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JournalISSN: 2080-6736

Geodesy and Cartography 

De Gruyter Open
About: Geodesy and Cartography is an academic journal published by De Gruyter Open. The journal publishes majorly in the area(s): Computer science & Remote sensing. It has an ISSN identifier of 2080-6736. It is also open access. Over the lifetime, 46 publications have been published receiving 14 citations.

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Journal ArticleDOI
TL;DR: In this paper , the authors aimed at the promotion of voluntary land consolidation through the improvement of land plots exchange, and the issue of the existing land plots boundaries adjustment in the course of land consolidation has been singled out.
Abstract: The paper is aimed at the promotion of voluntary land consolidation through the improvement of land plots exchange. The issue of the existing land plots boundaries adjustment in the course of voluntary land consolidation has been singled out. Possibilities, advantages and risks of land plots exchange without changing the existing boundaries as a constituent of land consolidation measures have been substantiated. The improvement of approaches to land plots exchange modelling without the existing boundaries adjustment has been suggested. Demands to the formation of consolidated land tenures as the result of exchange have been singled out. Theory of combinations has been applied to specify land plots exchange options. Calculation formulas for the number of the optimal consolidated agricultural land tenure placement options have been suggested. The calculations can be applied in the optimization and heuristic approaches to land reallocation and at land consolidation performance evaluation. Suggested approaches facilitate the implementation of heuristic methods especially in non-standard conditions, allow to increase the number of developers, especially involving experts with little experience. The results can be used for land plots lease optimization.

4 citations

Journal ArticleDOI
TL;DR: In this article , the authors developed a working method for classifying the land cover using high-resolution satellite images using object based method, which showed that use of textural data during the object image classification approach can considerably enhance land use classification performance.
Abstract: Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that use of textural data during the object image classification approach can considerably enhance land use classification performance. Moreover, the results showed higher overall accuracy (86.02%) in the o object based method than pixel based (79.06%) in urban extractions. The object based performed much more capabilities than pixel based.

3 citations

Journal ArticleDOI
TL;DR: The article deals with the matter of restoring the geofield based high-intensity spatio-temporal data streams received from a highly mobile geosensors network in real time, and proposes to apply the method of local smoothing for solving this task.
Abstract: The article deals with the matter of restoring the geofield based high-intensity spatio-temporal data streams received from a highly mobile geosensors network in real time. It includes dozen thousands of active measuring devices that measure at a frequency of up to 5 Hz and are installed on mobile platforms moving at speeds of up to 80–100 km/h. For solving this task, we propose to apply the method of local smoothing. To adapt a kernel of local regression to the conditions of a specific task, such as the features of the terrain or the simulated geofield, as well as the characteristics of the geosensor network, the authors suggested using the kernel of local regression obtained through using stochastic optimization methods. The application of genetic algorithms and the method of simulating annealing for this purpose are considered. The structure of a generalized method based on this idea is presented in the article. The results of model experiments aimed to confirm the operability of this method and those of evaluating the performance of this method in single-threaded mode are given.

3 citations

Journal ArticleDOI
TL;DR: The research shows that the National atlas site (nationalatlas.ru) has been functioning for fifteen years already, and the information provided still remains relevant.
Abstract: Exploring the relevance of the contents loaded in site Russian federation National atlas is considered in the article. Its attendance and the provided data’s importance for the period of the recent 7 years are analyzed. This analysis made a background for the author on renovation of the resource. During the research some foreign experience on creating printed and digital national atlases was studied. Basing on the modernization results the change in activities of the users was noted in comparison with their previous ones. The research shows that the site has been functioning for fifteen years already, and the information provided still remains relevant. Due to connecting the domain .ru the renovated resource became more easily available for foreign audience. In the period of the Russian Federation digital transformation the National atlas site (nationalatlas.ru) with its updated contents can make a part of the National spatial data system.

1 citations

Journal ArticleDOI
TL;DR: In this article , the authors used Principal Component Analysis (PCA) to identify key components to amplify signals and reduce noise in observations, and used Self-organizing Map Algorithm (SOM) to cluster total water storage (TWS) in 4 categories.
Abstract: The gravity recovery and climate experiment (GRACE) and GRACE-Follow on (FO) data provide valuable information about dynamic total water storage (TWS). The complexity of the computational process and the influence of various parameters on TWS changes are complicated in their interpretation. Principal component analysis (PCA) has been used to identify key components to amplify signals and reduce noise in observations. For this purpose, in this research, the Self-organizing map algorithm (SOM) has been used to cluster TWS in 4 categories. The results show that the western regions of Greenland and part of Antarctica are in the critical cluster and have a TWS rate of about –0.2 m/year, which indicates the melting of ice in these regions. The advantage of PCA-SOM is the easy interpretation of TWS, which reduces the impact of seasonal parameters, observation noise and measurement error, and facilitates global policy decisions in the face of climate change.

1 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202314
202235