Geographic Information Database Research Articles

Research on Dynamic Updating Technology of Digital Maps for Mobile Communications Based on Multi source Remote Sensing Images

Abstract. With the rapid development of 5G networks in China, mobile communication network simulation technology has gradually become a widely studied direction in the academic and application fields. This study focuses on three-dimensional digital geographic information data for mobile communication, including a large amount of geographic information data that significantly affects the propagation of mobile communication radio waves, such as terrain height and ground use types. These data play a crucial role in coverage prediction, interference analysis, and frequency planning in various telecommunications planning software. This article starts from the perspective of mobile communication network management, and combines typical large-scale terrain data production practice projects for mobile communication to deeply study the process and system function design of combining geographic information systems with multi-source remote sensing data in mobile communication network monitoring and management. On this basis, this study designed and implemented a dynamic updated terrain database for mobile communication. This database can update geographic information data in real-time, ensuring the timeliness and accuracy of the data. At the same time, by optimizing the database structure and query algorithms, the speed and efficiency of data processing have been improved, providing efficient and reliable solutions for coverage prediction, optimization analysis, and planning management of large-scale mobile communication networks.In summary, dynamic updating database of three-dimensional digital geographic information has important application value in mobile communication network simulation technology. Through in-depth research and application of this database, the performance and service quality of mobile communication networks can be effectively improved, laying a solid foundation for future communication technology development.

Geoinformation model for risk management in Burundi

This paper is focused on the development of a geographic information system model for decision preparation. The application of indicator approach for visualization of risks associated with agricultural activities is presented. The paper describes not only the functioning of the geographic information model, but also the interaction between the geographic information model, database and web application in order to present in real time the geographic risks inhibiting agricultural activities.

Pathways and technologies for modern irrigation services: Iran country paper

AbstractI.R. Iran, with 8.8 million ha of irrigated area, is fifth in the world with respect to irrigated land. Scarcity of water resources and great difficulty with water accessibility have brought about initiatives of technology building, as well as approaches to agricultural water management by local communities. During recent decades a huge public budget has been invested in order to improve irrigation services. Improving performance of irrigation systems needs proper diagnosis of the problems and a systematic approach towards modernization. Also having been recently developed and modernized, adjustment of the laws and regulations of the water sector has caused management frameworks of irrigation networks to be gradually improved. In general, in addition to the management framework, equipping with modern tools for measuring water flow and applying tools such as geographic information systems and databases have led to the improvement of water distribution and management. Technical, managerial and policies programmes, as well as investment in water resources and irrigation infrastructures in addition to training and education of farmers and field engineers in water saving in agriculture and enhancing water productivity, resulted in positive impacts on better management of limited fresh water in irrigation.

Constructing a digital system of historical geographic information from the perspective of digital humanities: a case study of the historical geographic information database of Tibetan Buddhist monasteries

Abstract Digital techniques are critical in the digital age. Research in various disciplines requires the use of digital technology. Extracting and digitizing information from text, particularly historical geographical information, is the most fundamental operation of digital humanities. However, digitizing historical geographical information is a challenging work. This article proposes a framework for constructing a digital system of historical geographic information. The feasibility of the digital system was verified in a case study of Tibetan Buddhist monasteries in Tibetan inhabited areas of China. The results demonstrate that the digital system proposed in this study can be used as a guide for digitizing historical geographic information of Tibetan Buddhist monasteries from the perspective of digital humanities. This study provides a useful reference for the digitalization of historical geographic information in the digital humanities discipline.

Digitization, and Coding, with Optimizing, of Iraqi Personnel Home Addresses forward, minimizing Storage Space, and Processing time

Individual Home Address is considered as an important sub set of attributes, and never find personnel record, whether a person is student, patient, or employee, etc. without having a home address. It is required to be recorded, registered, and documented in all personnel information systems, whether the system is manual or computerized. This research concern on the Coding, and Digitization of home address of Iraqi personnel in order to enhancing storage efficiency and reduce processing time. The conventional method of maintaining paper-based records for personnel home addresses has proven to be costly, and timeconsuming, and in most cases inefficient. The proposed solution involves the conversion of physical addresses into digital formats, allowing for streamlined data storage and faster retrieval, and update processes. The work starting by indicate the current challenges related to the paper-based address systems, including the demands on physical storage space and the delays incurred during information retrieval. Subsequently, it explores the potential benefits of digitizing home addresses, such as reduced storage requirements, improved data accessibility, and enhanced overall organizational efficiency. To implement this digitization process, the research investigates various technological solutions, including geographic information systems (GIS), databases, and data management protocols. The study also addresses potential concerns related to data security and privacy, proposing measures to safeguard sensitive information. The proposed solution for Digitization, Coding, with Optimizing the Personnel Home Address provide at least 85% of Optimizing Factor for each individual personnel record required storage space and processing time, this solution subsequently leed to better daily decision-making process in most business organizations.

