Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019

Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.

The Infeasibility of Establishing Land Development Rights in China Based on the Path of Real Rights

Using comparative analysis and documentation method,this paper reveals infeasibility of establishing land development rights in China based on the path of real rights,in the hope of providing recommendations for improving the research route on localization of land development rights. Results indicate that at the level of legislative techniques,the land development rights rooted from property right paradigm do not contain possess the elements of object of real rights and conflict with the principle of statutory real rights and single ownership. At the level of legal logic,individual case of TDR conflicts with real right in rem. In conclusion,it is infeasible to introduce land development rights based on the path of real rights. In future,it is required to discard the concept of mechanical transplantation and explore feasible path and seek feasible way for establishing land development rights along with the direction of quasi-property and improving regulation efficiency.

Hawk‐eye‐inspired perception algorithm of stereo vision for obtaining orchard 3D point cloud navigation map

The binocular stereo vision is the lowest cost sensor for obtaining 3D information.Considering the weakness of long‐distance measurement and stability,the improvement of accuracy and stability of stereo vision is urgently required for application of precision agriculture.To address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade,inspired by hawk eyes,higher resolution perception and the adaptive HDR(High Dynamic Range)were introduced in this paper.Simulating the function from physiological structure of‘deep fovea’and‘shallow fovea’of hawk eye,the higher resolution reconstruction method in this paper was aimed at ac-curacy improving.Inspired by adjustment of pupils,the adaptive HDR method was proposed for high dynamic range optimisation and stable perception.In various light conditions,compared with default stereo vision,the accuracy of proposed algorithm was improved by 28.0%evaluated by error ratio,and the stability was improved by 26.56%by disparity accuracy.For fixed distance measurement,the maximum improvement was 78.6%by standard deviation.Based on the hawk‐eye‐inspired perception algorithm,the point cloud of orchard was improved both in quality and quantity.The hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud recon-struction in orchard navigation map.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Paired regions for shadow removal approach based on multi-features

The existence of shadow leads to the degradation of the image qualities and the defect of ground object information.Shadow removal is therefore an essential research topic in image processing filed.The biggest challenge of shadow removal is how to restore the content of shadow areas correctly while removing the shadow in the image.Paired regions for shadow removal approach based on multi-features is proposed, in which shadow removal is only performed on related sunlit areas.Feature distance between regions is calculated to find the optimal paired regions with considering of multi-features(texture, gradient feature, etc.) comprehensively.Images in different scenes with peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) evaluation indexes are chosen for experiments.The results are shown with six existing comparison methods by visual and quantitative assessments, which verified that the proposed method shows excellent shadow removal effect, the brightness, color of the removed shadow area, and the surrounding non-shadow area can be naturally fused.

Deep Learning and Network Analysis:Classifying and Visualizing Geologic Hazard Reports

If progress is to be made toward improving geohazard management and emergency decision-making,then lessons need to be learned from past geohazard information.A geologic hazard report provides a useful and reliable source of information about the occurrence of an event,along with detailed information about the condition or factors of the geohazard.Analyzing such reports,however,can be a challenging process because these texts are often presented in unstructured long text formats,and contain rich specialized and detailed information.Automatically text classification is commonly used to mine disaster text data in open domains(e.g.,news and microblogs).But it has limitations to performing contextual long-distance dependencies and is insensitive to discourse order.These deficiencies are most obviously exposed in long text fields.Therefore,this paper uses the bidirectional encoder representations from Transformers(BERT),to model long text.Then,utilizing a softmax layer to automatically extract text features and classify geohazards without manual features.The latent Dirichlet allocation(LDA)model is used to examine the interdependencies that exist between causal variables to visualize geohazards.The proposed method is useful in enabling the machine-assisted interpretation of text-based geohazards.Moreover,it can help users visualize causes,processes,and other geohazards and assist decision-makers in emergency responses.

