bluuebunny/crossref_metadata_2025 huggingface dataset:
Fabrication and Characterization of Activated Carbon from Phyllostachys edulis Using Single-Step KOH Activation with Different Temperatures
Authors: Guo Yue , Wang Qingyue
Published: August 2022

Biomass waste from harvestable output is produced in significant quantities by agricultural and forestry processes and can have detrimental effects on the ecosystem. Therefore, biomass derived from the waste in the environment has been recognized as a potential source for preparing functional materials in recent years. In this study, activated carbon (ACs) was fabricated and characterized from Phyllostachys edulis (Moso bamboo) using single-step potassium hydroxide (KOH) activation at different temperatures (500 °C to 1000 °C). The prepared ACs were characterized for surface morphology, surface area, functional groups and crystallinity using scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) analysis, Fourier transform infrared (FTIR) and X-ray diffraction (XRD), respectively. The SEM revealed well-formed pores on the surface of all ACs, while BET analysis revealed the presence of microporous (≤800 °C) and mesoporous (>800 °C) structures. SBET surface area and total pore volume increased with increasing activation temperature, from 434 to 1790 m2/g and 0.2089 to 0.8801 cm3/g, reaching a maximum at 900 °C. FTIR revealed the presence of carbonyl and hydroxyl groups on the surface. XRD showed a dominant amorphous structure and a low crystallization degree in all ACs.
Entropy Flow Analysis of Thermal Transmission Process in Integrated Energy System Part II: Comparative Case Study
Authors: Chen Changnian , Wang Junjie , Zhao Haoran , Yu Zeting , Han Jitian , Chen Jian , Liu Chunyang
Published: August 2022

The dynamic characteristics of thermal energy play an important role of multi-scale coupling among heterogeneous energy sources in integrated energy systems (IES). In Part I, for the purpose of accurately describing the dynamic processes of thermal energy transmission, the theoretical approach and models were proposed and verified by numerical simulation. In this part, an innovative analytical method based on entropy flow was derived on the basis of theory developed in Part I, which can assess the quantity and quality of thermal transport. A comparative case study indicates that the change trend of entropy flow of each node is consistent with the change of available power, but independent of temperature. The node entropy flow is increased compared with the algebraic sum of branch entropy flow, which reflects the phenomenon of entropy generation in the mixing process; while the change of available power is just the opposite. This means the irreversible entropy generation at the node leads to loss of the available thermal power. Therefore, it is more accurate to describe the dynamic thermal transmission process on the scale of entropy. This proves the effectiveness of the models proposed in Part I as well as the methods in this part from the perspective of application.
Experimental Study on Particle-Based Temporary Plugging Material Selection and Diversion Law of Shale Gas Reservoirs in WY Area, Sichuan, China
Authors: Xu Hualei , Ma Yongle , Jiang Houshun , Wang Jie , Fan Longfei , Guo Panyang
Published: August 2022

A temporary plugging agent is an effective tool for increasing production in old wells. By considering the temporary plugging materials used in the remote WY area, three concentrations and five ratios under different fracture widths were designed and optimized. Thus, the fracture diversion under true triaxial stress was studied. The results showed that when the fracture widths were 2.0, 4.0, and 6.0 mm, the corresponding optimal plugging scheme was that the concentrations of the temporary plugging agent were 12.0, 18.0, and 18.0 kg/m3 and the ratios of 20–70 mesh to 200–300 mesh temporary plugging agent were 4:1, 2:1 and 4:1, respectively. When maintaining the same horizontal stress, an increase in the axial stress was accompanied by an increase in the fracture pressure; the initial fractures almost propagated along the direction of the horizontal maximum principal stress, but the shapes of the turning fractures were different. When the axial stress was the same, an increase in the horizontal stress difference was accompanied by a decrease in the corresponding fracture pressure and a gradual decrease in the degree of fracture turning. This study provides a basis for numerical simulation and field application of temporary plugging fracturing at fracture ends.
Memories of Ups and Downs: The Vicissitudes of the Chongshansi in Taiyuan
Authors: Wen Jing
Published: August 2022

This article traces the erection of and changes in the Buddhist temple of Chongshansi in Taiyuan through the process of spatial production under the social background of the Ming and Qing Dynasties. It is stated that the founding time of the temple complies with the reorganization policies aimed at Buddhist institutions in the early Ming Dynasty, which confirms the setup of the Prefectural Buddhist Registry as the motivation for erecting the temple. Within the spatial structure of Taiyuan in the Ming Dynasty, its relative position with the Princely Palace of Jin (completed in 1375) and the expanded Taiyuan City is analyzed, revealing how its layout participated in the construction of the ritual path of Taiyuan under the control of the palace. The article concludes with a description of the fall of the temple following the loss of protection from the Jin Principality by tracing back its original form through the remains still evident in the city. The vicissitudes of the physical space of the temple are deeply connected to its role in the political space of the city. The article, thus, presents the changes in the temple throughout history. In positioning the temple back to the power and physical space of the imperial court, as well as the Jin Principality, a new perspective is provided into regional monasteries during the Ming Dynasty.
A Neo-Confucian Definition of the Relationship between Individuals and Community in the Song–Ming Period (960–1644): Start with the Discovery of Multifaceted Individuals
Authors: Zhang Meihong
Published: August 2022

