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Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs. [ABSTRACT FROM AUTHOR]

Mangroves are a crucial part of the coastal ecosystem; thus, precise and up-to-date monitoring is essential to guide regional policies and inform conservation strategies. This study investigates the capabilities of semi-automated remote sensing approaches within a Google Earth Engine framework for national-scale mangrove mapping in Thailand. Remote sensing data from 2018—10,000 data points acquired from Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM)—was used to train several machine learning models. The Gradient Tree Boost (GTB) proved to be the most reliable, with the least variation in validity (the lowest IQR) and the highest average Overall Accuracy of 96.75 ± 0.63 % compared to the others—96.64 ± 0.72 % for Random Forest (RF); 96.12 ± 0.80 %for Classification and Regression Trees (CART); and 95.43 ± 0.74 % for Support Vector Machines (SVM). Thus, the GTB was instrumental in mapping mangrove distribution with 10-m spatial resolution across Thailand from 2016 to 2022, the period in which the mangrove areas increased by 11 %, reflecting successful conservation efforts over the past decade. The developed framework establishes the foundation for semi-automated mangrove mapping that can be developed for other geographical contexts. [ABSTRACT FROM AUTHOR]

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the 'r.learn.train' module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data. [ABSTRACT FROM AUTHOR]

Gábor Mezősi was born on July 18, 1952, in Szeged and spent most of his life there. He graduated as a teacher in Mathematics and Geography in 1975. With a German state scholarship, he gained professional experience and, observing their education system, reformed the Hungarian geographer training. In 1991, he won the Academy Award, and as head of department, he helped to introduce geoinformatics training. In 1993, he became Doctor of the Hungarian Academy of Sciences in geoinformatics and landscape geography. After the turn of the millennium, he played an important role as dean of the Faculty of Science of the University of Szeged. In 2021, he was awarded Professor Emeritus of the University of Szeged and in 2023 the Civil Section of the Middle Cross of the Order of Merit of Hungary. In addition to his scientific work, Gábor Mezősi did a lot to reform geographer training as well. The conversation with the Professor gives an insight into the most important stages and experiences of his life.

The study was conducted with the aim of improving the environmental monitoring of soils in the Rivne urban ecosystem using geo-information technologies, followed by visualization of the results based on indicators of total and accumulated pollution with heavy metals (HM). It was found that for the gross form of HM at 54% of soil sampling points, the soils are classified as "Significantly contaminated", 28% of the soils are "Moderately contaminated", and 18% of the soils belong to the category "No contamination" with HM. According to PLI values of the mobile form of HM, 62% of the soils are of the "No contamination" category. The soils in the "No contamination" category are predominantly of insular localization and are located near water bodies and on the outskirts of the city. The "Moderately contaminated" soils are identified as 26%, "Substantially contaminated" and "Significantly contaminated" - 4% and 8%, respectively. The "Significantly contaminated" category characterizes the soil cover near large enterprises, near main highways and railways. For visualization, interpolated surfaces of the distribution of total and accumulated contamination with HM were constructed. [ABSTRACT FROM AUTHOR]