Analyzing Asymmetric City Connectivity by Toponym on Social Media in China

The connectedness between cities has become one of the most widely discussed topics in urban and regional research in the mobile and big data era. One problem identified is the asymmetric city connectivity, partially due to data availability. We present a data-driven approach based on location and toponym(place name) extracted from social media data, to assess the asymmetric connectivity between cities. The assumption is that a higher frequency of occurrences of the name of city i in posts located in city j would imply that the city i is more influential than other cities upon city j. In addition, we’ve developed a group of measurements such as the relatedness index, impact index, link strength index, dependence index, and structure similar index to characterize such interactions. This framework of connectivity measurements can also be used to support smart planning taking into account the evolving interplay among cities. The space-time structure of urban systems in China is examined as the case study.

Integrated representation of geospatial data,model,and knowledge for digital twin railway

The real-time accurate description of all spatial features of railway and their spatiotemporal relationships is a crucial factor in realizing comprehensive management and related decision-making within the entire life cycle of railways.Nevertheless,available spatiotemporal data models mainly use static historical sequence data,which are insufficient to support multi-source heterogeneous real-time sensed data;they lack a systematic depiction of the interactive relationships among multiple feature entities,and are limited to low-level descriptive analysis.Therefore,this study proposes a data-model-knowledge integrated representation data model for a digital twin railway,which explicitly describes the spatiotemporal,and interaction relationships among railway features through a conceptual knowledge graph.This study first analyzes the characteristics of railway features from above ground to underground,and then constructs a conceptual model to clearly describe the complex relationships among railway features.Secondly,a logical model is developed to illustrate the basic data structure.Thirdly,an ontology model is constructed as a basic framework for further deepening the domain knowledge graph.Finally,considering the prevention of landslides as an example,it demonstrates the abundant spatiotemporal relationships among railway related features.The results of this study bring more clear understanding of the complex interactive relationships of railway entities.

Lagrangian eddies in the Northwestern Pacifi c Ocean

The Lagrangian eddies in the western Pacifi c Ocean are identifi ed and analysed based on Maps of Sea Level Anomaly(MSLA)data from 1998 to 2018.By calculating the Lagrangian eddy advected by the AVISO velocity fi eld,we analyzed the variations in Lagrangian eddies and the average transport eff ects on diff erent time scales.By introducing the Niño coeffi cient,the lag response of the Lagrangian eddy to El Niño is found.These data are helpful to further explore the role of mesoscale eddies in ocean energy transfer.Through normalized chlorophyll data,we observed chlorophyll aggregation and hole eff ects caused by Lagrangian eddies.These fi ndings demonstrate the important role of Lagrangian eddies in material transport.The transportation volume of the Lagrangian eddy is calculated quantitatively,and several major transport routes have been identifi ed,which helps us to more accurately and objectively estimate the transport capacity of Lagrangian eddies in the western Pacifi c Ocean.

Holdout Problem and Its Governance in Market-Oriented Urban Renewal: Taking Shenzhen as an Example

This paper studies the occurrence, characteristics, and governance mechanism of the holdout problem during market-oriented urban renewal in Shenzhen after a related foreign experience analysis, through methodologies of logical analysis and case study.The results indicate that the holdout problem is almost an inevitable result of market-oriented urban renewal, and its proliferation not only goes against social morality and the principle of justice, but also harms social public interests, and even causes market failure.The paper proposes two approaches to dealing with holdout problem: first, to introduce the contracting commitment to restrict the owners’ freedom of contract, and to guarantee the public benefit of all owners;second, to introduce land expropriation right to regulate holdout behavior via authority of state.

SAR image despeckling via Lp norm regularization

Synthetic aperture radar(SAR) image despeckling has been an attractive problem in remote sensing.The main challenge is to suppress speckle while preserving edges and preventing unnatural artifacts(such as annoying artifacts in homogeneous regions and over-smoothed edges).To address these problems,this paper proposes a new variational model with a nonconvex nonsmooth Lp(0 <p<1) norm regularization.It incorporates Lp(0<p<1) norm regularization and I-divergence fidelity term.Due to the nonconvex nonsmooth property,the regularization can better recover neat edges and homogeneous regions.The Ⅰ-divergence fidelity term is used to suppress the multiplicative noise effectively.Moreover,based on variable-splitting and alternating direction method of multipliers(ADMM) method,an efficient algorithm is proposed for solving this model.Intensive experimental results demonstrate that nonconvex nonsmooth model is superior to other state-of-the-art approaches qualitatively and quantitatively.

Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports

Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events.

Mathematical model for predicting fungal growth and decomposition rates based on improved Logistic equations

As the function of the decomposition of fungi has been clearly researched in the global carbon cycle,it is obviously of value to explore the decomposition rate of fungal populations.This study analyzed the relationship between environmental factors and biodiversity step by step.In order to explore the interaction between the fungi and the relationship between the decomposition rate of fungi with time,the model based on the Logistic model was built and the Lotka-Volterra model was employed in the condition of two kinds of fungi existing in an environment with limited resources.The changing trend of population number and decomposition rate of several fungi under different environmental conditions can be predicted through the model.To illustrate the applicability of the model,Laetiporus conifericola and Hyphoderma setigerum were applied as examples.The results showed that the higher the degree of population diversity,the greater the decomposition rate,and the higher the decomposition efficiency of the ecosystem.Its rich species diversity is conducive to accelerating the decomposition of litter,lignocellulose,and the circulation of the entire ecosystem.Based on the above model and using the data from measuring the mycelial elongation rate of each isolate at 10℃,16℃,and 22℃ under standardized laboratory conditions,the growth patterns of the five fungi combinations were simulated.The results revealed a general increase in growth rate with increasing temperature,which verifies the accuracy of the model.Moreover,it also revealed that the total decomposition rate after fungal incorporation was negatively correlated with the decomposition rate of a fungal single action.Based on the above model,predictions can be made for fungal growth in different environments,and suitable environments for fungal growth can be determined.In the future,the model can be further optimized,and lignin and cellulose decomposition factors can be added to fit the decomposition of logs.The application scenarios of the model can be further broadened,which can contribute to the restoration and management of the ecological environment,as well as produce good effects in the fields of fungi assisting the global carbon cycle and soil problem restoration.

M-ISFCM:A Semisupervised Method for Anomaly Detection of MOOC Learning Behavior

Massive online courses(MOOCs)are becoming increasingly vital in the modern era,yet tools to track and detect MOOC learners’progress are inadequate.In reality,labeled MOOC data are difficult to acquire,whereas unlabeled data make up the majority of the data,and these massive unlabeled data are difficult to analyze,resulting in data waste.This paper tackles this issue by presenting a MOOC learning behavior anomaly detection model(M-ISFCM)for the supervision and inspection of MOOC learners’learning that combines semisupervised fuzzy C-mean clustering(SFCM)and an isolated forest algorithm.To optimize MOOC data usage,the model leverages unlabeled and labeledMOOCdata as prior assumptions.The MOOC detection runtimes are enhanced by integrating the outliers of the isolated forest approach in SFCM.The results show that the model has a higher precision rate,recall rate,andAUC than the traditional anomaly models in MOOC data.Therefore,the model is effective for recognizing anomalous MOOC learning behaviors.

可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

CW-YOLO: joint learning for mask wearing detection in low-light conditions

1 Introduction With the rapid progress of Artificial Intelligence(AI)technology in object detection and face recognition,deep learning methods for face mask wearing detection have become increasingly mature and continuously take into account the needs of efficiency and accuracy.However,these conventional detection methods mostly ignore the complexity of real-world application scenarios,such as extremely darkness and gloomy weather.These unfavorable conditions lead to a series of image degradations that seriously hamper machine vision tasks.Although camera parameter adjustment,auxiliary lighting,or pre-processing enhancement[1]can weaken these negative effects to some extent to promote the detection,they will also result in increased hardware and time costs.