Alasdair MacIntyre doubts that Confucianism can discuss the relationship between individuals and community because he maintains that it is impossible to discuss the topic in depth without a Western conception of individual rights. In this article, I show that Neo-Confucianism pays extensive attention to the relationship between individuals and community by working through several Chinese thinkers’ theories from the 11th to the 17th centuries. Neo-Confucianism seems to be focused on the exploration of the common principles of a community, but its real intention is ensuring the fundamentality of individual selves and making up for limitations caused by an excess of individual limitations. Thus, a new relationship is formed between individuals and community; that is, all individuals are equal and the common principles of community are independent of any individual. In order to make each individual harmonize with common principles, some mainstream Neo-Confucian thinkers attached great importance to the effort (gongfu 工夫) of “eliminating personal desires” (qu renyu 去人欲) since they thought that personal desires represented a selfish appeal that contradicts common principles. Influenced by this line of thinking, Neo-Confucianism fell into the predicament where individuals were suppressed, but this shortcoming was corrected in its later stage by defending the right to satisfy individual desires for survival. This study shows that Neo-Confucian discourse has given much thought to the problem of the relationship between individuals and community.
Gratitude to God: Brief Prompts Do Not Increase It, Wording of Questions Matters, and Belief in a Loving, Powerful, Gift-Giving God Remains Central
Authors: Exline Julie J. , Wilt Joshua A.
Published: August 2022

When good things happen, what thoughts elicit gratitude to God (GTG)? Building on work highlighting divine attributions and appraisals, we examined whether consciously priming people to think about God would increase subsequent reports of GTG. U.S. adult participants (N = 553) completed an online survey asking them to describe a positive event from the past month. They were then randomly assigned to one of six conditions, five of which brought up beliefs about God or the idea that God might work indirectly through natural events. Contrary to preregistered predictions, there were no statistically significant differences between conditions on an open-ended or a Likert measure of GTG. Yet GTG reports differed dramatically between the open-ended question (only 20% of participants reported GTG) and a Likert item (81% of participants, and 93% of those who believed in God, endorsed some GTG). The most endorsed response on the 5-point Likert scale was 5, indicating that most people reported feeling extremely grateful to God. These results suggest that the methods used to assess GTG could have a major impact on conclusions about GTG prevalence. Yet, regardless of assessment method, and directly replicating earlier finding, several factors emerged as consistent GTG predictors: religiousness, belief in a loving, powerful, and generous God with positive intentions, attributing the positive event to God, feeling loved in response, and framing the event as a gift from God.
Mitigating the Strife between Atheists and Islamists in the Arab World: Dissolving Supremacy of Principles within Socio-Historical Reality
Authors: Obiedat A. Z.
Published: August 2022

Western New Atheism is witnessing a resurgence in the Arab world which has provoked a strong Islamic counter-atheism reaction. The present essay seeks to: 1—briefly describe the cultural resurgence of both opposing camps, 2—illuminate a philosophical Qur’ānic approach to atheism, 3—present a peculiar defense of atheism by Muḥammad li-Mzūghī utilizing classical Arab-Islamic scholasticism and literature, 4—examine an attempt to counter atheism by utilizing modern philosophy as articulated by ʻAbd al-Jalīl al-Kūr, 5—expand the theoretical debate by highlighting the current social magnitude of the atheistic-Islamic strife, 6—reveal an inner camp schism between the higher and lower intellectual levels of both Islamic and atheistic camps, an inner camp rivalry, and a cross camp alliance, and finally, 7—propose some epistemic solutions that can reduce the magnitude of this socio-cosmological debate.
Readability of Financial Footnotes, Audit Fees, and Risk Management Committee
Authors: Prabhawa Aditya Aji , Harymawan Iman
Published: August 2022

We find that the readability of financial footnotes and risk management committees contributes to audit fees. We use observations from 758 companies listed in Indonesia for 2014–2018, and moderated regression analysis is used for statistical analysis. The results show that the readability of financial footnotes will affect audit fees paid, and RMC strengthens the relationship between the readability of financial footnotes and audit fees. In addition, we also used robustness assays to address endogeneity issues with consistent results as moderated regression analysis (hereafter MRA). These findings provide evidence for policymakers about the relationship between the readability of financial footnotes, RMC, and audit fees.
Heat Equation as a Tool for Outliers Mitigation in Run-Off Triangles for Valuing the Technical Provisions in Non-Life Insurance Business
Authors: Barlak Jan , Bakon Matus , Rovnak Martin , Mokrisova Martina
Published: August 2022

