Geographic Information Database Research Articles

GEOINFORMATION DATABASE OF OUTDATED HOUSING STOCK AS THE BASIS FOR THE IMPLEMENTATION OF URBAN PROGRAMS FOR COMPLEX RECONSTRUCTION

Significant volumes of outdated housing stock in Ukrainian cities require careful accounting, analysis, and substantiation of the order of immediate reconstruction measures. A specialized geographic information database should become the basis for the development and successful implementation of programs and projects for complex reconstruction.
 This publication presents the results of the formation of the geoinformation base of the outdated housing stock of Kyiv as the basis for the implementation of the urban program for complex reconstruction. The database is presented in the form of passports of the territories of outdated buildings in the context of the administrative districts of Kyiv. Attribute information includes data both for each individual house and for blocks and neighbourhoods of outdated housing stock as objects of complex reconstruction. The cartographic basis is the vector topographic-geodetic mapping of Kyiv on a scale of 1:2000.
 The structure of such a system can serve as the basis for a database of outdated buildings in any settlement.

Vector data partition correction method supported by deep learning

ABSTRACT Road vector data are an important part of geographic information databases and play a leading role in social and economic development. Among them, the accuracy and current situation of vector data are the key values of their application. In recent years, with the development of remote sensing technology, remote sensing images contain increasingly more road information, which provides a large number of available features for the establishment of vector correction models. Therefore, the vector-to-image vector correction method based on remote sensing images has become an important means to ensure the accuracy and current situation of vector data. However, in actual scenes, there are great differences in the design structure and design index between urban roads and rural roads, which is reflected in the serious spatial heterogeneity between them in the remote sensing images. Therefore, when the existing methods use remote sensing images for vector correction, the universality is limited by the change of regional scenes, so it is difficult to realize the simultaneous correction of urban road and rural road vectors, with poor applicability and low compatibility. To solve this problem, this paper proposes a vector data partition correction method supported by deep learning. First, the U2-net model and line segment sequence method are used to generate the feature set. Second, according to the characteristics of the poor quality of urban road extraction results, the regular geometric form of vector data and the poor structural information of road images, the methods of vector line decomposition, subvector line correction, subvector line centralization and vector line synthesis are proposed to correct the vector lines of urban roads. Finally, according to the characteristics of high-quality rural road extraction, the irregular geometric form of vector data and the strong information of road image structures, this paper proposes road edge extraction, a road centreline reasoning model and a vector line connection method to complete the correction of rural vector road data. The quantitative analysis of the experimental results shows that compared with other methods, the urban road vector correction method in this paper is much better than the comparison methods and has achieved better correction results in rural areas. This method has the advantages of compatibility and better accuracy for roads in different areas.

ASFF-YOLOv5: Multielement Detection Method for Road Traffic in UAV Images Based on Multiscale Feature Fusion

Road traffic elements are important components of roads and the main elements of structuring basic traffic geographic information databases. However, the following problems still exist in the detection and recognition of road traffic elements: dense elements, poor detection effect of multi-scale objects, and small objects being easily affected by occlusion factors. Therefore, an adaptive spatial feature fusion (ASFF) YOLOv5 network (ASFF-YOLOv5) was proposed for the automatic recognition and detection of multiple multiscale road traffic elements. First, the K-means++ algorithm was used to make clustering statistics on the range of multiscale road traffic elements, and the size of the candidate box suitable for the dataset was obtained. Then, a spatial pyramid pooling fast (SPPF) structure was used to improve the classification accuracy and speed while achieving richer feature information extraction. An ASFF strategy based on a receptive field block (RFB) was proposed to improve the feature scale invariance and enhance the detection effect of small objects. Finally, the experimental effect was evaluated by calculating the mean average precision (mAP). Experimental results showed that the mAP value of the proposed method was 93.1%, which is 19.2% higher than that of the original YOLOv5 model.

