Pub Date : 2024-04-20DOI: 10.22389/0016-7126-2024-1005-3-42-49
I.S. Brylev, V. Budarova
In this article, one of the possible approaches to the urgent problem of information modeling systems development for three-dimensional urban space is proposed. The subject of the study was the capital construction object “Peter Stolypin Business house”, located at 8B, 50 let Oktyabrya Street, Tyumen, RF. In order to carry out the facade survey of the mentioned building, a number of measuring works was done using a ground-based laser scanning device Faro Focus S350. As a result, a general technological scheme was created for collecting, processing, modeling and visualizing the data in special software products for creating a three-dimensional space of the territory, which enables forming similar BIM models of various real estate objects. It can later be used to develop an urban area of the same format
{"title":"Application of a digital technology complex to form a three-dimensional real estate object in urban space","authors":"I.S. Brylev, V. Budarova","doi":"10.22389/0016-7126-2024-1005-3-42-49","DOIUrl":"https://doi.org/10.22389/0016-7126-2024-1005-3-42-49","url":null,"abstract":"\u0000In this article, one of the possible approaches to the urgent problem of information modeling systems development for three-dimensional urban space is proposed. The subject of the study was the capital construction object “Peter Stolypin Business house”, located at 8B, 50 let Oktyabrya Street, Tyumen, RF. In order to carry out the facade survey of the mentioned building, a number of measuring works was done using a ground-based laser scanning device Faro Focus S350. As a result, a general technological scheme was created for collecting, processing, modeling and visualizing the data in special software products for creating a three-dimensional space of the territory, which enables forming similar BIM models of various real estate objects. It can later be used to develop an urban area of the same format\u0000","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":"114 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140679654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-20DOI: 10.22389/0016-7126-2024-1005-3-6-13
M. Mustafin, Kh.I. Moussa
The technology for determining the coordinates of points on the earth using the global navigation satellite system (GNSS) is becoming a standard along with ground-based methods. In this case, determining the plane coordinates of points does not cause any particular difficulties. However, to identify normal altitudes using this technique with a given accuracy, a special research is required. The fact is that according to satellite definitions, the geodetic height (H) is directly obtained, which differs from the normal one (HN) by an amount called height anomaly. This and the above mentioned value itself can be determined from the results of satellite leveling, taking into account the gravitational model of the Earth. But without clarification through ground measurements the result may not meet the required accuracy. In this work, geodetic and normal heights were determined for 5 control points in the Mount Lebanon region, where surveys were carried out using GNSS technology and geometric levelling. The obtained data were compared with satellite levelling one using the EGM2008 Earth model. In this case, geometric levelling was performed along different routes to ensure the information redundancy and determine average values. Thus, the normal heights obtained using the referred technology (quasi-geoid) served to correct those of the EGM2008 Earth model. The results of creating an altitudinal base in a local area corresponding to the foothill area are presented
{"title":"Results of creating an altitude-base using a local quasi-geoid model in the Republic of Lebanon","authors":"M. Mustafin, Kh.I. Moussa","doi":"10.22389/0016-7126-2024-1005-3-6-13","DOIUrl":"https://doi.org/10.22389/0016-7126-2024-1005-3-6-13","url":null,"abstract":"\u0000The technology for determining the coordinates of points on the earth using the global navigation satellite system (GNSS) is becoming a standard along with ground-based methods. In this case, determining the plane coordinates of points does not cause any particular difficulties. However, to identify normal altitudes using this technique with a given accuracy, a special research is required. The fact is that according to satellite definitions, the geodetic height (H) is directly obtained, which differs from the normal one (HN) by an amount called height anomaly. This and the above mentioned value itself can be determined from the results of satellite leveling, taking into account the gravitational model of the Earth. But without clarification through ground measurements the result may not meet the required accuracy. In this work, geodetic and normal heights were determined for 5 control points in the Mount Lebanon region, where surveys were carried out using GNSS technology and geometric levelling. The obtained data were compared with satellite levelling one using the EGM2008 Earth model. In this case, geometric levelling was performed along different routes to ensure the information redundancy and determine average values. Thus, the normal heights obtained using the referred technology (quasi-geoid) served to correct those of the EGM2008 Earth model. The results of creating an altitudinal base in a local area corresponding to the foothill area are presented\u0000","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":"113 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-20DOI: 10.22389/0016-7126-2024-1005-3-14-23
