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Alfalfa is a valuable and widely adapted forage crop, and its nutritive value directly affects animal performance and ultimately affects the profitability of livestock production. Traditional nutritive value measurement method is labor-intensive and time-consuming and thus hinders the determination of alfalfa nutritive values over large fields. The adoption of unmanned aerial vehicles (UAVs) facilitates the generation of images with high spatial and temporal resolutions for field-level agricultural research. Additionally, compared with other imaging modalities, hyperspectral data usually consist of hundreds of narrow spectral bands and allow the accurate detection, identification, and quantification of crop quality. Although various machine-learning methods have been developed for alfalfa quality prediction, they were all single-task models that learned independently for each quality trait and failed to utilize the underlying relatedness between each task. Inspired by the idea of multitask learning (MTL), this study aims to develop an approach that simultaneously predicts multiple quality traits. The algorithm first extracts shared information through a long short-term memory (LSTM)-based common hidden layer. To enhance the model flexibility, it is then divided into multiple branches, each containing the same or different number of task-specific fully connected hidden layers. Through comparison with multiple mainstream single-task machine-learning models, the effectiveness of the model is illustrated based on the measured alfalfa quality data and multitemporal UAV-based hyperspectral imagery.

Emergency mapping is a task that requires abundant professional knowledge and complex operations, especially in high timeliness demands in disaster scenarios. The operation of the conventional cart...

Understanding population distribution has excellent applications for planning and provision of municipal services. This study aims to explore the space-time structure of population distribution wit...

Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely preserved. Geographic information retrieval (GIR) methods have focused on this issue; however, they sometimes fail to solve the problem because the spatial and textual similarities of words are considered and calculated separately. In this paper, from the perspective of spatial context, we consider the two parts as a whole—spatial context semantics, and we propose a method that measures spatial semantic similarity using a sliding geospatial context window for geo-tagged words. The proposed method was first validated with a set of simulated data and then applied to a real-world dataset from Flickr. As a result, a spatial semantic similarity model at different scales is presented. We believe this model is a necessary supplement for traditional textual-language semantic analyses of words obtained by word-embedding technologies. This study has the potential to improve the quality of recommendation systems by considering relevant spatial context semantics, and benefits linguistic semantic research by emphasising the spatial cognition among words.

Emergency remote sensing mapping can provide support for decision making in disaster assessment or disaster relief, and therefore plays an important role in disaster response. Traditional emergency remote sensing mapping methods use decryption algorithms based on manual retrieval and image editing tools when processing sensitive targets. Although these traditional methods can achieve target recognition, they are inefficient and cannot meet the high time efficiency requirements of disaster relief. In this paper, we combined an object detection model with a generative adversarial network model to build a two-stage deep learning model for sensitive target detection and hiding in remote sensing images, and we verified the model performance on the aircraft object processing problem in remote sensing mapping. To improve the experimental protocol, we introduced a modification to the reconstruction loss function, candidate frame optimization in the region proposal network, the PointRend algorithm, and a modified attention mechanism based on the characteristics of aircraft objects. Experiments revealed that our method is more efficient than traditional manual processing, the precision is 94.87%, the recall is 84.75% higher than that of the original mask R-CNN model, and the F1-score is 44% higher than that of the original model. In addition, our method can quickly and intelligently detect and hide sensitive targets in remote sensing images, thereby shortening the time needed for emergency mapping.

In the context of Industry 4.0, the high-profile false data injection (FDI) attacks are posing increasing cyber threats to the reliability of smart grids. Recent studies have investigated the possibilities of detecting FDI attacks on smart grids by using the distributed flexible AC transmission system (D-FACTS) devices. However, few studies focus on further locating such cyber threats using D-FACTS devices. To meet this gap, we systematically explored such a topic and propose a graph theory based scheme to locate FDI attacks by employing D-FACTS devices, where both single-bus FDI attacks and multiple-bus FDI attacks are considered. Numerical results on the standard IEEE 14-bus system demonstrated that the proposed scheme can achieve 100% accuracy when locating any single-bus FDI attacks and most of the independent multiple-bus FDI attacks. Future potential solutions are also discussed to some special cases of multiple-bus FDI attacks that the proposed scheme cannot well handle.