Approximate sorting and its applications in I/O model

Sorting is a fundamental component in many different applications including web indexing engines, geographic information systems, data mining and database systems. However, the existing optimal algorithms still cause huge costs when the input data becomes very large. Thus, the approximate sorting for big data is considered in this paper. The goal of approximate sorting for big data is to generate an approximate sorted result, but using less CPU and I/O cost. For big data, we consider the approximate sorting and applications for approximate sorted results in I/O model.The quality of approximate sorting results is usually measured by the distance metrics on permutation space. However, the existing metrics on permutation space are not available for external approximate sorting algorithms. Thus, we propose a new kind of metric named External metric, which ignores the errors and dislocations that happened in each I/O block. The External metric of Spearman's footrule metric is named as External Spearman's footrule metric (short as ESP metric), which is analyzed in this paper.Furthermore, to facilitate a better evaluation of the approximate sorted result, we propose a new metric, named as errors, which directly states the number of dislocation of the elements. The External errors are also considered in this paper.Then, according to the rate-distortion relationship under these two metrics, the lower bound of these two metrics on external approximate sorting problem with t I/O operations is proved. We propose a k-passes external approximate sorting algorithm, named as EASORT, and prove that EASORT is asymptotically optimal under ESP metric.Finally, we consider the applications on approximate sorting results. An index for the approximate sorted result is proposed. The single and range query on approximate sorted result using this index are also analyzed. Further, the sort-merge join on two relations, where one of the relations is approximate sorted or both relations are approximate sorted, are all discussed in this paper.

KEY TECHNOLOGIES FOR 1:10,000 RAPID MAPPING BASED ON GF-7 SATELLITE

Abstract. In order to solve the problems of low efficiency, high cost and long cycle of the traditional 1:10000 basic geographic information data production method (aerial photography combined with field mapping), this paper proposes a 1:10000 rapid mapping method based on GF-7 satellite data. It solves the key technical difficulties of automatic information recognition, extraction, integration and mapping of water body, building, road, vegetation, contour, and other elements. And an automatic software system of “image -> patch -> data -> map” is developed, which realizes the rapid production of 1:10000 basic geographic information data products and improves the ability of high-precision basic geographic information resource construction.

Geographic Information Database of Herbs against COVID-19 in Thailand: The Medicinal Plants those Folk Healers Commonly Used for Treatment and Boosting People's Immunity

The rapid outbreak of coronavirus disease 2019 (COVID-19) has demonstrated the need for the development of new vaccine candidates and therapeutic drugs to fight against the underlying virus, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Currently, no antiviral treatment is available to treat COVID-19, as treatment is mostly directed at relieving the symptoms, and retrospectively, herbal medicinal plants have been used for thousands of years as a medicinal alternative, including for the treatment of various viral illnesses. The aim of this study is to conduct a survey in terms of identifying the area where the population commonly uses the medicinal plant in comparison to the cumulative number of COVID-19 reports in each area, including the classification of medicinal plants by type and a stepwise approach shown in the form of geographic information maps in those areas. An observational study on the cultivation of medicinal plants those folk healers commonly used for healing. beneficial for treatment and strengthening the immunity of the people in 9 provinces of Thailand. According to the situation of the spread of COVID-19, there are people infected in Thailand. In each area where medicinal plants were used, there was a significant positive result when compared to the cumulative COVID-19 incidence; the majority was with the lowest cumulative COVID-19 incidence and the most commonly used medicinal plants, such as Artemisia annua, Harrisonia perforate (Blanco) Merr, Capparis micracantha, Tacca leontopetaloides, Andrographis paniculata, Phyllanthus emblica, Ficus carica, Tiliacora triandra, Terminalia bilaria, and Cannabis indica. This study exercise may lend enough credence to the potential value of Thai medicinal plants (herbs) as possible leads in anti-COVID-19 drug discovery through research and development.