V.N. Bogdanov, G. Dugarova
The authors examine the spatial dynamics and distribution of the population and analyze the features and nature of Ulaanbaatar, the capital of Mongolia, development. A geoinformation system that enables spatial analysis of data and obtaining new knowledge at multi-scale levels was created. Based on the compiled maps, intra-district differences are described in terms of khoroo, showing real differentiation within the city. The feature of the article is the study of the territory at the level of urban microdistricts (khoroo), which is practically focused along with the possibility of using the results obtained for operational management and identifying various problems, such as those of unbalanced development of the urban area, due primarily to the colossal intra-city differentiation of many factors, for instance, the dynamics and distribution of the population, the provision of infrastructure facilities, transport accessibility, etc. The study revealed that the city`s population is gradually shifting to new residential developments surrounding the center, with relatively good social conditions, while at the same time the residents are moving out of khoroo with old yurt buildings and poor quality of life, but almost half of them are still living there
{"title":"Spatial differentiation at the micro level (example of Ulaanbaatar, Mongolia)","authors":"V.N. Bogdanov, G. Dugarova","doi":"10.22389/0016-7126-2024-1005-3-14-23","DOIUrl":"https://doi.org/10.22389/0016-7126-2024-1005-3-14-23","url":null,"abstract":"\u0000The authors examine the spatial dynamics and distribution of the population and analyze the features and nature of Ulaanbaatar, the capital of Mongolia, development. A geoinformation system that enables spatial analysis of data and obtaining new knowledge at multi-scale levels was created. Based on the compiled maps, intra-district differences are described in terms of khoroo, showing real differentiation within the city. The feature of the article is the study of the territory at the level of urban microdistricts (khoroo), which is practically focused along with the possibility of using the results obtained for operational management and identifying various problems, such as those of unbalanced development of the urban area, due primarily to the colossal intra-city differentiation of many factors, for instance, the dynamics and distribution of the population, the provision of infrastructure facilities, transport accessibility, etc. The study revealed that the city`s population is gradually shifting to new residential developments surrounding the center, with relatively good social conditions, while at the same time the residents are moving out of khoroo with old yurt buildings and poor quality of life, but almost half of them are still living there\u0000","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":"8 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-20DOI: 10.22389/0016-7126-2024-1005-3-50-61
A. Portnov, D.О. Dobrovolsky
The authors substantiate the relevance of the tasks of developing methods ensuring the greatest efficiency of implementing state land supervision and monitoring using automated procedures for the centralized formation of an annual inspection plan. The mechanisms of identifying natural objects, buildings and structures as potential ones included in the annual inspection plans on the mentioned issue are described. This meets many goals and, above all, the safety of land use, and eliminating negative processes of land degradation. Examples of using aerial photographs as the most significant practice at detecting violations in the field of land protection and use are given. To a greater extent, this applies to real estate cadastre objects with simpler geometric shapes, e.g. boundaries of land plots, buildings. The methods of comparing the geometric complexity of contours proposed in the study enable creating automated mechanisms and determine discrepancies between the actual and recorded characteristics of control objects, depending on the set goals and objectives. The expediency determining mechanisms of automated search for features with signs of land legislation violations are presented. It simplifies the implementation of control measures and makes the inspection system itself more transparent. The purpose of the research was to study the possibility of applying the theory of geometric complexity in the implementation of a centralized system of state land supervision and monitoring. In this regard, we made an attempt to use Minkovsky metrics for simpler geometric structures in contrast to natural objects, as well as morphometric indicators to identify those where land legislation is not being followed. The relative criteria values of the real estate cadastre control’s compared objects’ geometric complexity are numerically determined and proposed