Location services based on address matching play an important role in people&rsquo, s daily lives. However, with the rapid development of cities, new addresses are constantly emerging. Due to the untimely updating of word segmentation dictionaries and address databases, the accuracy of address segmentation and the certainty of address matching face severe challenges. Therefore, a new address element recognition method for address matching is proposed. The method first uses the bidirectional encoder representations from transformers (BERT) model to learn the contextual information and address model features. Second, the conditional random field (CRF) is used to model the constraint relationships among the tags. Finally, a new address element is recognized according to the tag, and the new address element is put into the word segmentation dictionary. The spatial information is assigned to it, and it is put into the address database. Different sequence tagging models and different vector representations of addresses are used for comparative evaluation. The experimental results show that the method introduced in this paper achieves the maximum generalization ability, its F1 score is 0.78, and the F1 score on the testing dataset also achieves a high value (0.95).

Alfalfa is a valuable and intensively produced forage crop in the United States, and the timely estimation of its yield can inform precision management decisions. However, traditional yield assessment approaches are laborious and time-consuming, and thus hinder the acquisition of timely information at the field scale. Recently, unmanned aerial vehicles (UAVs) have gained significant attention in precision agriculture due to their efficiency in data acquisition. In addition, compared with other imaging modalities, hyperspectral data can offer higher spectral fidelity for constructing narrow-band vegetation indices which are of great importance in yield modeling. In this study, we performed an in-season alfalfa yield prediction using UAV-based hyperspectral images. Specifically, we firstly extracted a large number of hyperspectral indices from the original data and performed a feature selection to reduce the data dimensionality. Then, an ensemble machine learning model was developed by combining three widely used base learners including random forest (RF), support vector regression (SVR) and K-nearest neighbors (KNN). The model performance was evaluated on experimental fields in Wisconsin. Our results showed that the ensemble model outperformed all the base learners and a coefficient of determination (R2) of 0.874 was achieved when using the selected features. In addition, we also evaluated the model adaptability on different machinery compaction treatments, and the results further demonstrate the efficacy of the proposed ensemble model.

The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.

Landslides are a type of serious geologic disaster causing great damage to the human environment. Landslide susceptibility mapping is an effective means to reduce landslide risk. However, previous studies have not considered spatial heterogeneity. In this study, a hybrid model considering spatial heterogeneity is designed by integrating GeoSOM and Stacking ensemble methods and is applied to map the landslide susceptibility of Zhejiang Province, China. The GeoSOM method was used to cluster the study area into several homogeneous regions to solve the heterogeneity problem, and each region was assigned a cluster attribute as one of the landslide model inputs. The Stacking ensemble technique was utilized to design a high-performance landslide model by combining three traditional machine learning methods (support vector machine (SVM), artificial neural network (ANN), and gradient-boosting decision tree (GBDT)). We collected 1051 landslide samples and fourteen affecting factors after feature selection. For landslide modelling, the landslides were randomly split into two subsets: 70% samples for training and the rest for validation. Landslide models were assessed by the receiver operating characteristic (ROC) curve and statistical measures. The results indicated that the hybrid model was 0.11–0.135 higher than those of traditional machine learning methods in term of the area under the ROC curve (AUC). In general, this hybrid model can generate high-quality landslide susceptibility maps and help to develop policies that reduce the burden of landslides.

Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.

In urban areas, the spatial configuration of urban elements is vital to the sustainable development of cities. Due to the inherent spatial and networked characteristics of UBNs, research on the spatial configuration of UBNs requires a comprehensive approach involving the integration of spatial and network analysis methods. We propose a methodological framework for geospatial network analysis that combines spatial and network analysis to analyse the spatial configuration of UBNs. This framework is based on the construction of an urban bus spatial network (UBSN) in which the network nodes are spatial division units of urban space with more realistic geographical significance. Then, the framework analyses the spatial configuration of the UBN from the identification of macroscopic statistical characteristics to reveal the explicit roles played by microscopic elements such as individual nodes and to further identify the mesoscopic structural organization. The importance of nodes has spatial heterogeneity and directional imbalance. The community detection results demonstrate the UBSN presents obvious spatial agglomeration features, and the overlapping nodes are mostly distributed in the suburbs. With a novel viewpoint and detailed description of the spatial configuration of UBNs, this study provides meaningful insights for policy makers and planners seeking to optimize traffic infrastructure planning.