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

The purpose of this work is to study the integration of sets of core reference and thematic geospatial data based on the JOIN operation of relational algebra and its interaction with geocoding of geospatial features, which is implemented in modern geographic information systems (GIS) and database management systems (hereinafter – DBMS) for the development of the national spatial data infrastructure (hereinafter – NSDI). Method. The research is based on the analysis of the possibilities of applying the theory of geospatial databases and knowledge bases, international and national harmonized standards in the field of Geographic Information/ Geomatics to solve the problem of integration of geospatial data using the operation JOIN relational algebra in object-relational database management systems (OR DBMS). Results. The paper examines the models of the Join operation of relational algebra, which underlie the geocoding of features and the creation of electronic gazetteers, and proves its effectiveness: the Join operation integrates of core reference and thematic geospatial datasets. There is a need to define the required geographic identifiers, which must be present among the attributes of the core reference and thematic geospatial datasets to perform the join. The variety of uses of the Join operation covers all possible cases that arise in their practical application. Thus, the use of the Join operation involves identifying these required geographic identifiers at the geospatial database design stage. In particular, it is expedient to determine mandatory geographical identifiers (codes) of features according to the official national systems of features classification (codification) in the relevant sectoral thematic registers, which are responsible for certain holders of thematic data in accordance with Annex 2 of the Decree of Cabinet of Ministers “The order for the functioning of the national spatial data infrastructure” of May 26, 2021, № 532. Scientific novelty and practical significance. The integration of core reference data and thematic geospatial datasets based on JOIN operation models of relational algebra and their interaction with geocoding of geospatial features is researched, which is implemented in modern GIS and DBMS for the development of national spatial data infrastructure. The research was performed on a set of core reference spatial data, namely: information on administrative-territorial units of the Cherkasy region, including their borders; the data from the statistical bulletin of the socio-economic situation of the Cherkasy region for January 2021 of the Main Department of Statistics in Cherkasy region of the State Statistics Service of Ukraine were selected as thematic data. It has been shown that relational algebra join (JOIN) operations can be used to integrate other thematic geospatial data with core reference data using geographic identifiers that contain these datasets.

DYNAMIC UPDATING METHOD OF GEOSPATIAL DATABASE WITH INCREMENTAL DATA

Abstract. With the rapid development of computer technology and Geographic Information System (GIS), geospatial data are commonly stored and managed in the geospatial database. And geospatial data are widely used in government decision-making, environmental protection, scientific research, and national defence construction. The frequency of geospatial database updating is getting higher and higher due to economic and social development. In this paper, dynamic updating method of geospatial database with the incremental data is proposed, which means only changed features are updated into the geospatial database. Features that have not changed are not updated. This method can greatly improve the efficiency of database update. It has been applied into dynamic updating of national fundamental geographic information database of China. This database has been updated and published once a year. The current situation of data is improved continuously. It can provide more reliable data guarantee for national economic construction and social development. In the future, there will be further optimization of the dynamic updating method. We also hope more and more geospatial database can be updated by this method.

RESEARCH ON QUALITY TESTING AND EVALUATION TECHNOLOGY OF FUNDAMENTAL GEOGRAPHIC INFORMATION DATABASE SYSTEM

Abstract. The extensive application of fundamental geographic information data in various industries makes it play an increasingly important role in national economic construction and social development. As a fundamental geographic information database construction project with data and database management system as its core, quality testing and evaluation is the key link and means for measuring database suitability and system application success or failure. This paper proposes a set of fundamental geographic information database system quality model, conducts research on quality testing techniques and methods, and establishes a quality evaluation system, which has been applied in national and provincial basic geographic information database systems.