Snake-Hot-Eye-Assisted Multi-Process-Fusion Target Tracking Based on a Roll-Pitch Semi-strapdown Infrared Imaging Seeker

Aiming at intercepting large maneuvering targets precisely,the guidance law of advanced self-seeking missiles requires not only inertial line-of-sight(LOS)angular rate but also target maneuvering acceleration.Moreover,the semi-strapdown stabilization platform has lost the ability to measure the inertial LOS angular rate directly,which needs to be extracted by numerical calculation.The differential operation commonly used in traditional methods can magnify the measurement error of the sensor,resulting in insufficient calculation accuracy of the line-of-sight angular rate.By analyzing the mathematical relationship between the missile-target relative motion and the angle tracking system,a multi-process-fusion integrated filter model of relative motion and angle tracking is presented.To overcome the defect that the infrared seeker cannot directly measure the missile-target distance,following the snake-hot-eye visual mechanism,a visual bionic imaging guidance method of estimating the missile-target relative distance from the infrared images is proposed to improve the observability of the filter model.Finally,target-tracking simulations verify that the estimation accuracy of target acceleration is improved by four times.

Volunteered remote sensing data generation with air passengers as sensors

Remote sensing satellites are playing very important roles in diverse earth observation fields.However,long revisit period,high cost and dense cloud cover have been the main limitations of satellite remote sensing for a long time.This paper introduces the novel volunteered passenger aircraft remote sensing(VPARS)concept,which can partly overcome these problems.By obtaining aerial imaging data from passengers using a portable smartphone on a passenger aircraft,it has various advantages including low cost,high revisit,dense coverage,and partial anti-cloud,which can well complement conventional remote sensing data.This paper examines the concept of VPARS and give general data processing framework of VPARS.Several cases were given to validate this processing approach.Two preliminary applications on land cover classification and economic activity monitoring validate the applicability of the VPARS data.Furthermore,we examine the issues about data maintenance,potential applications,limitations and challenges.We conclude the VPARS can benefit both scientific and industrial communities who rely on remote sensing data.

Unsupervised GRNN flood mapping approach combined with uncertainty analysis using bi-temporal Sentinel-2 MSI imageries

Floods occur frequently worldwide.The timely,accurate mapping of the flooded areas is an important task.Therefore,an unsupervised approach is proposed for automated flooded area mapping from bitemporal Sentinel-2 multispectral images in this paper.First,spatial–spectral features of the images before and after the flood are extracted to construct the change magnitude image(CMI).Then,the certain flood pixels and non-flood pixels are obtained by performing uncertainty analysis on the CMI,which are considered reliable classification samples.Next,Generalized Regression Neural Network(GRNN)is used as the core classifier to generate the initial flood map.Finally,an easy-toimplement two-stage post-processing is proposed to reduce the mapping error of the initial flood map,and generate the final flood map.Different from other methods based on machine learning,GRNN is used as the classifier,but the proposed approach is automated and unsupervised because it uses samples automatically generated in uncertainty analysis for model training.Results of comparative experiments in the three sub-regions of the Poyang Lake Basin demonstrate the effectiveness and superiority of the proposed approach.Moreover,its superiority in dealing with uncertain pixels is further proven by comparing the classification accuracy of different methods on uncertain pixels.

An event-driven dynamic updating method for 3D geo-databases

With the rapid development of 3D Digital City, the focus of research has shifted from 3D city modeling and geo-database construction to 3D geo-database service and maintenance. The frequent modifications on geometry, texture, attribute, and topology present a great challenge to the 3D geo-database updating.This article proposes an event-driven spatiotemporal database model (ESDM) that combines the historical and present 3D city models with the semantic classification and state expression, triggered by changing events predefined. In addition, a corresponding dynamic updating method based on adaptive matching algorithm is presented to perform the dynamic updating operation for the complex 3D city models automatically, according to the compound matching of semantics, attributes, and spatial locations. finally, the validity and feasibility of the proposed ESDM and its updating method are demonstrated through a 3D geo-database with more than 1.5 million 3D city models.