Estimating outstanding claims reserves in the non-life insurance business is often impaired by outlier-contaminated datasets. Widely used methods to eliminate outliers in non-life development triangles are either limiting the number of outliers by robust statistical methods or by change of development factors. However, the whole estimation process is likewise adversely affected so that: (i) the total sum of all triangle payments is not correct or (ii) the difference between the original triangle and its backward estimation via the bootstrap method is ineligible. In this paper, the properties of the heat equation are examined to obtain an outlier smoothing technique for development triangles. The heat equation in two dimensions is being applied on an outlier contaminated dataset where no individual data are available. As a result, we introduce a new methodology to (i) treat outliers in non-life development triangles, (ii) keep the total sum of all triangle payments, and (iii) provide acceptable differences between the original and the backward estimated triangle. Consequently, the outlying values are eliminated and the resulting development triangle could be used as an input for any claims reserving method without a need for further robustification or change of development factors. Additionally, the research on the application of heat equation in one dimension presented in this paper enables one to employ the bootstrap method using Pearson’s residuals in cases where the method was originally inapplicable due to development factors being lower than one.
Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge
Authors: Shen Beibei , Ding Lei , Ma Leichao , Li Zhenwang , Pulatov Alim , Kulenbekov Zheenbek , Chen Jiquan , Mambetova Saltanat , Hou Lulu , Xu Dawei , Wang Xu , Xin Xiaoping
Published: August 2022

Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2/m2, MAE = 0.29 m2/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.
A Deep Learning Application to Map Weed Spatial Extent from Unmanned Aerial Vehicles Imagery
Authors: Fraccaro Paolo , Butt Junaid , Edwards Blair , Freckleton Robert P. , Childs Dylan Z. , Reusch Katharina , Comont David
Published: August 2022

Weed infestation is a global threat to agricultural productivity, leading to low yields and financial losses. Weed detection, based on applying machine learning to imagery collected by Unmanned Aerial Vehicles (UAV) has shown potential in the past; however, validation on large data-sets (e.g., across a wide number of different fields) remains lacking, with few solutions actually made operational. Here, we demonstrate the feasibility of automatically detecting weeds in winter wheat fields based on deep learning methods applied to UAV data at scale. Focusing on black-grass (the most pernicious weed across northwest Europe), we show high performance (i.e., accuracy above 0.9) and highly statistically significant correlation (i.e., ro > 0.75 and p < 0.00001) between imagery-derived local and global weed maps and out-of-bag field survey data, collected by experts over 31 fields (205 hectares) in the UK. We demonstrate how the developed deep learning model can be made available via an easy-to-use docker container, with results accessible through an interactive dashboard. Using this approach, clickable weed maps can be created and deployed rapidly, allowing the user to explore actual model predictions for each field. This shows the potential for this approach to be used operationally and influence agronomic decision-making in the real world.
Vegetation Growth Dynamic and Sensitivity to Changing Climate in a Watershed in Northern China
Authors: Cao Wenxu , Xu Hang , Zhang Zhiqiang
Published: August 2022

Global climate change profoundly influences the patterns of vegetation growth. However, the disparities in vegetation responses induced by regional climate characteristics are generally weakened in large-scale studies. Meanwhile, distinct climatic drivers of vegetation growth result in the different reactions of different vegetation types to climate variability. Hence, it is an extraordinary challenge to detect and attribute vegetation growth changes. In this study, the spatiotemporal distribution and dynamic characteristics of climate change effects on vegetation growth from 2000 to 2020 were investigated by the normalized difference vegetation index (NDVI) dataset during the growing season (April–October). Meanwhile, we further detected the climate-dominated factor between different vegetation types (i.e., forest, shrub, and grass) within the Chaohe watershed located in temperate northern China. The results revealed a continuous greening trend over the entire study period, despite slowing down since 2007 (p < 0.05). Growing-season precipitation (P) was identified as the dominant climatic factor of the greening trend (p < 0.05), and approximately 34.83% of the vegetated area exhibited a significant response to increasing P. However, continued warming-induced intensive evaporation demand caused the vegetation growth to slow down. Hereinto, the areas with a significantly positive response of forest growth to temperature decreased from 24.38% to 18.06% (p < 0.05). In addition, solar radiation (SW) corresponds to the vegetation trend in the watershed (p < 0.05), and the significantly positive SW-influenced areas increased from 9.24% and 2.64% to 11.78% and 3.37% in forests and shrubland, respectively (p < 0.05). Our findings highlight the nonlinearity of long-term vegetation growth trends with climate variation and the cause of this divergence, which provide vital insights into forecasting vegetation responses to future climate change.
WCA-Based Low-PSLL and Wide-Nulling Beampattern Synthesis for Radar Applications
Authors: Xu Yanhong , Wang Dongyun , Wang Anyi , Yan Yan
Published: August 2022

