The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data

With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

Evaluation of the Protégé^(TM) stent in the treatment of carotid artery stenosis with adjunctive use of a filter Embolic Protection Device (PROCAR)-one-month follow-up data on 77 patients

Objective The trial was designed to evaluate the safety and performance of the ev3 Protégé TM stent in the treatment of de novo or re-stenotic common and/or internal carotid artery stenoses with adjunctive use of a CE-marked filter embolic protection device.Methods This study was a prospective multi-center, single-arm trial. Between June and October 2003, 77 patients were enrolled in 8 investigational centers throughout Europe. The primary endpoint was the incidence of Major Neurological Events (MANE) through one month. Other endpoints were the ability to properly place the stent, and primary patency and MANE after six months. Eligible for the study were patients with a de novo or restenotic target lesion located in the common and/or internal carotid artery (>70% stenosis for asymptomatic and >50% stenosis for symptomatic patients). The ev3 Spider (Embolic Protection Filter was used in 75 of 77 cases. Results In 76 out of the 77 patients (99%), the stent could be successfully implanted with a residual stenosis ≤30% as criterion. Of the 74 patients that had a carotid ultrasound at one month follow-up, none had a re-stenosis of the target lesion. There were three MANEs during or immediately after the procedure (3.9%), two were major and one was a minor stroke. There were eight severe complications (9.1%); six of these happened during or immediately after the procedure and were related to the procedure, none was related to the device. They are resolved without sequelae. No deaths have occurred.Conclusions The Protégé stent is safe and performs well in the treatment of carotid artery stenosis. The technical success rate for placement of the Protégé stent as assessed by the residual stenosis post implant was very high and all stents were successfully deployed. The incidence of MANE was comparable with that in other recent carotid stent studies and still lower than standard CEA.

Study on Residential Water Use in North China:Analysis of Nationwide Statistical Data and Case Study of Taiyuan City

Residential water use is gradually becoming the focus in China's municipal water supply planning and management in recent years.Little is known,however,about the residential water use in modern China due to the transition of economy and enhancement of management on water conservation.In order to better understand the characteristics of residential water use in North China,a model for identifying the determinants of residential water use was established and analyzed by using panel data and cross-section data methodologies.Then Taiyuan city,the capital city of Shanxi Province in Northern China was selected as a case study.Both the analyses and field investigation indicate that the relatively slow increase of residential water use in recent years may result from the implementation of strict laws and regulations on water conservation.And through the investigation, first-hand information about water consumption pattern,water reuse/conservation,people's attitude toward water quantity and quality,etc.have been obtained.

Use of Data Mining to Support the Development of Knowledge Intensive CAD

In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.

Land Use Change in Hongta District in Yuxi City, China Based on Archived Landsat Data of the Past 30 Years

Urban expansion, which has affected ecosystem functioning and services at local to global scale, is projected to have aggravating impacts on landscape in the future. A detailed study on spatiotemporal patterns of Hongta District in Yuxi City over a relatively long timeframe was conducted using multi-temporal Landsat TM, ETM+ and OLI data of 1987-2017 associated with Geographic Information System techniques and landscape analysis approaches. Results showed that sealed surfaces (urban area) have expanded from 1.4% to 11.8%, with an annually increasing rate of 7.63%. Trees, farm areas and barren lands all underwent a slight reduction during this time period, whereby 23.8% of farms and 9.8% of bare lands transferred into sealed surfaces. Focusing on the urban expansion pattern, it presents a uninuclear polarizing polygon mode illustrating the urbanization sprawl over time to all directions. The greatest expansions are seen in NNE and SSE direction, mainly because of the topographic constraints. A trade-off between urbanization and sustainable development is under investigation by reclaiming barren soil at the mountain range to transfer farming areas from low altitude plains to make way for urban growth.

Personal Data Use and Morality in the E-Business Environment

This paper deals with personal data use by firms in the e-business environment from the viewpoint of business administration and information ethics. Whereas the tremendous development of information and communication technology (ICT) has made it easier for firms to acquire, store, share, and utilise personal data on their customers, firms that use personal data are exposed to risks related to privacy issues. Since individuals fear the invasion of their privacy, the failure of a firm to appear or remain trustworthy would make it difficult for it to maintain accurate, up-to-date databases and to construct desirable business processes, which would affect the bottom line. Therefore, modern firms should do what they can to ensure that their customers trust them. For them, one promising way to remain trustworthy is to behave as a moral agent. Although it is difficult for any firm to meet the conditions necessary to be a moral agent, competence in behaving as a moral agent is a hard-to-imitate capability af firms for which personal data use is vital for enjoying the benefits of business relationships in the e-business environment.