تعد دراسة الخصائص البيدوجغرافية وخاصة معدلات تعرية التربة بأحواض التصريف من الموضوعات المهمة التي يجب الأخذ بها في ظل اتجاه الدولة لزيادة مساحة الأراضي المستصلحة، كما أن دراسة معدلات التعرية وفق النماذج الرياضية يمكن من خلالها تقدير درجات الخطورة التي قد تتعرض لها تلك الأحواض، والتي تؤثر على استدامة الموارد والمحافظة البيئية عليها. واعتمدت الدراسة منهج التحليل الكمي، وتقنيات الجيوانفورماتيكس مع تطبيق الأسلوب الكمي بنموذج RUSLE لتقدير فقدان التربة بسبب التعرية المائية بحوض وادي سدر، والتي تتأثر بالعديد من العوامل والعمليات، مثل: الخصائص الجيولوجية، والتضاريسية، والمناخية، والهيدرولوجية. ويمثل عامل قابلية التربة للتعرية المائية من أهم العوامل في تقدير حجم التعرية لما له من تأثير يعكس خصائص التربة ومدى قابليتها لفقدان أجزاء منها من خلال نقل الرواسب والعمليات الهيدروليكية المرتبطة بها، حيث يرتبط بنسيج ونفاذية التربة ومحتواها من المادة العضوية. وبناءً عليه تم تصنيف المناطق المعرضة لتعرية التربة المحتملة سنويًا بحوض وادي سدر، وقد ساعد التحقق الميداني من التأكد من ذلك، ورصد بعض الدلائل والظاهرات البيدوجغرافية على عمليات التعرية المائية للتربة، مثل: رواسب القاع، والتشققات الطينية، والمصاطب، وقد توصلت الدراسة إلى وضع خطة شاملة ومتكاملة للتنمية المستدامة بحوض وادي سدر وخاصة التنمية الزراعية وتنمية محاجر الرمال والزلط، واقتراح العديد من الوسائل للحماية من أخطار التعرية المائية عن طريق إنشاء السدود والخزانات والبرابخ.AbstractThe study of pedogeographic characteristics, particularly soil erosion rates in drainage basins, is one of the significant topics that must be taken into account in light of the state’s tendency to increase the area of reclaimed lands. Besides, studying erosion rates according to mathematical models can be used to estimate the degrees of danger to which these basins may be exposed, which affects the sustainability of resources and their environmental preservation. The study adopts the quantitative analysis approach and geoinformatics techniques with the application of the quantitative method using the RUSLEmodel to estimate soil loss due to water erosion in Wadi Sidr Basin, which is affected by many factors and processes such as: geological, topographic, climatic, and hydrological characteristics. The factor of soil susceptibility to water erosion represents one of the most significant factors in estimating the extent of erosion due to its impact that reflects the characteristics of the soil and the extent of its susceptibility to losing parts through the transfer of sediments and the hydraulic processes associated with them, as it is linked to the texture and permeability of the soil as well as its organic content. Accordingly, the areas exposed to potential soil erosion annually in Wadi Sidr Basin were classified. Field investigation helped confirm this, and monitored some pedogeographic evidence and phenomena of water erosion processes such as: bottom sediments, mud cracks, and terraces. The study concludes with developing a comprehensive and integrated plan for sustainable development in Wadi Sidr Basin, particularly agricultural development and the development of sand and gravel quarries, and proposing several means to protection against the dangers of water erosion by constructing dams, reservoirs and culverts.

Sectoral real estate state management is one of the complex types of management. It includes individual objects management technologies, corporate management technologies considering the interests of the industry and state management technologies considering the state interests and inter-sectoral balance. Sectoral university real estate state management is even more complex. It uses not commercial effect as for commercial enterprises, but social effect and the criterion of resource provision of the country’s industries as the main efficiency criterion. Indirect indicators rather than direct ones consider this effect. The education system is subsidized, so the direct assessment of its efficiency is conditional. Higher education organizations real estate management is one of the important components of management. Real estate management is related to spatial economics and geoinformatics. The original development of a decision support system for educational organizations real estate management has been described. The system was developed under the assignment of the Ministry of Science and Higher Education of the Russian Federation. It uses geographic information systems as an additional component. It has been shown that it is necessary to apply geodata for real estate management. The basic principles of university real estate management with the help of this system have been described. Spatial information for sectoral real estate management should be combined with environmental, social, and economic information.

In successful geoinformatics education, students' active role in the learning process, e.g. through applying self-assessment, show an increasing interest but the evidence of benefits and challenges of self-assessment are sporadic. In this article, we examine the usefulness of an online self-assessment tool developed for geoinformatics education. We gathered data in two Finnish universities on five courses (n = 11–73 students/course) between 2019 and 2021. We examined 1) how the students' self-assessed knowledge and understanding in geoinformatics subject topics changed during a course, 2) how the competencies at the end of a course changed between the years in different courses, and 3) what was the perceived usefulness of the self-assessment approach among the students. The results indicate support for the implementation of self-assessment, both as a formative and summative assessment. However, it is crucial to ensure that the students understand the contents of the self-assessment subject topics. To increase students' motivation to take a self-assessment, it is crucial that the teacher actively highlights how it supports their studying and learning. As the teachers of the examined courses, we discuss the benefits and challenges of the self-assessment approach and the applied tool for the future development of geoinformatics education. [ABSTRACT FROM AUTHOR]