A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, traditional extraction methods often have problems, such as subjective parameter settings and inconsistent cartographic scales, making it difficult to identify PUBs objectively and accurately. To address these problems, we proposed a self-supervised learning approach for PUB extraction. First, we used nighttime light and OpenStreetMap road data to map the initial urban boundary for data preparation. Then, we designed a pretext task of self-supervised learning based on an unsupervised mutation detection algorithm to automatically mine supervised information in unlabeled data, which can avoid subjective human interference. Finally, a downstream task was designed as a supervised learning task in Google Earth Engine to classify urban and non-urban areas using impervious surface density and nighttime light data, which can solve the scale inconsistency problem. Based on the proposed method, we produced a 30 m resolution China PUB dataset containing six years (i.e., 1995, 2000, 2005, 2010, 2015, and 2020). Our PUBs show good agreement with existing products and accurately describe the spatial extent of urban areas, effectively distinguishing urban and non-urban areas. Moreover, we found that the gap between the national per capita GDP and the urban per capita GDP is gradually decreasing, but regional coordinated development and intensive development still need to be strengthened.

Research on optimal path planning technology for vehicle positioning and navigation system

Abstract Intelligent Transportation System (ITS) is created in the context of the full development and progress of contemporary science and technology, aiming to help people make the best choice, drive the best vehicle, and control the best road network traffic. Intelligent vehicle positioning and navigation system is a high-tech integrated system that applies automatic vehicle positioning technology, geographic information system and database technology, computer technology, multimedia technology, and modern communication technology. In this paper, based on the research of GPS and GIS technology, by analyzing and comparing the advantages and disadvantages of different optimal path planning algorithms, we propose to optimize the A* algorithm by using the minimum heap sorting method for nodes and combining the twoway search and hierarchical search strategies to complete the improvement of the A* algorithm. The experimental results show that the path search speed of the improved algorithm has been significantly improved and the path design optimization is obvious. The improvement algorithm proposed in this paper is feasible and reasonable, which is good for upgrading the in-vehicle navigation system and relieving urban traffic congestion.

Digital archaeology today: achievements and challenges

Over the last decade, the active use of various computer methods and digital technologies has had a profound impact on modern archaeological research. New methods of field recording have emerged, and modern tools such as laser total stations and scanners, GNSS receivers and unmanned aerial vehicles have become firmly embedded in archaeological practice. A whole trend of the Digital Archaeology has emerged within the framework of which many archaeologists are working. It seems relevant to summarize some of the results of the development of digital archaeology over the past decades and to highlight the main trends in the modern use of a number of technological techniques that have significantly affected the image of archaeological research at this stage. The article highlights the main methods and approaches in digital recording, analysis and visualization of archaeological information: geographic information systems, digital archives and databases, field electronic diaries, photogrammetry and laser scanning methods of different spatial levels, as well as the first steps in the use of artificial intelligence in archaeological practice. Such an overview, although limited, covers for the first time all the major trends in digital archaeology of the last two decades. In addition to the achievements that are certainly present in the works cited, the author pays special attention to some of the challenges that arise in the process of implementing digital archaeology in everyday archaeological practice.

Forest-Fire-Risk Prediction Based on Random Forest and Backpropagation Neural Network of Heihe Area in Heilongjiang Province, China

Forest fires are important factors that influence and restrict the development of forest ecosystems. In this paper, forest-fire-risk prediction was studied based on random forest (RF) and backpropagation neural network (BPNN) algorithms. The Heihe area of Heilongjiang Province is one of the key forest areas and forest-fire-prone areas in China. Based on daily historical forest-fire data from 1995 to 2015, daily meteorological data, topographic data and basic geographic information data, the main forest-fire driving factors were first analyzed by using RF importance characteristic evaluation and logistic stepwise regression. Then, the prediction models were established by using the two machine learning methods. Furthermore, the goodness of fit of the models was tested using the receiver operating characteristic test method. Finally, the fire-risk grades were divided by applying the kriging method. The results showed that 11 driving factors were significantly correlated with forest-fire occurrence, and days after the last rain, daily average relative humidity, daily maximum temperature, daily average water vapor pressure, daily minimum relative humidity and distance to settlement had a high correlation with the risk of forest-fire occurrence. The prediction accuracy of the two algorithms in regard to fire points was higher than that for nonfire points. The overall prediction accuracy and goodness of fit of the RF and BPNN algorithms were similar. The two methods were both suitable for forest-fire occurrence prediction. The high-fire-risk zones were mainly concentrated in the northwestern and central parts of the Heihe area.