Comparison of analytic network process and artificial neural network models for flash flood susceptibility assessment

Floods are natural risks with devastating consequences for the environment, people and the economy. Gabes Catchment is regularly devastated by floods owing to urban sprawl, population growth, unregulated municipal systems and indiscriminate land use. However, mitigation of flood impacts can be achieved via flood implementation forecasting systems. In this study, Analytical Network Process (ANP) and Artificial Neural Network (ANN) models were integrated into a Geographic Information System (GIS) to identify and classify flood-prone areas. A geographic information database was derived from the existing geological map, digital elevation model (DEM), precipitation and land-use data. The evaluation of different factors can affect the flood analysis. The ranked and normalized indicators were then weighted and classified with an ANP model to establish the training database. The normalized layers, combined with the training site maps, were then fed to a multilayer perceptron neural network (MLP) to yield a flood risk map. Using a field survey, historical flood data, and satellite imagery, 226 flood locations were identified and classified into 70% training data sets and 30% validation data. Results obtained from ANP and ANN showed that 10% and 14% of all areas were classified as high and very high flood susceptibility, respectively. The performance of both models was assessed using the operational characteristic of the ROC model. The Area Under the Curve ‘AUC’ of ANP and ANN models were 0.861 and 0.876, respectively. The obtained results show the similarity and comparability of the used methods. These results corroborate the perception of susceptibility in the population of the city of Gabes. The study outcomes are of great value to policy makers and state authorities in order to achieve greater awareness and adopt strategies for the preparation and management of the environment in the future for the city of Gabes.

UAV Imagery for Automatic Multi-Element Recognition and Detection of Road Traffic Elements

Road traffic elements comprise an important part of roads and represent the main content involved in the construction of a basic traffic geographic information database, which is particularly important for the development of basic traffic geographic information. However, the following problems still exist for the extraction of traffic elements: insufficient data, complex scenarios, small targets, and incomplete element information. Therefore, a set of road traffic multielement remote sensing image datasets obtained by unmanned aerial vehicles (UAVs) is produced, and an improved YOLOv4 network algorithm combined with an attention mechanism is proposed to automatically recognize and detect multiple elements of road traffic in UAV imagery. First, the scale range of different objects in the datasets is counted, and then the size of the candidate box is obtained by the k-means clustering method. Second, mosaic data augmentation technology is used to increase the number of trained road traffic multielement datasets. Then, by integrating the efficient channel attention (ECA) mechanism into the two effective feature layers extracted from the YOLOv4 backbone network and the upsampling results, the network focuses on the feature information and then trains the datasets. At the same time, the complete intersection over union (CIoU) loss function is used to consider the geometric relationship between the object and the test object, to solve the overlapping problem of the juxtaposed dense test element anchor boxes, and to reduce the rate of missed detection. Finally, the mean average precision (mAP) is calculated to evaluate the experimental effect. The experimental results show that the mAP value of the proposed method is 90.45%, which is 15.80% better than the average accuracy of the original YOLOv4 network. The average detection accuracy of zebra crossings, bus stations, and roadside parking spaces is improved by 12.52%, 22.82%, and 12.09%, respectively. The comparison experiments and ablation experiments proved that the proposed method can realize the automatic recognition and detection of multiple elements of road traffic, and provide a new solution for constructing a basic traffic geographic information database.

5G Virtual Reality in the Design and Dissemination of Contemporary Urban Image System under the Background of Big Data

As an important part of the soft power of urban culture, the importance of city image has received widespread attention from academic circles and society. The study of city image communication has become one of the important research directions in the field of communication in recent years. This research mainly discusses the application of 5G virtual reality in the design and dissemination of a contemporary urban image system under the background of big data. This research designs and develops a city image system that models and simulates virtual cities in a three-dimensional scene. The system consists of several different modules, including a data acquisition module, scene construction, and roaming module. The most important data resource formats used by this system are the DXF format and 3DS format, which can be created and acquired using digital stereo images in the data acquisition module. The data import and export module can convert the models in DXF format and 3DS format into Openfligt format models. The model editing and reconstruction module can visually edit and reconstruct the converted model, so that the space and attribute information of the recreated model is stored in the data storage and management system. Finally, a scene roaming module is created, and models are selected from the database to create a 3D city scene and provide interactive roaming information extraction. After the implementation of virtual reality technology, the per capita awareness of urban humanities reached 63.2%. The test results show that because the system integrates the respective advantages of the geographic information system, virtual reality, and database technology, it can better realize the modeling and simulation of digital cities under the conditions of three-dimensional visualization, which is conducive to the spread of urban image.