{"title":"Comparative assessment of the terrain objects contours’ geometric complexity at implementing state land supervision and monitoring, an example of capital construction projects","authors":"A. Portnov, D.О. Dobrovolsky","doi":"10.22389/0016-7126-2024-1005-3-50-61","DOIUrl":"https://doi.org/10.22389/0016-7126-2024-1005-3-50-61","url":null,"abstract":"\u0000The authors substantiate the relevance of the tasks of developing methods ensuring the greatest efficiency of implementing state land supervision and monitoring using automated procedures for the centralized formation of an annual inspection plan. The mechanisms of identifying natural objects, buildings and structures as potential ones included in the annual inspection plans on the mentioned issue are described. This meets many goals and, above all, the safety of land use, and eliminating negative processes of land degradation. Examples of using aerial photographs as the most significant practice at detecting violations in the field of land protection and use are given. To a greater extent, this applies to real estate cadastre objects with simpler geometric shapes, e.g. boundaries of land plots, buildings. The methods of comparing the geometric complexity of contours proposed in the study enable creating automated mechanisms and determine discrepancies between the actual and recorded characteristics of control objects, depending on the set goals and objectives. The expediency determining mechanisms of automated search for features with signs of land legislation violations are presented. It simplifies the implementation of control measures and makes the inspection system itself more transparent. The purpose of the research was to study the possibility of applying the theory of geometric complexity in the implementation of a centralized system of state land supervision and monitoring. In this regard, we made an attempt to use Minkovsky metrics for simpler geometric structures in contrast to natural objects, as well as morphometric indicators to identify those where land legislation is not being followed. The relative criteria values of the real estate cadastre control’s compared objects’ geometric complexity are numerically determined and proposed\u0000","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ch. Kouassi, Chen Qian, Dilawar Khan, L. Achille, Zhang Kebin, J. K. Omifolaji, Tu Ya, Xiaohui Yang
Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach u
{"title":"LAND USE LAND COVER CHANGE MAPPING FROM SENTINEL 1B < 2A IMAGERY USING RANDOM FOREST ALGORITHM IN CÔTE D’IVOIRE","authors":"Ch. Kouassi, Chen Qian, Dilawar Khan, L. Achille, Zhang Kebin, J. K. Omifolaji, Tu Ya, Xiaohui Yang","doi":"10.3846/gac.2024.18724","DOIUrl":"https://doi.org/10.3846/gac.2024.18724","url":null,"abstract":"Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach u","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":"287 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traffic noise mapping frequently employs Kriging, Inverse Distance Weighted (IDW), and Triangular Irregular Networks (TIN) spatial interpolations. This study uses the Henk de Kluijver noise model to evaluate the performance of spatial interpolations. Effective traffic noise mapping requires that noise observation points (Nops) be designed as 2 m grids. The upper and lower slopes function as noise barriers to reduce sound levels. Therefore, assessment of accuracy is essential for visualising noise levels in undulating and level terrain. In addition, this work gives an accurate comparison of traffic noise interpolation in undulating areas. The elements of spatial interpolations, such as the weighting factor, variogram, radius, and number of points influence the interpolation accuracy. The Kriging with a Gaussian variogram, where the radius is 5 m and the number of points is 12 demonstrates the highest level of precision. However, there is no direct relationship between accuracy validation and cross-validation. In cross-validation, however, the accuracy of the Gaussian variogram with a 7 m radius and 18 points is more accurate. In addition, this study demonstrates that Kriging is superior for extrapolating noise levels in undulating regions. Accurate visualising traffic noise levels requires a prior understanding of spatial interpolations.