There are many unavoidable array errors in practical scenarios, which would drastically increase the sidelobe level (SLL) and distort the performance of radar systems accordingly. In this paper, an effective beampattern synthesis approach is proposed to realize a low peak sidelobe level (PSLL) and wide-nulling in the presence of array errors, thus improving the consequent performance of the radar. In particular, the covariance matrix of the sidelobe region (CMSR) is incorporated into the optimization. Considering the randomness of array errors, the statistical mean method is adopted to obtain a more accurate calculation of the CMSR in the presence of array errors based on a Monte Carlo trial. To efficiently and effectively solve the optimization problem, an advanced metaheuristic algorithm, i.e., the water cycle algorithm (WCA), is adopted when seeking the corresponding optimal weight vectors. Numerical results are provided and discussed to demonstrate the effectiveness of the proposed approach including the results based on a wideband linearly spaced magneto-electric (ME) dipole array.
Extraction of Citrus Trees from UAV Remote Sensing Imagery Using YOLOv5s and Coordinate Transformation
Authors: Tian Haoxin , Fang Xipeng , Lan Yubin , Ma Chenyang , Huang Huasheng , Lu Xiaoyang , Zhao Dehua , Liu Hanchao , Zhang Yali
Published: August 2022

Obtaining the geographic coordinates of single fruit trees enables the variable rate application of agricultural production materials according to the growth differences of trees, which is of great significance to the precision management of citrus orchards. The traditional method of detecting and positioning fruit trees manually is time-consuming, labor-intensive, and inefficient. In order to obtain high-precision geographic coordinates of trees in a citrus orchard, this study proposes a method for citrus tree identification and coordinate extraction based on UAV remote sensing imagery and coordinate transformation. A high-precision orthophoto map of a citrus orchard was drawn from UAV remote sensing images. The YOLOv5 model was subsequently used to train the remote sensing dataset to efficiently identify the fruit trees and extract tree pixel coordinates from the orchard orthophoto map. According to the geographic information contained in the orthophoto map, the pixel coordinates were converted to UTM coordinates and the WGS84 coordinates of citrus trees were obtained using Gauss–Krüger inverse calculation. To simplify the coordinate conversion process and to improve the coordinate conversion efficiency, a coordinate conversion app was also developed to automatically implement the batch conversion of pixel coordinates to UTM coordinates and WGS84 coordinates. Results show that the Precision, Recall, and F1 Score for Scene 1 (after weeding) reach 0.89, 0.97, and 0.92, respectively; the Precision, Recall, and F1 Score for Scene 2 (before weeding) reach 0.91, 0.90 and 0.91, respectively. The accuracy of the orthophoto map generated using UAV remote sensing images is 0.15 m. The accuracy of converting pixel coordinates to UTM coordinates by the coordinate conversion app is reliable, and the accuracy of converting UTM coordinates to WGS84 coordinates is 0.01 m. The proposed method is capable of automatically obtaining the WGS84 coordinates of citrus trees with high precision.
A Generalized Labeled Multi-Bernoulli Filter Based on Track-before-Detect Measurement Model for Multiple-Weak-Target State Estimate Using Belief Propagation
Authors: Cao Chenghu , Zhao Yongbo
Published: August 2022

In this paper, we propose the specific recursion formula for the generalized labeled multi-Bernoulli filter based on the track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak targets with superior performance. Compared to the Murty algorithm-based and Gibbs sampling-based implementation of GLMB-TBD filter, the proposed algorithm improves the tracking accuracy of multiple weak targets without pruning operation to preserve the relevant association information. The superior performance in tracking accuracy of the algorithm is validated for simulated scenarios using OSPA(2) metric. More importantly, the simulation results demonstrate that the proposed algorithm outputs both the Gibbs sampling-based version and Murty algorithm-based version in computational cost due to linear complex in the number of both Bernoulli components and measurements.
Unmanned Aircraft System Applications in Damage Detection and Service Life Prediction for Bridges: A Review
Authors: Li Hongze , Chen Yanli , Liu Jia , Zhang Zheng , Zhu Hang
Published: August 2022