Spatial data infrastructures, which are characterized by multi-represented datasets, are prevalent through- out the world. The multi-represented datasets contain different representations for identical real-world entities. Therefore, update propagation is useful and required for maintaining multi-represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real-world entities and the detection of changes in updated datasets. Using polygon fea- tures of settlements as examples, this article addresses these key problems and proposes an approach for multi-represented feature matching based on spatial similarity and a back-propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in con- trast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one-to-one matching but also to one-to-many and many-to-many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to dem- onstrate the weight differences in different data contexts and to measure the performance of the BPNN- based feature matching approach.

Complex systems are very often organized under the form of networks, where nodes and edges are embedded in the geographic space in which human beings live. A geospatial network is a complex network that includes road networks, flight connections, physical internet infrastructures, and power grids, etc. The properties of geographic spaces and network topologies are key to the geospatial network function and evolution. It is crucial to characterize and understand the structure and evolution of these geospatial networks for different domains, ranging from social sciences to geographic sciences. Community structures, whose nodes often cluster into tightly knit groups (with a high density of within-group edges and a low density of between-group edges), have been given much attention because of their significance in analyzing complex geospatial networks. Community structures, which account for the functionality of a complex system in geographic space, represent a partition (disjoint community) or cover (overlapping community) of a geospatial network that is expected to have an optimal solution. Community structure found in geospatial network, such as those present in networks capturing the movements of people or animals, are subject to environmental factors, physical constraints, and other spatial influences. Therefore, communities in spatial networks often have geo-graphic meanings such as neighborhood boundaries, urban structures, and habitat territories. Moreover, the influence of geospatial indicators on the detection of community structure must not be overlooked.Compared with disjoint community structures, overlapping community structures are more ubiquitous community structures, which represent the fundamental characteristic of any complex network form in the physical world. In a geospatial network, community structures represent the different functions or structural units of a geographic space. Furthermore, overlapping communities are the result of the network evolution process, which represent the share channel of a neighbouring community. In geographical space, overlapping communities are always in the middle of two different communities. Spatially and functionally, overlapping communities are the bridge in communication between neighbouring communities. Therefore, the detection of overlapping communities in geospatial networks is of great significance. Although there are many methods for the detection of overlapping community structures, existing algorithms usually focus on network topology characteristics and ignore the effects of geospatial attributes on overlapping community structures.Hence, in this paper, we proposed a new methodology for uncovering communities in geospatial networks. The focus of our methodology is the spatially constrained speaker-listener label propagation algorithm (SLPA), which adds spatial resistance and spatial adjacency relations to the community detection algorithm. Furthermore, we make a case study using urban bus network in Hangzhou, China. The results show that the algorithm is efficient and reasonable to discovering overlapping community structures in geospatial networks. Overall, this study will help to understand and monitor the spatial interaction development of geographic phenomena and explore the functional characteristics of the urban complex system that are useful and meaningful for policy makers and planners.

Research on human location prediction is beneficial personalized location recommendations, location-based advertising, and ride-sharing services. The prediction of future locations is generally based on historical trajectory data. With the development and prevalence of mobile sensors, large volumes of data that can capture human trajectories are generated and provide researchers with more adequate and accurate data to study human trajectories and location predictions. In this paper, we propose a model containing long short-term memory (LSTM) to understand historical trajectories and predict future locations. LSTM is a special recurrent neural network (RNN) architecture that contains three gates and one internal state to control the storage of information. Additionally, we leverage the LINE (Large-scale Information Network Embedding), an embedding method to transform vertices into large scale networks, to transform locations into spatial vectors according to their distance information, which we name it “Location2Vec”. By feeding the summation of the dynamic location embeddings and pre-trained Location2Vec embeddings into LSTM model, it can effectively predict next locations of a trajectory and improve the predicting accuracy. Finally, we verify the reliability of the model, and the results show that our model outperforms other traditional trajectory models.