{"title":"PERFORMANCE ASSESSMENT OF SPATIAL INTERPOLATIONS FOR TRAFFIC NOISE MAPPING ON UNDULATING AND LEVEL TERRAIN","authors":"N. Wickramathilaka, U. Ujang, S. Azri, T. Choon","doi":"10.3846/gac.2024.18751","DOIUrl":"https://doi.org/10.3846/gac.2024.18751","url":null,"abstract":"Traffic noise mapping frequently employs Kriging, Inverse Distance Weighted (IDW), and Triangular Irregular Networks (TIN) spatial interpolations. This study uses the Henk de Kluijver noise model to evaluate the performance of spatial interpolations. Effective traffic noise mapping requires that noise observation points (Nops) be designed as 2 m grids. The upper and lower slopes function as noise barriers to reduce sound levels. Therefore, assessment of accuracy is essential for visualising noise levels in undulating and level terrain. In addition, this work gives an accurate comparison of traffic noise interpolation in undulating areas. The elements of spatial interpolations, such as the weighting factor, variogram, radius, and number of points influence the interpolation accuracy. The Kriging with a Gaussian variogram, where the radius is 5 m and the number of points is 12 demonstrates the highest level of precision. However, there is no direct relationship between accuracy validation and cross-validation. In cross-validation, however, the accuracy of the Gaussian variogram with a 7 m radius and 18 points is more accurate. In addition, this study demonstrates that Kriging is superior for extrapolating noise levels in undulating regions. Accurate visualising traffic noise levels requires a prior understanding of spatial interpolations.","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":"31 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article describes the sources of geomagnetic data, the reduction of geomagnetic data for the territory of Latvia to the epoch 2021.5, the history of previous magnetic observations in Latvia, the information available in the State Geodetic Network database and the information available in the World Geomagnetism Data Centre. The sequence of absolute measurements is described in detail. To visualise the changes in the magnetic declination value in the territory of Latvia, a 2021.5 year declination fluctuation has been created using ArcGIS Pro. The declination values in Latvia range from 6.68° to 10°, the inclination values range from 71.089° to 72.245° and the total magnetic field values from 51100 nT to 52594 nT. The values obtained for the magnetic field components refer to a magnetically clean environment, and there can be, and are, differences in the natural conditions in the Latvian territory, in natural anomalous locations and in locations with artificially high magnetic field noise (e.g. in cities, near railways, near high voltage lines, etc.). In the Latvian network, points have been selected in locations where the magnetic noise is minimal, as this is the technological process for building such stations. Magnetic observatories are even stricter, so the data coming from the observatories reflect the natural magnetic field without the influence of magnetic anomalies. The reduced magnetic field values and their representation on a map can be used for aeronautical navigation, military applications, identification of local magnetic anomaly sites or search for magnetically clean environments.
文章介绍了地磁数据的来源、将拉脱维亚境内的地磁数据还原至 2021.5 年、拉脱维亚以往的磁观测历史、国家大地测量网络数据库中的可用信息以及世界地磁数据中心中的可用信息。详细介绍了绝对测量的顺序。为直观显示拉脱维亚境内磁偏角值的变化，使用 ArcGIS Pro 创建了 2021.5 年偏角波动图。拉脱维亚的偏角值从 6.68°到 10°不等，倾角值从 71.089°到 72.245°不等，总磁场值从 51100 nT 到 52594 nT 不等。所获得的磁场分量值是指磁场洁净的环境，而拉脱维亚境内的自然条件、自然异常地点和人为高磁场噪声地点（如城市、铁路附近、高压线附近等）可能存在差异。在拉脱维亚的网络中，观测点都选在磁场噪声最小的地方，因为这是建造此类观测站的技术流程。磁场观测站的要求更为严格，因此观测站的数据反映的是自然磁场，不受磁场异常的影响。减小的磁场值及其在地图上的表示可用于航空导航、军事应用、识别当地磁异常点或寻找磁清洁环境。
{"title":"THE REDUCTION OF GEOMAGNETIC DATA FOR THE TERRITORY OF LATVIA TO THE EPOCH 2021.5","authors":"Lubova Sulakova, J. Kaminskis","doi":"10.3846/gac.2024.20996","DOIUrl":"https://doi.org/10.3846/gac.2024.20996","url":null,"abstract":"The article describes the sources of geomagnetic data, the reduction of geomagnetic data for the territory of Latvia to the epoch 2021.5, the history of previous magnetic observations in Latvia, the information available in the State Geodetic Network database and the information available in the World Geomagnetism Data Centre. The sequence of absolute measurements is described in detail. To visualise the changes in the magnetic declination value in the territory of Latvia, a 2021.5 year declination fluctuation has been created using ArcGIS Pro. The declination values in Latvia range from 6.68° to 10°, the inclination values range from 71.089° to 72.245° and the total magnetic field values from 51100 nT to 52594 nT. The values obtained for the magnetic field components refer to a magnetically clean environment, and there can be, and are, differences in the natural conditions in the Latvian territory, in natural anomalous locations and in locations with artificially high magnetic field noise (e.g. in cities, near railways, near high voltage lines, etc.). In the Latvian network, points have been selected in locations where the magnetic noise is minimal, as this is the technological process for building such stations. Magnetic observatories are even stricter, so the data coming from the observatories reflect the natural magnetic field without the influence of magnetic anomalies. The reduced magnetic field values and their representation on a map can be used for aeronautical navigation, military applications, identification of local magnetic anomaly sites or search for magnetically clean environments.","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":"5 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For users of Precise Point Positioning (PPP), Multi-GNSS Advanced Orbit and Clock Augmentation PPP signals provide corrective data. When using the PPP approach