Design Path and Application Analysis of Integrated Geological Data Management Platform

Data is the main carrier of geological work to serve the society, and it is also an important information resource to serve the development of geological prospecting and other industries. There are many problems in the management of geological data in most geological data collection institutions. All kinds of reasons lead to the lack of effective management and update of geological data, and even the lack of sharing methods. Based on GIS location service, the provincial basic geographic information data, physical preservation and storage data and the file information of collected physical geological data are integrated, and the provincial information management platform of physical geological data is designed and implemented, which can effectively improve the management, development and utilization rate of physical geological data. From the practice of geological data management in China for more than 50 years, as geological data production units and collection organizations have followed the idea of geological data file management, a geological data-based working mode has been formed, which emphasizes collection and neglects utilization. All kinds of geological data are integrated to realize unified geological data management and model application. By analyzing them, the design scheme of management system is put forward, the functions of each module are introduced in detail, and the corresponding implementation methods are pointed out, which provides an example for the construction and typical application of this kind of management system.

Modeling the Ecological-Socio-Economic System of the White Sea and its Watershed

Purpose. The work is aimed at developing a cognitive model of the ecological-socio-economic system of the White Sea (Beloe more) and its watershed (called for short Belomor’e). Unlike the previously developed cognitive models for the region, the new model of the system has a hierarchical structure including five sub-models united by a common management system. The model is intended for obtaining prognostic qualitative assessments of the transformations ongoing in a complex system under various scenarios of nature management and climate change. The model makes it possible to determine different targets, which, in their turn, permit to assess the possibilities of improving the population living standards, the environment rational use and protection, and development of the White Sea region social sphere. These factors constitute an important foundation for achieving sustainable development of the region. The results can serve a basis for constructing a system of quantitative models required to develop the management decision support systems. Methods and Results. The cognitive model of the White Sea is considered to be a tool for synthesizing heterogeneous information about a complex ecological-socio-economic system. The conceptual modeling and the mathematical apparatus of continuous or probabilistic logic are applied. Unlike the traditional cognitive models, the new one implies the variables’ change in time over 100 years. This allows us to describe the relationship between the interaction agents, and to characterize the mechanisms of their mutual adaptation. The time step in the model is preset to be one year. Development of the cognitive models for the White Sea region was supported by the following information: geographic information systems, databases, integrated electronic and paper atlases of the White Sea and its watershed area, original 3D mathematical models of the sea thermohydrodynamics and ecosystem. The patterns of climate change, hydrological conditions and fishing (basic occupation of local population – the Pomors) were studied. At that, the models both for assessing the regional economy state and for forecasting its development are used. Conclusions. A new cognitive model of the White Sea region ecological-socio-economic system was created based on the hierarchical principle. The developed sub-models relate to various fields of knowledge: economy, demography, oceanography, soil and agrophysics. Dynamics of the model elements over 100 years was demonstrated. Besides, it was shown that with the quasi-cyclic climate fluctuations, the economic parameters change insignificantly, whereas they have a noticeable impact upon the population living standards and the White Sea ecosystem. The demonstrated features resulted from the climate change effects upon the White Sea ecosystem are manifested in the fluctuations of water temperature, phyto- and zooplankton biomass and fishing, but the changes in benthos are hardly noticeable. Dependence of the White Sea region population outflow upon the gross regional product size, availability of production facilities and water quality is shown. Water quality in the region increases, unfortunately, not due to the investments in water treatment, but because of the pollution decrease resulted from the population and production shrinkage.