A Diachronic Perspective on the Use of French Spatial Data Infrastructures

Despite the recent development of many worldwide initiatives, there is still a need for the development of observation frameworks that will provide a comprehensive view of SDI’s use. Amongst the many challenges left, a thorough analysis of the information flows between existing SDIs as well as their respective uses and the way that those evolve over time is an important issue to explore. The research presented in this paper introduces a methodological framework oriented to the study of the SDIs use from a diachronic perspective. The approach is based on a Social Network Analysis (SNA) and questionnaires collected by online surveys. We develop a structural and diachronic analysis based on a series of graph-based measures identifying the main patterns that appear over time. The methodological framework is applied to a series of French SDIs and users involved in environmental management. The study identifies a series of structural differences in the data flows that emerge between the users and SDIs. Last, the diachronic network analysis provides an overall understanding on how data flows evolve over time at different institutional levels.

Research on Evaluation and Optimization of Regional Land Use Structure Efficiency

[Objective] The purpose of this study is to judge the rationality of land use structure from new perspectives and method. [Method] The theory of data en- velopment analysis （DEA） has been employed in this article. A case study of evalu- ation of land use structure for Wujin district in 2012 is provided to illustrate the ap- plication of this research model. At the micro perspective, land use efficiency has been analyzed by use of data envelopment analysis model. The optimization and adjustment direction in land use structure has been pointed out from the view point of land use efficiency. [Result] The result has shown that the research method based on information entropy and data envelopment analysis model can effectively overcome the drawback of traditional evaluation techniques. Compared with the tra- ditional methods, the new optimization model based on a structure of the multi-crite- ria factors and objective weighting method can evaluate the rationality of land use structure more comprehensively.

Bayesian framework for satellite rechargeable lithium battery synthesizing bivariate degradation and lifetime data

Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data,including bivariate degradation data and lifetime data.Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system.First,linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB's temperature and discharge voltage.Next,the Bayesian method,in combination with Markov Chain Monte Carlo(MCMC)simulations,is provided to integrate limited bivariate degradation data with other congeneric RLBs'lifetime data.Then reliability evaluation and RUL prediction are carried out for PHM.A simulation study demonstrates that due to the data fusion,parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes.Finally,a practical case study of a satellite RLB verifies the usability of the model.

Facilitating transparency in spinal cord injury studies using data standards and ontologies

Progress in developing robust therapies for spinal cord injury （SCI）, trau- matic brain injury （TBI） and peripheral nerve injury has been slow. A great deal has been learned over the past 30 years regarding both the intrinsic factors and the environmental factors that regulate axon growth, but this large body of information has not yet resulted in clinically available thera- peutics. This therapeutic bottleneck has many root causes, but a consensus is emerging that one contributing factor is a lack of standards for experi- mental design and reporting. The absence of reporting standards, and even of commonly accepted definitions of key words, also make data mining and bioinformatics analysis of neural plasticity and regeneration difficult, if not impossible. This short review will consider relevant background and poten- tial solutions to this problem in the axon regeneration domain.

Commentary on:“Facilitating transparency in spinal cord injury studies using data standards and ontologies”

Commentary Most would agree that providing comprehensive detail in scientific reporting is critical for the development of mean- ingful therapies and treatments for diseases. Such stellar practices 1） allow for reproduction of experiments to con- firm results, 2） promote thorough analyses of data, and 3） foster the incremental advancement of valid approaches. Unfortunately, most would also agree we have far to go to reach this vital goal （Hackam and Redelmeier, 2006; Prinz et al., 2011; Baker et al., 2014）.

Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data

As a key parameter for indicating the fraction of surface-reflected solar incident radiation, land surface albedo plays an important role in the Earth’s surface energy budget(SEB). Since the Sanjiang Plain has been severely affected by human activities(e.g., reclamation and shrinking of wetlands), it is important to assess the spatiotemporal variations of surface albedo in this region using a long-term remote sensing dataset. In order to investigate the surface albedo climatology, trends, and mechanisms of change, we evaluated the surface albedo variations in the Sanjiang Plain, China from 1982 to 2015 using the Global LAnd Surface Satellite(GLASS) broadband surface albedo product. The results showed that: 1) an increasing annual trend(+0.000 58/yr) of surface albedo was discovered in the Sanjiang Plain based on the GLASS albedo dataset, with a much stronger increasing trend(+0.001 26/yr) occurring during the winter. Most of the increasing trends occurred over the cultivated land, unused land, and land use conversion types located in the northeastern Sanjiang Plain. 2) The increasing trend of land surface albedo in Sanjiang Plain can be largely explained by the changes of both snow cover extent and land use. The surface albedo in winter is highly correlated with the snow cover extent in the Sanjiang Plain, and the increasing trend of surface albedo can be further enhanced by the land use changes.