The increasing need for inexpensive, safe, highly efficient, and time-saving damage detection technology, combined with emerging technologies, has made damage detection by unmanned aircraft systems (UAS) an active research area. In the past, numerous sensors have been developed for damage detection, but these sensors have only recently been integrated with UAS. UAS damage detection specifically concerns data collection, path planning, multi-sensor fusion, system integration, damage quantification, and data processing in building a prediction model to predict the remaining service life. This review provides an overview of crucial scientific advances that marked the development of UAS inspection: underlying UAS platforms, peripherals, sensing equipment, data processing approaches, and service life prediction models. Example equipment includes a visual camera, a multispectral sensor, a hyperspectral sensor, a thermal infrared sensor, and light detection and ranging (LiDAR). This review also includes highlights of the remaining scientific challenges and development trends, including the critical need for self-navigated control, autonomic damage detection, and deterioration model building. Finally, we conclude with a brief discussion regarding the pros and cons of this emerging technology, along with a prospect of UAS technology research for damage detection.
Urban Green Space Planning Based on Remote Sensing and Geographic Information Systems
Authors: Bai Hua , Li Ziwei , Guo Hanlong , Chen Haopeng , Luo Pingping
Published: August 2022

Urban construction land expansion damages natural ecological patches, changing the relationship between residents and ecological land. This is widespread due to global urbanization. Considering nature and society in urban planning, we have established an evaluation system for urban green space construction to ensure urban development residents’ needs while considering natural resource distribution. This is to alleviate the contradiction of urban land use and realize the city’s sustainable development. Taking the Fengdong New City, Xixian New Area as an example, the study used seven indicators to construct an ecological source evaluation system, four types of factors to identify ecological corridors and ecological nodes using the minimum cumulative resistance model, and a Back Propagation neural network to determine the weight of the evaluation system, constructing an urban green space ecological network. We comprehensively analyzed and retained 11 ecological source areas, identified 18 ecological corridors, and integrated and selected 13 ecological nodes. We found that the area under the influence of ecosystem functions is 12.56 km2, under the influence of ecological demands is 1.40 km2, and after comprehensive consideration is 22.88 km2. Based on the results, this paper concludes that protecting, excavating, and developing various urban greening factors do not conflict with meeting the residents’ ecological needs. With consideration of urban greening factors, cities can achieve green and sustainable development. We also found that the BP neural network objectively calculates and analyzes the evaluation factors, corrects the distribution value of each factor, and ensures the validity and practicability of the weights. The main innovation of this study lies in the quantitative analysis and spatial expression of residents’ demand for ecological land and the positive and negative aspects of disturbance. The research results improve the credibility and scientificity of green space construction so that urban planning can adapt and serve the city and its residents.
Spatiotemporal Correlation between Artificially Triggered and Adjacent Natural Lightning Flashes
Authors: Liu Xiaojie , Zheng Dong , Zhang Yang , Zhang Yijun , Fan Xiangpeng , Lyu Weitao , Yao Wen , Fan Yanfeng
Published: August 2022

A triggered lightning flash (TLF) provides a unique perspective on the relationship between spatiotemporal proximity flashes, owing to its determined location and time, convenient direction measurement, and explicit association with the charge region. In this study, 3-D lightning location, current measurement, and atmospheric average electric field (AAEF) data were used to investigate the spatiotemporal relationship of TLFs (68 samples in South China) with adjacent natural lightning flashes (NLFs). The TLF-related negative charge regions had an average core height of 5.2 km and ambient temperature of approximately −1.7 °C. The effective negative charge region (the charge density that was high enough for the occurrence of lightning discharge) can be approximately equivalent to a circle with an average diameter of 10.3 km. For approximately 93% of (all) the TLFs, no NLF channel (initiation) was located within 5 km of the flash-triggered position, within 5 s before and after their occurrence. In situations where spatiotemporally adjacent NLFs and TLFs occurred, they were either associated with different charge layers or the same charge layer but different charge positions. Most NLFs that caused significantly sharp AAEF changes just before or after the TLFs were not associated with the TLF-related negative charge. Therefore, the recovery of the AAEF, which has usually been referenced as the timing choice of the triggering operation, was not directly associated with the TLF-related charge region. The average interval between the TLFs and NLFs that occurred within 10 min before and after the TLFs and neutralized the TLF-related negative charge was approximately 145 s.
Object Tracking in Satellite Videos Based on Improved Kernel Correlation Filter Assisted by Road Information
Authors: Wu Di , Song Haibo , Fan Caizhi
Published: August 2022