The study presents the advantages of, and possible uses for, Orange software for data mining in combination with processing spatial data by ArcGIS Pro software in education. To present suitability of Orange software in education, the scientific method of Physics of Notation by D. Moody is used to evaluate the Orange software's visual vocabulary. All nine principles are applied in the presented evaluation. As a result, a high level of effective cognition of the Orange visual vocabulary is proven by this method. Namely, the semantic transparency of visual vocabulary, thanks the explicit inner icons, is semantically immediate. Also, principle of dual coding is used properly by automatic text labels of graphical symbols with the opportunity to rename labels. Renaming is also a way to ensure the partial overloading of symbols found by the first principle of semiotic clarity. The principle of cognitive interaction is partially fulfilled by automatically reorganizing connector lines between symbols to reduce the crossing of lines. A high level of effective cognition is beneficial for students. The evaluation of the visual notation of Orange software is presented to inform teachers and the geoinformatics community of the highly effective cognitive aspects of Orange software. The two practical lectures of processing in Orange and ArcGIS Pro software are shown to the teachers and students of geoinformatics community as examples of machine learning tasks. They are cluster analyses carried out with the density‐based spatial clustering of applications with noise method, first for the location of cafés in Olomouc town and the second example concerns finding similar European towns based on their land use arrangement, using the neural network and following hierarchical clustering. Both examples could provide inspiration for the geoinformatics community to adopt Orange data mining software. [ABSTRACT FROM AUTHOR]

In the context of ecological civilization construction in China, interregional ancient trees are important natural and cultural resources, and it is vital that they are effectively protected and utilized according to their spatial distribution and regional conditions in order to provide a balance between local economic development and the ecological environment. This article analyzes the spatial heterogeneity of 26,842 ancient trees and explores the underlying natural and human factors by using geoinformatics–based techniques (i.e., the nearest neighbor index, kernel density, spatial autocorrelation, and the geographically weighted regression model) in Henan Province, Central China. The research findings conclude that: (1) The interregional ancient trees show positive spatial autocorrelation, and the aggregation effect in Western Henan is the most significant. (2) The growing environment and elevation exert a more obvious influence on the spatial pattern of interregional ancient trees. (3) The main influencing factors per region are different, for example, historical culture in Western Henan, water source and elevation in Southern Henan, and the growing environment in Eastern Henan, while ancient trees in Northern Henan have a weak relationship with each factor. This research facilitates political strategy making for sustainable development and the protection of interregional ancient trees. [ABSTRACT FROM AUTHOR]

Desertification processes in arid, semi-arid, and dry sub-humid conditions have been enhanced in recent decades. The geospatial database and associated satellite data can be effectively employed for regional planning to address desertification and land degradation. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model has been used to map environmentally sensitive areas due to desertification in the Satara and Sangli districts of Maharashtra, India. This was achieved by combining Landsat-8 multispectral data, Census data, soil data, and climatic variables like temperature, rainfall, and evapotranspiration. The algorithm of MEDALUS is the geometric mean of four indicators, namely soil quality index (SQI), climate quality index (CQI), vegetation quality index (VQI), and socio-economic quality index (SEQI). The findings indicated that the majority of the study area comes under the potential category of desertification (60.32%) followed by fragile (27.87%) and critical (11.81%). Areas with a high propensity for desertification were found over the low to very low climatic quality and moderate to high soil quality including lower socio-economic quality. The lower socio-economic quality is mainly due to high to very high population density (>100 people/km2), low to moderate illiteracy rate (<16%), and low to moderate work participation rate (<50%) that incentivize unsustainable land use practices. The study provides a valuable tool for understanding and managing natural resources. It offers a detailed analysis of the environmental sensitivity of the study area, taking into account various factors like land use, vegetation cover, slope, and soil erosion potential. The developed comprehensive map of the area helps in identifying the most sensitive regions and developing appropriate conservation strategies. The information obtained from the study can be utilized to develop and implement successful measures to prevent or alleviate desertification, which is crucial for sustaining the health of ecosystems and the welfare of local residents. [ABSTRACT FROM AUTHOR]

The general condition of urban watercourses is inextricably linked to ecological balance and public health (physical and mental well-being). Given the vulnerability of these systems to climate change and human pollution, this work aims to demonstrate the effective use of environmental indicators to: a) rapidly assess soil and water conditions near urban streams and b) highlight the importance of geoinformatics and earth observation supported by ground-based techniques. There is a great need for new technology and methods for spatial and temporal monitoring and further quantification of environmental quality in urban streams and the land surrounding them to sensitize policymakers and the public to the environmental degradation of these exceptional habitats and to further protect people's mental health. [ABSTRACT FROM AUTHOR]

The Bátaapáti National Radioactive Waste Repository was created using mining methods in a granitoid formation. Changes in the receiving rock body are monitored by the seismo-acoustic measurement network that has been in operation since the cavity was designed. The present work builds on geoinformatics methods and presents automated codes written for a Geographical Information System, which filters out incorrect spatial positions from a large mass of seismoacoustic point sources. The incorrectly measured points show the greatest inaccuracy vertically. The most intense emission source points with appropriate positions can be detected in the areas between the side walls of the adjacent storage chambers and near the fracture system detected along the trench end of the chambers. Based on the distance between the point sources and the storage chambers, it was also possible to refer to the extent of the damaged/disturbed rock zone around the excavation, which means a 7-meter rock belt.