Research about pruning hyper-parameter optimization method based on transfer learning in geographic information system

Recently, researchers have found that some automated optimization methods and techniques can speed up the whole search process and can obtain better model hyper-parameter configurations. In this paper, the optimized initial hyper-parameter configuration is obtained through the loss function defined under the definition of meta-features combined with the algorithm of transfer learning. Furthermore, the sequence model-based hyper-parameter optimization model (SMBO model) is transformed by introducing a pruning strategy. By categorizing the hyper-parameters according to their importance, the non-important hyper-parameters are eliminated but important hyper-parameters are strengthened, so the final hyper-parameter optimization configuration is obtained. It is the parameter optimization design based on this secondary optimization that makes it possible to quickly search and select relevant information and carry out data application. The main goal of this paper is to clarify the mathematical principle of secondary optimization and combine it with geographic information system to create the value development of geographic information database.

Road Centreline Extraction of High-Resolution Remote Sensing Image with Improved Beamlet Transform and K-Means Clustering

The extraction of road centreline from a remote sensing image is an important part of obtaining and updating basic geographic information data. In view of the shortcomings of traditional road centreline extraction algorithm, such as the presence of burrs, lengthy time consumption and ineffective extraction of the centreline around intersections, we propose an algorithm based on an improved beamlet transform combined with K-means clustering to extract the road centreline of high-resolution remote sensing images. The proposed method includes the following steps. Firstly, K-means clustering is used to identify the road area. Secondly, the improved beamlet transform algorithm is utilised to extract the road centreline. Reducing the beamlet base and simplifying the beamlet dictionary reduces the algorithm complexity. By adding energy threshold and direction constraint, each binary block is guaranteed to contain only one optimal base that avoids the overlapping and missing of the beamlet base. Lastly, a connection algorithm is designed to connect the breaks of some road centrelines. Experimental results show that the proposed algorithm can accurately extract the road centreline, effectively form high-resolution remote sensing images and had good anti-noise ability. Compared with traditional beamlet transform, the proposed beamlet transform achieves better results.

Modelirovaniye ekologo-sotsio-ekonomicheskoy sistemy Belogo morya i yego vodosbora

Purpose. The work is aimed at developing a cognitive model of the ecological-socio-economic system of the White Sea and its watershed (called for short Belomorje). Unlike the previously developed cognitive models for the region, the new model of the system has a hierarchical structure including five sub-models united by a common management system. The model is intended for obtaining prognostic qualitative assessments of the transformations ongoing in a complex system under various scenarios of nature management and climate change. The model makes it possible to determine different targets, which, in their turn, permit to assess the possibilities of improving the population living standards, the environment rational use and protection, and development of the White Sea region social sphere. These factors constitute an important foundation for achieving sustainable development of the region. The results can serve a basis for constructing a system of quantitative models required to develop the management decision support systems. Methods and Results. The cognitive model of the White Sea is considered to be a tool for synthesizing heterogeneous information about a complex ecological-socio-economic system. The conceptual modeling and the mathematical apparatus of continuous or fuzzy logic are applied. Unlike the traditional cognitive models, the new one implies the variables’ change in time over 100 years. This allows us to describe the relationship between the interaction agents, and to characterize the mechanisms of their mutual adaptation. The time step in the model is preset to be one year. Development of the cognitive models for the White Sea region was supported by the following information: geographic information systems, databases, integrated electronic and paper atlases of the White Sea and its watershed area, original 3D mathematical models of the sea thermohydrodynamics and ecosystem. The patterns of climate change, hydrological conditions and fishing (basic occupation of local population – the Pomors) were studied. At that, the models both for assessing the regional economy state and for forecasting its development are used. Conclusions. A new cognitive model of the White Sea region ecological-socio-economic system was created based on the hierarchical principle. The developed sub-models relate to various fields of knowledge: economics, demography, oceanography, soil and agrophysics. Dynamics of the model elements over 100 years was demonstrated. Besides, it was shown that with the quasi-cyclic climate fluctuations, the economic parameters change insignificantly, whereas they have a noticeable impact upon the population living standards and the White Sea ecosystem. The demonstrated features resulted from the climate change effects upon the White Sea ecosystem are manifested in the fluctuations of water temperature, phyto- and zooplankton biomass and fishing, but the changes in benthos are hardly noticeable. Dependence of the White Sea region population outflow upon the gross regional product size, availability of production facilities and water quality is shown. Water quality in the region increases, unfortunately, not due to the investments in water treatment, but because of the pollution decrease resulted from the population and production shrinkage.