and/or PPP-Ambiguity Resolution (AR) method, the QZSS signal provides globally applicable error corrections on satellite orbit, clock offset, and code/phase biases. In addition, from FY2024, as a part of the MADOCA-PPP technology demonstration, wide-area ionospheric correction data will be provided for the Asia-Oceania region. A software estimator of precise satellite information developed by JAXA, Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA), allows u-blox CO99-ZED-F9P and MSJ 3008-GM4-QZS utilizing MADOCA-PPP to be used in GNSS applications that need sub-decimetre precision but don’t have to be expensive. Errors caused by positioning satellites are computed by using observation data from domestic and overseas GNSS monitoring station networks such as IGS and MIRAI, and obtained correction data is transmitted from QZSS signal to provide highly precise positioning augmentation services that can be used in the Asia-Oceania Region. Users may utilize the PPP technique for high-precision locating by employing a GNSS receiver that supports the QZSS signals. This paper describes an experiment carried out with the static method to combine GPS, GLONASS, and QZSS signals in the project site (ISHI, USUD and MIZU stations in Japan). This paper examines the GPS/GLONASS/QZSS obtainable accuracy. These obtained results indicate that integrating GPS system with GLONASS and QZSS is favoured for surveying applications. It appears that integrating GPS/GLONASS/QZSS (MADOCA precise ephemeris file) static measurements in the study area between 0–4 millimetres accuracy can be guaranteed on all occasions.
{"title":"EVALUATION OF THE PERFORMANCE OF MULTI-GNSS ADVANCED ORBIT AND CLOCK AUGMENTATION – PRECISE POINT POSITIONING (MADOCA-PPP) IN JAPAN REGION","authors":"A. Pırtı","doi":"10.3846/gac.2024.17763","DOIUrl":"https://doi.org/10.3846/gac.2024.17763","url":null,"abstract":"For users of Precise Point Positioning (PPP), Multi-GNSS Advanced Orbit and Clock Augmentation PPP signals provide corrective data. When using the PPP approach and/or PPP-Ambiguity Resolution (AR) method, the QZSS signal provides globally applicable error corrections on satellite orbit, clock offset, and code/phase biases. In addition, from FY2024, as a part of the MADOCA-PPP technology demonstration, wide-area ionospheric correction data will be provided for the Asia-Oceania region. A software estimator of precise satellite information developed by JAXA, Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA), allows u-blox CO99-ZED-F9P and MSJ 3008-GM4-QZS utilizing MADOCA-PPP to be used in GNSS applications that need sub-decimetre precision but don’t have to be expensive. Errors caused by positioning satellites are computed by using observation data from domestic and overseas GNSS monitoring station networks such as IGS and MIRAI, and obtained correction data is transmitted from QZSS signal to provide highly precise positioning augmentation services that can be used in the Asia-Oceania Region. Users may utilize the PPP technique for high-precision locating by employing a GNSS receiver that supports the QZSS signals. This paper describes an experiment carried out with the static method to combine GPS, GLONASS, and QZSS signals in the project site (ISHI, USUD and MIZU stations in Japan). This paper examines the GPS/GLONASS/QZSS obtainable accuracy. These obtained results indicate that integrating GPS system with GLONASS and QZSS is favoured for surveying applications. It appears that integrating GPS/GLONASS/QZSS (MADOCA precise ephemeris file) static measurements in the study area between 0–4 millimetres accuracy can be guaranteed on all occasions.","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":"18 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vahid Isazade, Abdul Baser Qasimi, Abdulla Al Kafy, Pinliang Dong, Mustafa Mohammadi
Flood events are the most sophisticated and damaging natural hazard compared to other natural catastrophes. Every year, this hazard causes human-financial losses and damage to croplands in different locations worldwide. This research employs a combination of artificial neural networks and geographic information systems (GIS) to simulate flood-vulnerable locations in the Samangan Province of Afghanistan. First, flood-influencing factors, such as soil, slope layer, elevation, flow direction, and land use/cover, were evaluated as influential factors in simulating flood-prone areas. These factors were imported into GIS software. The Fishnet command was used to partition the information layers. Furthermore, each layer was converted into points, and this data was fed into the perceptron neural network along with the educational data obtained from Google Earth. In the perceptron neural network, the input layers have five neurons and 16 nodes, and the outputs showed that elevation had the lowest possible weight (R2 = 0.713) and flow direction had the highest weight (R2 = 0.913). This study demonstrated that combining GIS and artificial neural networks results in acceptable performance for simulating and modeling flood susceptible areas in different geographical locations and significantly helps prevent or reduce flood hazards.