Trauma is considered a “modern civilized sickness”, and its occurrence substantially affects all of society, as well as individuals. The implementation of trauma emergency systems in cities with young, prosperous, and highly mobile populations is necessary and significant. A complete trauma emergency system includes both low-level trauma centers that offer basic emergency services and high-level trauma centers that offer comprehensive services. GIS and operational research methods were used to solve the location problem associated with these centers. This study analyzed the spatial distribution characteristics of trauma demands and the candidate locations of trauma centers based on a spatial analysis and presented a hierarchical location-allocation model for low- and high-level trauma centers in Shenzhen. The response, coverage, treatment and cost capacities of the trauma center locations were considered, and an ant colony optimization was used to calculate the optimal solution. The objectives of this study were to optimize trauma center locations, improve the allocation of medical trauma resources and reduce the rate of deaths and disabilities due to trauma.

A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with Noise) algorithm in the form of knowledge representation for constraint clustering. When used in the process of clustering geographic information, semantic reasoning based on a defined ontology and its relationships is primarily intended to overcome the lack of knowledge of the relevant geospatial data. Better constraints on the geographical knowledge yield more reasonable clustering results. This article uses an ontology to describe the four types of semantic constraints for geographical backgrounds: “No Constraints”, “Constraints”, “Cannot-Link Constraints”, and “Must-Link Constraints”. This paper also reports the implementation of a prototype clustering program. Based on the proposed approach, DBSCAN can be applied with both obstacle and non-obstacle constraints as a semi-supervised clustering algorithm and the clustering results are displayed on a digital map.

With the coming era of big data and the rapid development and widespread applications of Geographical Information Systems (GISs), geocoding technology is playing an increasingly important role in bridging the gap between non-spatial data resources and spatial data in various fields. However, Chinese geocoding faces great challenges because of the complexity of the address string format in Chinese, which contains no delimiters between Chinese words, and the poor address management resulting from the existence of multiple address authorities spread among different governmental agencies. This paper presents a geocoding service based on an optimized Chinese address matching method, including address modeling, address standardization and address matching. The address model focuses on the spatial semantics of each address element, and the address standardization process is based on an address tree model. A geocoding service application is implemented in practice using a large quantity of data from Shenzhen Municipality. More than 1,460,000 data records were used to test the geocoding service, and good matching rates were achieved with good adaptability and intelligence.

For the automatic generation of geographical information service chain, this article defines the quality of service (QoS) metrics based on service response time, reliability, and matching degree, among others, and the error propagation model. Based on the semantic matching and trustworthiness assessment of the geographical information services, this article proposes a trustworthy adaptive composition framework and implementation algorithm for geographical information services, ensuring the composition of service chains to better meet various QoS constraints. The effectiveness of this approach is proven in the simulation experiments.

Based on research on information organization and visualization mechanism of multimedia electronic map, this paper proposes an organization method about spatial information and multimedia information of multimedia electronic map: seamless spatial data were organized and managed by stratified expansion R-tree and Quad-tree hierarchy; and multimedia information data were organized and managed by hypermedia model based on an electronic map. Considering electronic map features, the paper also proposes a method for spatial date organization, which integrates Digital Landscape Model and Digital Cartographic Model, and a hybrid model between vector and raster spatial data. Then, the paper studies information visualization mechanism of multimedia electronic map. Finally, the paper certifies the results via a case of multimedia electronic map authoring tools software-Atlas2005.

The fusion of VISI (visual identity system Internet), digital maps and Web GIS is presented. Web GIS interface interactive design with VISI needs to consider more new factors. VISI can provide the design principle, elements and contents for the Web GIS. The design of the Wuhan Bus Search System is fulfilled to confirm the validity and practicability of the fusion.