Full Convolutional Network Algorithm Improves the Accuracy of the Boundary of Orthographic Composite Images

In the past several “five-year plans”, China has basically established the national, provincial and regional streets of basic geographic information database, and on this basis has carried on a certain degree of improvement. Based on deep learning, this paper aims at improving the boundary accuracy of the orthographic composite image based on the convolutional neural network algorithm. It mainly compares the effects of different parameters on the performance of the network model, and describes the training and testing performance of the model through the corresponding performance evaluation method. In the research and test, the detection accuracy statistics of internal industry encryption points and field control points with different scale accuracy were adopted. The results showed that the error in the DOM detection of new aerial images and satellite images was less than 3m and 9m, which met the technical requirements and improved the boundary accuracy compared with the plane accuracy of existing digital orthophoto images. Thus, it can be seen that the new production technical scheme of aerial image/satellite shadow DOM generated by enhancing the precision of the orthographic composite image boundary based on the full convolutional network algorithm under deep learning is efficient and feasible. The study not only improves the relevant theories, but also has some practical inspirations for future research.

Retracted] AHP‐Based Analysis Factors Influencing the Construction of a Smart City with Three‐Dimensional Regional Color

Urban color is becoming more and more important in the construction of smart cities. This research mainly discusses the influencing factors of AHP‐based 3D regional color smart city construction. From the perspective of city managers, the factors selected in the article that affect the choice of urban color planning are economic development, urban culture, designer’s ideas, customs, and natural environment. We use GIS to comprehensively collect basic geographic information data of the city. We use CityEngine and GIS data to quickly create virtual cities. When constructing the AHP model, once the weight value of each factor in the color‐influencing factors of the entire smart city is obtained, the total ranking of the levels needs to be checked once. After that, the study uses the MATLAB program platform to realize the parametric design of the main color of the city, including the control of the changes in the spatial structure of the main color of the city, followed by the control of the transition and coordination mode between the key elements of the city color. Explorer provides comprehensive and rich 3D digital city roaming browsing, query positioning, analysis, and other functions; supports the loading of multiple data sets from the Internet and local; and can dynamically load massive 3D city simulation models above and below ground. Finally, the design of the urban architectural color or improvement plan is further carried out, and the urban color main color design plan is finally implemented in the implementation of the urban architectural color, which effectively realizes the quantitative control of the urban color. When evaluating smart city color resources, the color weight value of public facilities is 0.147, which accounts for 14.7% of the total weight value. This research helps to enhance the regional and local cultural characteristics.

Ақтөбе өнеркәсіптік агломерациясының табиғи-аумақтық және аква кешендердің трансформациясына әсері

Қазіргі таңда Ақтөбе агломерациясы Қазақстанның өнеркәсіп орталықтарының бірі және сәйкесінше геоэкологиялық жағдайлардың орталығы болып табылады. Тұрақты бақылаулардың нәтижесінде мақалада техногенездің табиғи-аумақтық кешендердің трансформациясына әсері сипатталған. Зерттеулер көрсеткендей, объектілердің кеңістіктік орналасуын, сондай-ақ олардың жағдайын сипаттайтын дұрыс құрылған географиялық ақпараттық деректер базасы қала аумақтарының геожүйелерінің жағдайын талдауға көмектеседі. Аймақтағы экологиялық қауіпсіздікті қамтамасыз ету үшін табиғи-ресурстық әлеует туралы сенімді деректер қажет.