{"title":"SIMULATION OF FLOOD-PRONE AREAS USING MACHINE LEARNING AND GIS TECHNIQUES IN SAMANGAN PROVINCE, AFGHANISTAN","authors":"Vahid Isazade, Abdul Baser Qasimi, Abdulla Al Kafy, Pinliang Dong, Mustafa Mohammadi","doi":"10.3846/gac.2024.18555","DOIUrl":"https://doi.org/10.3846/gac.2024.18555","url":null,"abstract":"Flood events are the most sophisticated and damaging natural hazard compared to other natural catastrophes. Every year, this hazard causes human-financial losses and damage to croplands in different locations worldwide. This research employs a combination of artificial neural networks and geographic information systems (GIS) to simulate flood-vulnerable locations in the Samangan Province of Afghanistan. First, flood-influencing factors, such as soil, slope layer, elevation, flow direction, and land use/cover, were evaluated as influential factors in simulating flood-prone areas. These factors were imported into GIS software. The Fishnet command was used to partition the information layers. Furthermore, each layer was converted into points, and this data was fed into the perceptron neural network along with the educational data obtained from Google Earth. In the perceptron neural network, the input layers have five neurons and 16 nodes, and the outputs showed that elevation had the lowest possible weight (R2 = 0.713) and flow direction had the highest weight (R2 = 0.913). This study demonstrated that combining GIS and artificial neural networks results in acceptable performance for simulating and modeling flood susceptible areas in different geographical locations and significantly helps prevent or reduce flood hazards.","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":"25 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research seeks to assess the effect of different selected feature descriptors on the accuracy of an automatic image registration scheme. Three different feature descriptors were selected based on their peculiar characteristics, and implemented in the process of developing the image registration scheme. These feature descriptors (Modified Harris and Stephens corner detector (MHCD), the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Feature (SURF)) were used to automatically extract the conjugate points common to the overlapping image pairs used for the registration. Random Sampling Consensus (RANSAC) algorithm was used to exclude outliers and to fit the matched correspondences, Sum of Absolute Differences (SAD) which is a correlation-based feature matching metric was used for the feature match, while projective transformation function was used for the computation of the transformation matrix (T). The obtained overall result proved that the SURF algorithm outperforms the other two feature descriptors with an accuracy measure of -0.0009 pixels, while SIFT with a cumulative signed distance of 0.0328 pixels also proved to be more accurate than MHCD with a cumulative signed distance of 0.0457 pixels. The findings affirmed the importance of choosing the right feature descriptor in the overall accuracy of an automatic image registration scheme.
{"title":"ACCURACY ASSESSMENT OF THE EFFECT OF DIFFERENT FEATURE DESCRIPTORS ON THE AUTOMATIC CO-REGISTRATION OF OVERLAPPING IMAGES","authors":"O. Ajayi, I. J. Nwadialor","doi":"10.3846/gac.2024.18199","DOIUrl":"https://doi.org/10.3846/gac.2024.18199","url":null,"abstract":"This research seeks to assess the effect of different selected feature descriptors on the accuracy of an automatic image registration scheme. Three different feature descriptors were selected based on their peculiar characteristics, and implemented in the process of developing the image registration scheme. These feature descriptors (Modified Harris and Stephens corner detector (MHCD), the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Feature (SURF)) were used to automatically extract the conjugate points common to the overlapping image pairs used for the registration. Random Sampling Consensus (RANSAC) algorithm was used to exclude outliers and to fit the matched correspondences, Sum of Absolute Differences (SAD) which is a correlation-based feature matching metric was used for the feature match, while projective transformation function was used for the computation of the transformation matrix (T). The obtained overall result proved that the SURF algorithm outperforms the other two feature descriptors with an accuracy measure of -0.0009 pixels, while SIFT with a cumulative signed distance of 0.0328 pixels also proved to be more accurate than MHCD with a cumulative signed distance of 0.0457 pixels. The findings affirmed the importance of choosing the right feature descriptor in the overall accuracy of an automatic image registration scheme.","PeriodicalId":502308,"journal":{"name":"Geodesy and cartography","volume":"5 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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