Research on spatial cluster detection of traffic crash (TC) at the city level plays an essential role in safety improvement and urban development. This study aimed to detect spatial cluster pattern and identify riskier road segments (RRSs) of TC constrained by network with a two-step integrated method, called NKDE-GLINCS combining density estimation and spatial autocorrelation. The first step is novel and involves in spreading TC count to a density surface using Network-constrained Kernel Density Estimation (NKDE). The second step is the process of calculating local indicators of spatial association (LISA) using Network-constrained Getis-Ord Gi* (GLINCS). GLINCS takes the smoothed TC density as input value to identify locations of road segments with high risk. This method was tested using the TC data in 2007 in Wuhan, China. The results demonstrated that the method was valid to delineate TC cluster and identify risk road segments. Besides, it was more effective compared with traditional GLINCS using TC counting as input. Moreover, the top 20 road segments with high-high TC density at the significance level of 0.1 were listed. These results can promote a better identification of RRS, which is valuable in the pursuit of improving transit safety and sustainability in urban road network. Further research should address spatial-temporal analysis and TC factors exploration.

This chapter presents a feature matching approach based on a particle swarm optimization neural network (PSONN) in data integration to identify the corresponding features in different datasets. Unlike previous probability-based feature matching using a weighted average of multiple measures calculating matching probability, the proposed approach utilizes PSONN, obtaining similarity rules of feature matching to find matched features in different datasets. The feature matching strategy utilizing bidirectional matching, two-stage matching, and feature combination is also provided for solving all types of feature matching, including 1:0, 0:1, 1:1, 1:n, m:n, and m:1. The proposed approach is implemented for matching features from different datasets and is compared with a probability-based feature matching method. The experiments show that the weights of the same measures may vary for different data contexts. In addition, the results demonstrate the availability and advantages of the proposed approach in feature matching.

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

In medicine, artificial neural networks (ANN) have been extensively applied in many fields to model the nonlinear relationship of multivariate data. Due to the difficulty of selecting input variables, attribute reduction techniques were widely used to reduce data to get a smaller set of attributes. However, to compute reductions from heterogeneous data, a discretizing algorithm was often introduced in dimensionality reduction methods, which may cause information loss. In this study, we developed an integrated method for estimating the medical care costs, obtained from 798 cases, associated with myocardial infarction disease. The subset of attributes was selected as the input variables of ANN by using an entropy-based information measure, fuzzy information entropy, which can deal with both categorical attributes and numerical attributes without discretization. Then, we applied a correction for the Akaike information criterion (ΑICc) to compare the networks. The results revealed that fuzzy information entropy was capable of selecting input variables from heterogeneous data for ANN, and the proposed procedure of this study provided a reasonable estimation of medical care costs, which can be adopted in other fields of medical science.

In recent years the propagating update of multi-represented datasets has become a crucial issue for maintaining geographic data, especially since National Spatial Data Infrastructure (NSDI) appeared. The key to propagating update is building the mappings between the datasets. Usually the traditional approaches of building the mappings don’t consider the attributive similarity and often use object IDs to build the mappings which may differ with the variation of data storage. Accordingly, a comprehensive similarity computing method is proposed and unified geographic entity code (UGEC) is put forwards to build the mappings in this chapter. A workflow of propagating update, which mainly consists of data preprocessing, changes detecting, changes extracting, master dataset updating, and target dataset updating, is presented on the basis of objects mappings. An experiment on implementation of this method demonstrates its viability at the end.

There is a tendency toward the creation of thematic maps on the web in the ongoing development of cartographic technology. However, automatically creating maps through the web and creating interactive web maps are two challenges associated with this field. To solve these problems, a web thematic cartography method based on a web service chain is presented here. Automated cartography is performed through knowledge-based web cartographic services, and interacting with the services is possible. The method is implemented in the construction of a urban thematic atlas in Shenzhen, China that is dedicated to supporting decision making for urban planning and public participatory thematic map making on the web. Há uma tendência em direção à criação dos mapas temáticos na Web. Entretanto, a geração automática de mapas e a geração de web maps interativos são dois desafios associados com este campo de pesquisa. Para resolver estes problemas, apresenta-se mapas tematicos na web criados por uma cadeia de serviços. A cartografia automatizada é realizada por meio de um serviços cartoraficos baseados em conhecimento, que interagem com os serviços quando possivel. O método foi implementado na construção de um atlas urbano de Shenzhen - China, dedicado a dar suporte à tomada de decisão para o planejamento urbano participação popular na geração de mapas temáticos na Web.