Methodologycal problems and approaches to identifying and evaluating typical and rare landscapes of the Republic of Belarus

The analysis of the studying of the problem of identifying and evaluating of typical and rare landscapes was carried out, which showed that quite a lot of knowledge has been accumulated in this area of the identification of such landscapes and the criteria for their assessment. It is established that insufficient attention is paid to the classification and mapping of landscapes, the use of mathematical methods for their assessment. A scientific concept is proposed for the study of typical and rare landscapes, which contains four levels of their study (country, regional, subregional, local), the scale of work corresponding to the level, landscape classification units, criteria and indicators for their allocation are determined. So, at the country level, the criterion for assessing typical landscapes is the degree of representativeness, depending on bioclimatic factors and the large size of the landscape contour, at the regional (provincial) – the contour size of the dominant landscapes of the province and the index of landscape diversity, at the subregional – the intensity of anthropogenic transformation, at the local – characteristic value of attractiveness. The main criteria for assessing rare landscapes at the country level are the small size of the contour and the criteria of uniqueness, at the regional (provincial) indicator of occurrence, at the subregional – naturalness, at local – vulnerability. A geographic information database has been created, a methodology has been developed for identifying typical and rare landscapes and their typology. An assessment of landscape diversity was made and the map showing that typical landscapes are characterised by a high and medium level of diversity, and rare landscapes are characterised by medium and low was created. An assessment of the occurrence of rare landscapes was carried out, which made it possible to distinguish three groups – low, medium, and high occurrence; their list and a map of occurrence were created. It has been determined that unique landscapes, which are part of a group with low occurrence, require special protection. A direct correlation has been established between the level of landscape diversity and occurrence. The proposed criteria for identifying and assessing typical and rare landscapes can be used in organising or transforming nature protected areas, in the design of their passports and conservation obligations.

Development of the structure of the geospatial database for beekeeping

The work draws attention to the importance of beekeeping to provide the population of Ukraine with food, as pollination is necessary to obtain many foods and more. The method of collecting information using Remote sensing is mentioned. And how is the operational approach to identifying possible locations of apiaries. The main problems of accumulation of a large amount of data are revealed, namely the methods of storing geospatial data. The main purpose is to develop a model of geographic information database that will store information about land (areas) safe and dangerous for bees. The necessity of such a database is substantiated, its constituent parts (land plots, safe zones with fodder base of beekeeping, buffer zones around dangerous objects, annual and perennial vegetation) are defined and described. The paper also gives examples of similar geoinformation databases of the European Union, the purpose of which is to monitor the forest stands of Europe (The Forest Information System for Europe). An example of application of the developed database in the country is presented and the direction of development with the help of geoinformation technologies is determined.

Analysis of Human-Land Coupled Bearing Capacity of Qiangtang Meadow in Northern Tibet Based on Fuzzy Clustering Algorithm

Aiming to address the problems of high energy consumption, low efficiency, low correlation between the analyzed and actual results, and poor rationality of research indexes in current methods of analysis of human-land coupled bearing capacity of meadows, a novel method of human-land coupled bearing capacity analysis of Qiangtang meadow in northern Tibet, based on fuzzy clustering algorithm, is proposed. Basic geographic information data in Tibet were acquired, the collected data images were registered by ENVI4.2 software, and the collected data were vectorized by ArcGIS 9.3 software to construct a basic geographic information database in Tibet. Based on the frequency domain processing algorithm, the geographic information image was suppressed by noise and filtered by using a high-pass filter to realize the geographic information data processing in the study area. The human-land coupled bearing capacity analysis of Qiangtang meadow in northern Tibet was evaluated through fuzzy clustering, bearing capacity evaluation, and bearing capacity calculation under the sharing of closure. The experimental results showed that the average running energy consumption of the method was 81 J, and 97% of the analyzed results were consistent with the actual situation. These results indicate that the operation efficiency of the method is high, and the rationality coefficient of the research index is large. The proposed method has superior performance and feasibility.