Video satellites can stare at target areas on the Earth’s surface to obtain high-temporal-resolution remote sensing videos, which make it possible to track objects in satellite videos. However, it should be noted that the object size in satellite videos is usually small and has less textural property, and the moving objects in satellite videos are easily occluded, which puts forward higher requirements for the tracker. In order to solve the above problems, consider that the remote sensing image contains rich road information, which can be used to constrain the trajectory of the object in a satellite video, this paper proposes an improved Kernel Correlation Filter (KCF) assisted by road information to track small objects, especially when the object is occluded. Specifically, the contributions of this paper are as follows: First, the tracking confidence module is reconstructed, which integrates the peak response and the average peak correlation energy of the response map to more accurately judge whether the object is occluded. Then, an adaptive Kalman filter is designed to adaptively adjust the parameters of the Kalman filter according to the motion state of the object, which improves the robustness of tracking and reduces the tracking drift after the object is occluded. Last but not least, an object tracking strategy assisted by road information is recommended, which searches for objects with road information as constraints, to locate objects more accurately. After the above improvements, compared with the KCF tracker, our method improves the tracking precision by 35.9% and the tracking success rate by 18.1% with the tracking rate at a speed of 300 frames per second, which meets the real-time requirements.
A Model Simplification Algorithm for 3D Reconstruction
Authors: Liu Zhendong , Zhang Chengcheng , Cai Haolin , Qv Wenhu , Zhang Shuaizhe
Published: August 2022

Mesh simplification is an effective way to solve the contradiction between 3D models and limited transmission bandwidth and smooth model rendering. The existing mesh simplification algorithms usually have problems of texture distortion, deformation of different degrees, and no texture simplification. In this paper, a model simplification algorithm suitable for 3D reconstruction is proposed by taking full advantage of the recovered 3D scene structure and calibrated images. First, the reference 3D model scene is constructed on the basis of the original mesh; second, the images are collected on the basis of the reference 3D model scene; then, the mesh and texture are simplified by using the reference image set combined with the QEM algorithm. Lastly, the 3D model data of a town in Tengzhou are used for experimental verification. The results show that the algorithm proposed in this paper basically has no texture distortion and deformation problems in texture simplification and can effectively reduce the amount of texture data, with good feasibility.
Real-Time Weed Control Application Using a Jetson Nano Edge Device and a Spray Mechanism
Authors: Assunção Eduardo , Gaspar Pedro D. , Mesquita Ricardo , Simões Maria P. , Alibabaei Khadijeh , Veiros André , Proença Hugo
Published: August 2022

Portable devices play an essential role where edge computing is necessary and mobility is required (e.g., robots in agriculture within remote-sensing applications). With the increasing applications of deep neural networks (DNNs) and accelerators for edge devices, several methods and applications have been proposed for simultaneous crop and weed detection. Although preliminary studies have investigated the performance of inference time for semantic segmentation of crops and weeds in edge devices, performance degradation has not been evaluated in detail when the required optimization is applied to the model for operation in such edge devices. This paper investigates the relationship between model tuning hyperparameters to improve inference time and its effect on segmentation performance. The study was conducted using semantic segmentation model DeeplabV3 with a MobileNet backbone. Different datasets (Cityscapes, PASCAL and ADE20K) were analyzed for a transfer learning strategy. The results show that, when using a model hyperparameter depth multiplier (DM) of 0.5 and the TensorRT framework, segmentation performance mean intersection over union (mIOU) decreased by 14.7% compared to that of a DM of 1.0 and no TensorRT. However, inference time accelerated dramatically by a factor of 14.8. At an image resolution of 1296×966, segmentation performance of 64% mIOU and inference of 5.9 frames per second (FPS) was achieved in Jetson Nano’s device. With an input image resolution of 513×513, and hyperparameters output stride OS = 32 and DM = 0.5, an inference time of 0.04 s was achieved resulting in 25 FPS. The results presented in this paper provide a deeper insight into how the performance of the semantic segmentation model of crops and weeds degrades when optimization is applied to adapt the model to run on edge devices. Lastly, an application is described for the semantic segmentation of weeds embedded in the edge device (Jetson Nano) and integrated with the robotic orchard. The results show good spraying accuracy and feasibility of the method.
Accuracy Assessment of Low-Cost Lidar Scanners: An Analysis of the Velodyne HDL–32E and Livox Mid–40’s Temporal Stability
Authors: Kelly Carter , Wilkinson Benjamin , Abd-Elrahman Amr , Cordero Orlando , Lassiter H. Andrew
Published: August 2022

Identifying and mitigating sources of measurement error is a critical task in geomatics research and the geospatial industry as a whole. In pursuit of such error, accuracy assessments of lidar data have revealed a range bias in low-cost scanners. This phenomenon is a temporally correlated instability in the lidar scanner where the measured distance between target and sensor changes over time while both are held stationary. This research presents an assessment of two low-cost lidar scanners, the Velodyne® HDL–32E and Livox® Mid–40, in which their temporal stability is analyzed and methods to mitigate systematic error are implemented. By immobilizing each scanner as it observes a stationary target surface over the course of multiple hours, trends in scanner precision are identified. Scanner accuracy is then determined using a terrestrial lidar scanner, the Riegl® VZ-400, to observe both subject scanner and target, and extracting the distances between scanner origin and observed surface. Patterns identified in each scanner’s distance measurements indicate temporal autocorrelation, and, by exploiting the high linear correlation between scanner internal temperature and measured distance in the HDL–32E, it is possible to mitigate the resulting error. Application of the proposed solution lowers the Velodyne® scanner’s measurement RMSE by over 60%, providing levels of measurement accuracy comparable to more expensive lidar systems.
Vertical Structures Associated with Orographic Precipitation during Warm Season in the Sichuan Basin and Its Surrounding Areas at Different Altitudes from 8-Year GPM DPR Observations
Authors: Shen Chengfeng , Li Guoping , Dong Yuanchang
Published: August 2022