This paper discussed the expression of vector data under the Web environment, introduced a spatial data sensitive crawler (SDSC), and tested the novel technique to collect vector data in real cases. Furthermore, a defect reduction model has been improved so that the level of confidence of different vector data can be measured more accurately. The results of experiments show that the data collected by SDSC have a relatively higher credibility. Keywords—Spatial Data Sensitive Crawler; Vector Data; Defect Reduction Model; Measure of Confidence Level

In recent years, with the reforms to the land use system and the development of urbanization in China, land price evaluation has tended towards marketization. Prices are determined by the government, the land transaction market and the public. It is necessary to propose higher standards to be used in the evaluation process. This paper presents an online land price evaluation approach for convenience in evaluation. In a network environment, taking advantage of the data services provided by various departments, we propose two models to assist in decision-making: (1) a geographic information system (GIS)- and fuzzy set-based location factor quantification model, which adopts dynamic data, rules and quantification measures (based on the road network) to dynamically quantify location factors, thus transforming fuzzy sets into appropriate values; and (2) a neartude-based transaction sample push model, which quantifies the similarity between a given land and other samples, thus providing a basis for decision-making by an appraiser. This approach is applied in Shenzhen to evaluate its ability to simplify the work of appraisers and make their decisions more intuitive and objective in a real case.

The construction for thematic map symbol is a very complex and intelligent process. This symbol can be automatically generated and easily shared on the web through the syntactic characteristics of statistical indicators and cartography visualization. This paper expounds the symbol types, inner structure and its design pattern. And a syntactic construction theory based on phoneme (thematic map primitive) — word (single thematic symbol) — sentence (combined symbols or complex symbols) structure model is put forward for automatic construction of thematic map symbol. As a result of this research, symbols can be defined using cartographic primitives which are arranged according to its syntactic principles. Its concept and schema is discussed, and some examples are presented.

Context-Sensitive, also known as context-aware, is a new computing model which taking people as a dominant factor, it requires the computing devices can detect the user's context information to adjust the system's behavior. Web Map Service (WMS) is a GIS (Geography information system) specification which is established by the OGC in accordance with the development of Web Service technology. In order to make the map services more user-friendly and get better user experience, our works are people-oriented and dynamically provide corresponding map services on the basis of the user's context information. In this paper we address an architecture of web map service which concentrates on context-sensitive. It can convert varies user's context information to different kinds of parameters, about the visualization of geographic data, to get variable map images in response to the users' context., and finally realizes providing context-sensitive Web Map Service.

Land registration is public act which is to record the state-owned land use right, collective land ownership, collective land use right and land mortgage, servitude, as well as other land rights required the registration according to laws and regulations onto land registering books. Land registration is one of the important government affairs , so it is very important to standardize, optimize and humanize the process of land registration. The management works of organization are realized through a variety of workflows. Process knowledge is in essence a kind of methodology knowledge and a system which including the core and the relational knowledge. In this paper, the ontology is introduced into the field of land registration and management, trying to optimize the flow of land registration, to promote the automation-building and intelligent Service of land registration affairs, to provide humanized and intelligent service for multi-types of users . This paper tries to build land registration procedure ontology by defining the land registration procedure ontology's key concepts which represent the kinds of processes of land registration and mapping the kinds of processes to OWL-S. The land registration procedure ontology shall be the start and the basis of the Web service.

