and skid resistance under traffic polishing in the real world. A total of thirty pairs of pavement texture and friction data were systematically collected from three distinct locations with different levels of traffic polishing (middle, right wheel path, and edge) along an asphalt pavement in Oklahoma, USA. Data acquisition utilized a laser imaging device to reconstruct 0.01 mm 3D images to characterize pavement texture and a Dynamic Friction Tester to evaluate pavement friction at different speeds. Twenty 3D areal parameters were calculated on whole images, macrotexture images, and microtexture images to investigate the effects of traffic polishing on pavement texture from different perspectives. Then, texture parameters and testing speeds were combined to develop friction prediction models via linear and nonlinear methodologies. The results indicate that Random Forest models with identified inputs achieved excellent performance for non-contact friction evaluation. Last, the friction decrease rate was discussed to estimate the timing of future maintenance to restore skid resistance. This study provides more insights into how engineers should plan maintenance to restore pavement texture and friction considering real-world traffic polishing.

Research on nondestructive detection of sweet-waxy corn seed varieties and mildew based on stacked ensemble learning and hyperspectral feature fusion technology

The variety and quality of corn seeds are crucial factors affecting crop yield and farmers’ economic benefits. This study adopts an innovative method based on a hyperspectral imaging system combined with stacked ensemble learning, aiming to achieve varieties classification and mildew detection of sweet-waxy corn seeds. First, data interference is eliminated by extracting the spectral and texture information of each corn sample and preprocessing the data. Secondly, a stacked ensemble learning model (Stack) was constructed by stacking base models and meta-models. Its results were compared with those of the base models, including Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF).Finally, the overall performance of the model is improved through the information fusion strategy of hyperspectral data and texture information. The research results indicate that the GBDT-Stack model, which integrates spectral and texture data, demonstrated optimal performance in the comprehensive classification of both corn seed varieties and mold detection. On the test set, the model achieved an average prediction accuracy of 97.01%. Specifically, the model achieved a test set accuracy ranging from 94.49% to 97.58% for different corn seed varieties and a test set accuracy of 98.89% for mildew detection. This model not only classifies corn seed varieties but also accurately detects mildew, demonstrating its wide applicability. The method has huge potential and is of great significance for improving crop yield and quality.

Improved Video-Based Point Cloud Compression via Segmentation.

A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual reality and augmented reality. However, the point cloud, especially those representing dynamic scenes or objects in motion, must be compressed efficiently due to its huge data volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D point cloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches are projected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity information and some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps maintain the points' proximity and retain more original points by exploiting the density and occlusion of the points. The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods regarding rate-distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences.

Fusion of Multiple Basic Element Features for Airborne LiDAR in-house Surveys

When using airborne LiDAR point clouds for city modelling and road extraction, point cloud classification is a crucial step. There are numerous ways for classifying point clouds, but there are still issues like redundant multi-dimensional feature vector data and poor point cloud classification in intricate situations. A point cloud classification method built on the fusing of multikernel feature vectors is suggested as a solution to these issues. The technique employs random forest to classify point cloud data by merging colour information, and it extracts feature vectors based on point primitives and object primitives, respectively. In this study, a densely populated area was chosen as the study area. Light airborne LIDAR mounted on a delta wing was used to collect point cloud data at a low altitude (170 m) over a dense cross-course. The point cloud data were then combined, corrected, and enhanced with texture data, and the houses were vectorized on the point cloud. The accuracy of the results was then assessed. With a median inaccuracy of 4.8 cm and a point cloud data collection rate of 83.3%, using airborne LIDAR to measure house corners can significantly lighten the labour associated with external house corner measurements.This test extracts the texture information of point cloud data through the efficient processing of high-density point cloud data, providing a reference for the application of texture information of airborne LIDAR data and a clear understanding of its accuracy.