Global precipitation measurement (GPM) is one of the effective means employed to observe orographic precipitation, and its inversed GPM DPR data can be used to study the microphysical structure of precipitation particles. This study considers statistics on convective precipitation (CP) and stratiform precipitation (SP) events over three types of terrain (plains, mountains, and high mountains) using the DPR onboard the GPM Core Observatory from May to September of 2014–2021 to analyze the vertical structure of heavy CP and SP. In mountain areas and high mountain areas, the updraft rendered by topography or seeder-feeder mechanism is not only conducive to the collision and merger of raindrops into large raindrops, but also increases the concentration of small drops, which is the main reason why the occurrence probability of not only large but also small raindrops increases and the horizontal distribution domain of mass weighted average raindrop diameter (Dm) widens. For heavy SP, the occurrence probability of medium-diameter precipitation particles below the freezing height (FzH) over high mountains is greater than those over plains. The precipitation particles above 10 km altitude of high mountains have characteristics, such as lower droplet number concentration and larger diameter, compared with those over plains. Furthermore, the study also analyzed the correlation between storm top altitude (STA) and Dm, water vapor and STA respectively. This study is helpful to further understand the effect of topography on heavy precipitation through cloud microphysical processes and the vertical structure of precipitation.
CAiTST: Conv-Attentional Image Time Sequence Transformer for Ionospheric TEC Maps Forecast
Authors: Xia Guozhen , Liu Moran , Zhang Fubin , Zhou Chen
Published: August 2022

In recent years, transformer has been widely used in natural language processing (NLP) and computer vision (CV). Comparatively, forecasting image time sequences using transformer has received less attention. In this paper, we propose the conv-attentional image time sequence transformer (CAiTST), a transformer-based image time sequences prediction model equipped with convolutional networks and an attentional mechanism. Specifically, we employ CAiTST to forecast the International GNSS Service (IGS) global total electron content (TEC) maps. The IGS TEC maps from 2005 to 2017 (except 2014) are divided into the training dataset (90% of total) and validation dataset (10% of total), and TEC maps in 2014 (high solar activity year) and 2018 (low solar activity year) are used to test the performance of CAiTST. The input of CAiTST is presented as one day’s 12 TEC maps (time resolution is 2 h), and the output is the next day’s 12 TEC maps. We compare the results of CAiTST with those of the 1-day Center for Orbit Determination in Europe (CODE) prediction model. The root mean square errors (RMSEs) from CAiTST with respect to the IGS TEC maps are 4.29 and 1.41 TECU in 2014 and 2018, respectively, while the RMSEs of the 1-day CODE prediction model are 4.71 and 1.57 TECU. The results illustrate CAiTST performs better than the 1-day CODE prediction model both in high and low solar activity years. The CAiTST model has less accuracy in the equatorial ionization anomaly (EIA) region but can roughly predict the features and locations of EIA. Additionally, due to the input only including past TEC maps, CAiTST performs poorly during magnetic storms. Our study shows that the transformer model and its unique attention mechanism are very suitable for images of a time sequence forecast, such as the prediction of ionospheric TEC map sequences.
Detecting Object-Level Scene Changes in Images with Viewpoint Differences Using Graph Matching
Authors: Doi Kento , Hamaguchi Ryuhei , Iwasawa Yusuke , Onishi Masaki , Matsuo Yutaka , Sakurada Ken
Published: August 2022

We developed a robust object-level change detection method that could capture distinct scene changes in an image pair with viewpoint differences. To achieve this, we designed a network that could detect object-level changes in an image pair. In contrast to previous studies, we considered the change detection task as a graph matching problem for two object graphs that were extracted from each image. By virtue of this, the proposed network more robustly detected object-level changes with viewpoint differences than existing pixel-level approaches. In addition, the network did not require pixel-level change annotations, which have been required in previous studies. Specifically, the proposed network extracted the objects in each image using an object detection module and then constructed correspondences between the objects using an object matching module. Finally, the network detected objects that appeared or disappeared in a scene using the correspondences that were obtained between the objects. To verify the effectiveness of the proposed network, we created a synthetic dataset of images that contained object-level changes. In experiments on the created dataset, the proposed method improved the F1 score of conventional methods by more than 40%. Our synthetic dataset will be available publicly online.
Evaluation of Global Historical Cropland Datasets with Regional Historical Evidence and Remotely Sensed Satellite Data from the Xinjiang Area of China
Authors: Li Meijiao , He Fanneng , Zhao Caishan , Yang Fan
Published: August 2022