GIS middleware is a mediator between application (or logical level) and basic services (or physical level), which is a key part of GeoInformation web services, can adapt to the context changes including spatial-temporal data, devices, situation of user, and services. There are many researches achievements of context-sensitive pervasive computing middleware existed. But in the GIS Field, it is very little about research on architecture of context-sensitive spatial information service middleware. This paper focuses on the system architecture of GIS middleware to support the building of context-sensitive applications. Four main components of GIS middleware: GIS Core Service, Context Manager, Data Adapter, and GIS Service Provider, are introduced in detail.GIS middleware is a mediator between application (or logical level) and basic services (or physical level), which is a key part of GeoInformation web services, can adapt to the context changes including spatial-temporal data, devices, situation of user, and services. There are many researches achievements of context-sensitive pervasive computing middleware existed. But in the GIS Field, it is very little about research on architecture of context-sensitive spatial information service middleware. This paper focuses on the system architecture of GIS middleware to support the building of context-sensitive applications. Four main components of GIS middleware: GIS Core Service, Context Manager, Data Adapter, and GIS Service Provider, are introduced in detail.

Spatial information services are complicated and various nowadays, so how to push appropriate services according to users' real-time spatial context intelligently is a problem needed to be solved urgently. This paper summarizes the context of geospatial information, and puts forward the features of context-sensitive geospatial information services as well as formal model. A mechanism of pushing services intelligently was proposed aiming at users' features and personalized using demands, by studying the key operations of the context sensitive geospatial information services, and the storage, management as well as user pattern of the geospatial information service.

With the rapid development of the gazetteers, more and more geographical names databases has been established. Since the geographical names exit in form of records which provide little qualitative description other than quantitative information, geographical names are hardly shared and interoperable. In order to solve this problem, we urgently need to set up knowledge base for geographical names that shall provide qualitative knowledge to describe the essence of the elements. So, we use ontology and production rules to build geographical name knowledge base, where the geographical names ontology is regarded as the foundation for reuse and sharing of the geographical names information, and production rules are used to enhance the expressivity of the ontology. First of all, we analyzed the geographical names concepts and their semantics, the concepts of space and time and their relationships in geographical names to describe the knowledge structure for this field, used Web Ontology Language (OWL) to provide formal descriptions to give them explicit semantics, and proposed a unified semantic framework for description. Secondly, we established the common-sense rules and spatial relations inference rules coded with Semantic Web Rule Language (SWRL) which laid the foundation for geographical names knowledge discovery and automatic reasoning. Finally, we established a geographical name knowledge base combining both the geographical names ontology and rules established above. Through the analysis of examples we showed that based on the geographical names knowledge base the geographical names information can be well shared and reused.

Context-awareness is increasingly becoming an important capability in devices for ubiquitous computing environments. These devices use on-board sensors and history of user interaction to collect data that are used to adapt their behavior to suit with the current environment. There is a need to support real-time software in ubiquitous computing environments, especially in reactive systems, such as distributed and mobile sensors, location-based information service, etc. In these cases, both behavior and interaction among devices depend on constantly changing environmental conditions, in addition to explicit user control. This characteristic requires specific system services to support the development and the runtime operation of real-time context-aware software. This implies that the underlying services must themselves be context-sensitive. The paper analysis current Web-3D visualization technique, based on context-sensitive middleware, and uses B/S internet mode and HTTP transmitting protocol to realize development of Web-3D visualization.

Geographical Names are key components of all georeferenced information systems, including GIS applications in many diverse fields of knowledge and services. This paper we focus on building an ontology for gazetteers, the ontology will better support the gazetteers applications, maintenances, and usability. The ontology contains place types, spatial and temporal concepts and their relations. We firstly establish a prototype of geographical name ontology. Secondly, we analyzed geographical names concepts and their semantics, to describe the knowledge structure for this field. Then we use OWL to provide a formal description for these concepts and relations and propose a unified semantic framework for the description of the geographical names information. Finally, we make an experiment by establishing ontology-based knowledge base. Through the analysis of examples we showed that ontology-based geographical names information can be shared and reused conveniently, and that the information retrieval by assistant of ontology can be easily realized.