Application of 3D Digital Modeling Technology in the Construction of Digital Cities

The concepts of “digital twins”, “3D real scene”, “metacosm” and others were the technical paths for building digital cities with the development of emerging surveying and mapping science and technology, which was to build a digital and virtualized city that matched the real physical world, to achieve a one-to-one correspondence between all elements of the physical world and the digital virtual world. And one of its basic geographic information data was a highly similar, virtual simulation of the 3D real scene. After exploring the traditional manual 3DsMax modeling, UAV low-altitude digital oblique photogrammetry modeling, airborne laser scanning modeling and other single modeling technologies, this paper discussed the 3D digital modeling technology used by the UAV airborne laser scanning point cloud and low-altitude digital oblique photogrammetry for complementary integration, constructing the 3D scene of the digital city. This paper expounded the technical route and production process of 3D digital modeling, in order to provide technical references for related projects.

METHOD OF IDENTIFICATION OF TREE SPECIES COMPOSITION OF FORESTS ON THE BASIS OF GEOGRAPHIC INFORMATION DATABASE


 
 
 Monitoring of the forests with the help of remote sensing of the Earth systems is actual on the present stage of people development. Existing systems of research do not allow to use geographic information databases and don't include the spectrum of exploration that are presented in our work. The aim of this article is to develop the method of identification of tree species composition of forests on the basis of geographic information database in the integration with correlation analysis. It will give the possibility to find the most negative things that influence on the condition of forest ecosystems and neutralize them at the first stage of their influence. Satellite images that are received from the satellites Landsat 5 and 8 are used to make the database. Tree species composition of Mozhariv forest during 20 years is analyzed as an example in this article. Classification of tree species composition of forests using space images is made on the basis of Bayes classifier. There is a special geographic information database for further data processing and identification of tree species composition is developed. Forest is analyzed on the number of trees in each species during continuous time measures (in 2000, 2010 and 2020) in this article. Results of the work are presented with the help of graphics. Correlation analysis, which helps to make the analysis of tightness of connections between changes trees in number and different forest criteria, is made to identify tree species composition of forest. Developed method together with well-known methods of monitoring of forests help us to make effective application for analysis of tree species composition of forest changes and to make better existing information systems including different quality demands. Peculiarity of developed information system is in using of the data processing module as a part of geographic information system and implementing correlation analysis with identification of factors that influence each other. It helps to increase quality in management and monitoring of forest resources. Geographic information becomes available for users.
 
 

A fine construction method of urban road DEM considering road morphological characteristics

Urban road DEM is not only an important basic geographic information data of the city, but also an important element to describe and express the urban topography, and it is an indispensable part of the construction of the smart digital city, urban planning and urban surface process simulation. Previous methods for constructing urban road DEMs do not sufficiently consider the actual morphological characteristics of urban roads, and morphological distortion is evident in the expression of urban roads, seriously affecting the application of urban rainfall flood simulation and urban pipe network design. In response to these problems, this study proposed a considering morphological characteristics fine (CMCF) method of urban road DEM construction, selected a typical urban road area in the Jianye District of Nanjing City in China as the study area, used the 1:500 digital line graphic as data source, hierarchized roads in accordance with different morphological characteristics from the perspective of DEM construction, constructed the corresponding DEMs, and finally merged all levels of road DEMs to produce a complete high-precision urban road DEM. Results showed that the DEM constructed using the CMCF method not only exhibited higher elevation accuracy than the urban road DEM constructed using previous methods, i.e., inverse distance weighting (IDW) and triangulated irregular network (TIN) methods, with a mean error and a root-mean-square error of 0.015 and 0.060, respectively, but it can also accurately express the spatial element composition form and road morphological characteristics of urban roads, avoiding the distorted expression of road morphological characteristics. This study can provide a reference for a new DEM construction method and data support for smart digital city construction and urban surface simulation.

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.