Global land use/cover change (LUCC) datasets are essential for quantitatively assessing the impacts of LUCC on global change, but many uncertainties in existing global datasets seriously hamper climate modeling. Evaluating the reliability of existing global LUCC datasets is a precondition for improved data quality. In this study, based on the regional historical document-based reconstructions, satellite-based data, and historical reclamation evidence for the Xinjiang area of China, the accuracy and rationality of cropland data for this area in the HYDE 3.2 and SAGE datasets were evaluated by utilizing comparative analysis regarding three aspects, namely the change tendency of the cropland area, the area of cropland, and the differences in spatial pattern. This study concluded that the amount of cropland in the Xinjiang area in the global and regional datasets shows both disparate trends and large differences in absolute values. Spatially, historical reclamation evidence indicated that agricultural cultivation in the Xinjiang area underwent expansion from south to north and from east to west over the past 300 years; however, the global datasets revealed that the cropland spatial patterns in the Xinjiang area in the historical period are similar to those in the current period. These differences are attributable to the uncertainties of the basic assumptions, per capita cropland area estimates, and reconstruction methods in the global datasets. The findings of the study highlight the necessity of regional studies on historical LUCC in the Xinjiang area.
Improving Mountain Snow and Land Cover Mapping Using Very-High-Resolution (VHR) Optical Satellite Images and Random Forest Machine Learning Models
Authors: Hu J. Michelle , Shean David
Published: August 2022

Very-high-resolution (VHR) optical imaging satellites can offer precise, accurate, and direct measurements of snow-covered areas (SCA) with sub-meter to meter-scale resolution in regions of complex land cover and terrain. We explore the potential of Maxar WorldView-2 and WorldView-3 in-track stereo images (WV) for land and snow cover mapping at two sites in the Western U.S. with different snow regimes, topographies, vegetation, and underlying geology. We trained random forest models using combinations of multispectral bands and normalized difference indices (i.e., NDVI) to produce land cover maps for priority feature classes (snow, shaded snow, vegetation, water, and exposed ground). We then created snow-covered area products from these maps and compared them with coarser resolution satellite fractional snow-covered area (fSCA) products from Landsat (~30 m) and MODIS (~500 m). Our models generated accurate classifications, even with limited combinations of available multispectral bands. Models trained on a single image demonstrated limited model transfer, with best results found for in-region transfers. Coarser-resolution Landsat and MODSCAG fSCA products identified many more pixels as completely snow-covered (100% fSCA) than WV fSCA. However, while MODSCAG fSCA products also identified many more completely snow-free pixels (0% fSCA) than WV fSCA, Landsat fSCA products only slightly underestimated the number of completely snow-free pixels. Overall, our results demonstrate that strategic image observations with VHR satellites such as WorldView-2 and WorldView-3 can complement the existing operational snow data products to map the evolution of seasonal snow cover.
RI-LPOH: Rotation-Invariant Local Phase Orientation Histogram for Multi-Modal Image Matching
Authors: Tu Huangwei , Zhu Yu , Han Changpei
Published: August 2022

To better cope with the significant nonlinear radiation distortions (NRD) and severe rotational distortions in multi-modal remote sensing image matching, this paper introduces a rotationally robust feature-matching method based on the maximum index map (MIM) and 2D matrix, which is called the rotation-invariant local phase orientation histogram (RI-LPOH). First, feature detection is performed based on the weighted moment equation. Then, a 2D feature matrix based on MIM and a modified gradient location orientation histogram (GLOH) is constructed and rotational invariance is achieved by cyclic shifting in both the column and row directions without estimating the principal orientation separately. Each part of the sensed image’s 2D feature matrix is additionally flipped up and down to obtain another 2D matrix to avoid intensity inversion, and all the 2D matrices are concatenated by rows to form the final 1D feature vector. Finally, the RFM-LC algorithm is introduced to screen the obtained initial matches to reduce the negative effect caused by the high proportion of outliers. On this basis, the remaining outliers are removed by the fast sample consensus (FSC) method to obtain optimal transformation parameters. We validate the RI-LPOH method on six different types of multi-modal image datasets and compare it with four state-of-the-art methods: PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH. The experimental results show that our proposed method has obvious advantages in the success rate (SR) and the number of correct matches (NCM). Compared with PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH, the mean SR of RI-LPOH is 170.3%, 279.8%, 81.6%, and 25.4% higher, respectively, and the mean NCM is 13.27, 20.14, 1.39, and 2.42 times that of the aforementioned four methods.