Progress of Geological Survey Using Airborne Hyperspectral Remote Sensing Data in the Gansu and Qinghai Regions

Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.

The impact of Bt cotton adoption on the stability of pesticide use

Even though the impact of Bacillus thuringiensis （Bt） cotton on pesticide use has been well documented, all previous stud- ies focus on the mean value of pesticide use. Using seven unique waves of panel data collected between 1999 and 2012 in China, we show that Bt cotton adoption has not only caused a reduction of the mean value of pasticide use, but also a reduction of the standard deviation of pesticide use. We conclude that Bt technology adoption has also contributed to the stability of pesticide use in cotton production. We believe that this contribution is theoretically and practically relevant because of the long length of our unique dataset.

A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.

Satellite Data Reduction Using Entropy-preserved Image Compression Technique

In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.

Mixture Models for Estimating the Number of Drug Users in Thailand 2005-2007

It is difficult to measure the sizes of illegal drug user populations directly by using the survey method because of many “hidden drug addicts” and the difficulty of receiving a true response. Systematic and routine information on treatment episodes of drug users is adopted to estimate the population size in this study. Mixture models of zero-truncated Poisson distributions using the nonparametric maximum likelihood estimators (NPMLE) by means of capture-recapture repeated count data were used to project the number of drug users. The method was applied to surveillance data of drug users identified by treatment episodes in over 1140 health treatment centers in Thailand from the Bureau of Health Service System Development, Ministry of Public Health. We presented how this mixture model could be utilized to construct the unobserved frequency of drug users with no treatment episode and further estimated the total population size of drug users in the country from 2005 to 2007. The result of simulation was confirmed that mixture model is suitable when population is large. By means of mixture models, the estimations for the number of drug users were fitted with excellent goodness-of-fit values and we were also compared to the conventional Chao estimates. The NPMLE for the total number of drug users in Thailand 2005, 2006, and 2007 were 184,045 (95% CI: 181,297-86,793), 230,665 (95% CI: 226,611-234,719), 299,670 (95% CI: 294,217-305,123), respectively, also 125,265 (95% CI: 123,092-127,142), 166,287 (95% CI: 163,222-169,352), 228,898 (95% CI: 224,766 - 233,030) for the number of methamphetamine (Yaba) users, and 11,559 (95% CI: 10,234-12,884), 11,333 (95% CI: 9276-13,390), 8953 (95% CI: 7878-10,028) for the number of heroin users, respectively. The numbers of marijuana, kratom-plant, opium, and inhalant users were underestimated because their symptoms were mild and not severe enough to remedy in health treatment centers which led to the smaller size of the total number of drug users. The well-estimated sizes of heroin and methamphetamine addicts are high reliable because they are based on clearly evident count with a severe addiction problem to health treatment centers. The estimation by means of mixture models can be recommended to monitor drug demand trend and drug health service routinely;it is easy to calculate via the available programs MIXTP based on request.

Challenges using electronic nursing routine data for outcome analyses:A mixed methods study

Objectives To explore the challenges of secondary use of routinely collected data for analyzing nursing-sensitive outcomes in Austrian acute care hospitals.Method A convergent parallel mixed methods design was performed.We conducted a quantitative representative survey with nursing managers from 32 Austrian general acute care hospitals and 11 qualitative semi-structured interviews with nursing quality management experts.Both results were first analyzed independently and afterward merged in the discussion.Results On average,76%of nursing documentation is already electronically supported in the surveyed Austrian hospitals.However,existing nursing data is seldom used for secondary purposes such as nursing-sensitive outcome analyses.This is due to four major reasons:First,hospitals often do not have a data strategy for the secondary use of routine data.Second,hospitals partly lack the use of standardized and uniform nursing terminologies,especially for nursing evaluation.Third,routine nursing data is often not documented correctly and completely.Fourth,data on nursing-sensitive outcomes is usually collected in specific documentation forms not integrated into routine documentation.Conclusion The awareness of the possibilities for secondary use of nursing data for nursing-sensitive outcome analyses in Austrian hospitals is still in its infancy.Therefore,nursing staff and nursing management must be trained to understand how to collect and process nursing data for nursing-sensitive outcome analyses.Further studies would be interesting in order to determine the factors that influence the decision-making processes for the secondary use of nursing data for outcome analyses.