The cadastral plays a very important role in managing spatial and non-spatial legal real property information. And the legal aspect is the important component of the cadastral. And the success of a cadastral system is not dependent on its legal or technical sophistication, but whether it protects land rights adequately and permits those rights to be traded (where appropriate) efficiently, simply, quickly, securely and at low cost. However, the ambiguity of legal cadastral domain has been the major barrier to data integration and interoperability. This paper intends to optimize the concept model of legal cadastral domain based on the model established in my previous paper which can be a first step towards facilitate the effective interchange of cadastral information and the administration of land use. And the way expressing these conceptions and relationships between them was an object-oriented approach in ontology principles. The outcome of this paper is also a basic but better expression legal cadastral domain model of china.

This paper will combine ontology and rule based qualitative reason with real time calculation, designing a combined traffic model of national scope which contains highway, railroad, water carriage, scheduled flight etc. That method follows the sense of people to space, establishes ontologies and rules knowledge base, using concepts, instances, relations and rules of traffic field as the basic knowledge for qualitative reason to discover implicit semantic information and eliminate unnecessary ambiguities. The knowledge from the ontologies and rules provides abundant information for query which can lighten the burden of computation, in the mean time, real-time calculation guarantees the accuracy of the data, has raised accuracy and efficiency of the query, which has strengthened the ease of query service and improved web users' experience.

In this paper, based on spatial cognitive theories connected with the construction of natural language generation model for spatial information representation, we research on related spatial orientation results from the study field of modern Chinese, analyzed semantic mapping between spatial information represented by kinds of maps and spatial orientation information expressed by natural language, and bring spatial concept into the background of linguistics and GIS from a unique perspective. We determine the spatial information that should be represented in this kind of natural language, as well as set up intermediate semantic model which can be described by spatial point of point. This study is a contribution to summarize the macroscopic research achievements including spatial conception related to natural language, and generalize the microcosmic methods with respect to object-based spatial model and natural language generation. Implementation of a semantic framework mapped between spatial information and natural language is important from a methodological point of view because it guides us to build spatial knowledge base which formalize the semantics of natural language and specify spatial relation in a computational model which allows for similarity comparisons. This paper describes an experiment that investigates text generation result for bus route-finding.

The main goal of this article is to present a generalized conceptual model for the Chinese cadastre. For the Chinese cadastre, both the geometric and legal components will be considered. According to the cadastral situation of china and existent data structure, this paper modified the core cadastral domain model by an objected-oriented way. After that the refined Land package and geometry package are presented. The other contribution of this article is identification of the core cadastral model by using it to Chinese cadastral domain, although some specific classed are modified.

Spatial cognition is a cognitive process of spatial environment. As an important tool of recording and presenting geographic information, map is the main medium for spatial cognition result. This paper evaluates the existing measurement approaches for map information, and the measurement of metric information proposed by Li and Huang is especially emphasized, for it considers the spatial distribution and relationships of map features. Because the map is a complex cognitive system, human cognition should be considered here. However these measures have neglected the cognitive concepts 'type' and 'level' of map features, which are valuable for human's spatial cognition. As a result, the authors employed the weighted Voronoi diagram to propose a new quantitative measure for metric information. An experimental evaluation is also conducted. Results show that metric information considering cognitive multi-type and multi-level is closer to the spatial environment and human's spatial cognition.

The achievements of computer graphics have supported the developments of augmented reality (AR), a technique of an automatic connection of additional information generated from a computer model to reality, which is offered to enhance or augment the cognition to the real world of a user. Especially due to the developments in hardware in the recent years not only visualization but also interaction with these environments has become possible. Outdoor AR system integrates the presentation of three-dimensional objects out of a database on site in a Geographical information system (GIS). It not only expands a GIS to the third dimension, also reacts to the current position and viewing field of the user. From the perspective of the theory of spatial cognition and visualization, the paper puts forward there search contents and basic structure of spatial data AR visualization by analyzing the research status and related research works, comparing there exists frameworks. Furthermore it sums up the key technologies in detail and proposes the improved approaches. By a case study, it verifies the procedure of AR visualization of geospatial data. Lastly, it discusses the significant applications and development prospects of AR visualization of geospatial data.