Making paleoecological data accessible, usable, and useful to a global and diverse community of researchers, educators, and students across Earth sciences is an essential yet challenging task. Multiple data access and discovery tools must co-evolve with ever-changing user needs through an iterative, open-ecosystem software development approach. We employ a user-centered design study to evaluate one such ecosystem, the Neotoma Paleoecology Database, whose mission is to advance understanding of global-change processes by providing an expert-curated data resource. Neotoma contains over 11 million observations from 30,000 + datasets across 20,000 + sites, representing proxies of environmental and ecological conditions of the past. Neotoma comprises three interrelated software components – an interactive web mapping application, statistical programming package, and API – that provide different levels of functionality to different audiences. Although development efforts of these software components have involved the user community, Neotoma lacks a systematic evaluation process, which is important for informing the gap between perceived potential and actual utility. We address this knowledge gap using scenario-based design exercises and a usability/utility assessment. Major contributions include actionable insights for enhancing Neotoma's user experience and a user-centered design model that can be adapted to advance the development of other Earth and environmental science software ecosystems.

Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world's largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta. [ABSTRACT FROM AUTHOR]

Floods, as natural disasters, impose significant human and financial burdens, necessitating stringent mitigation measures. The recurrent annual incidence of floods precipitates considerable economic setbacks and tragic human casualties. In the realm of disaster management, flood susceptibility mapping has evolved into an indispensable instrument for preemptive intervention. In recent years, the amalgamation of machine learning (ML) methodologies and geographic information systems (GIS) has demonstrated remarkable promise in the realm of flood susceptibility mapping. Nonetheless, the inherent limitations of standalone ML models have constrained their predictive efficacy. Several shortcomings are evident in prior research. These include the failure to utilize contemporary ensemble approaches capable of enhancing performance and the limited exploration of diverse classifier combinations, which are instrumental in augmenting reliability. Simultaneously, there is an absence of current and up-to-date flood susceptibility maps on recent floods within the study area. Hence, this study endeavors to enhance the precision of flood susceptibility mapping, within the Haraz-Neka River basin across Mazandaran province, by harnessing an ensemble of ML models. The research methodology encompassed several pivotal phases. Initially, data about 240 flood sites were meticulously compiled. Subsequently, 70% of this dataset was allocated for training and cartographic elucidations, whereas the remaining 30%, selected at random, served to validate the resultant maps. The analytical framework incorporated a spectrum of influential parameters, encompassing Elevation, Slope, Aspect, Rainfall, land use, Vegetation Differentiation Index (NDVI), Soil Hydrology Groups, Proximity to the River, Distance from Landslides, Topographic Wetness Index (TWI), Stream Power Index (SPI), and Sediment Transport Index (STI) for spatial modeling. The results undeniably highlight the superior performance of the ensemble model compared to its individual counterparts. Validation exercises, leveraging historical flood data, prominently endorsed the AdaBoost algorithm integrated with the Decision Tree classifier as the most efficacious. Garnering an Area Under ROC curve surpassing 0.96, accompanied by an accuracy of 0.93%, a sensitivity of 0.95%, and a specificity of 0.92%, this amalgamation substantiates its prowess. The proposed framework stands poised to empower decision-makers in identifying vulnerable regions and devising efficacious flood risk mitigation strategies. [ABSTRACT FROM AUTHOR]

Geospatial technologies enable accurate and detailed documentation of cultural heritage sites. They allow for the creation of digital representations of these sites that can be shared with interested audiences. Given the above, this paper presents the possibility of using archival data to reconstruct the topography of two German labor camps, Dyhernfurth I and II, which operated during World War II. To perform this task, multi-source archival data was obtained and interpreted. These data came from various sources: archives, historical institutes, and museums. The results of the study were presented to the staff of the Gross-Rosen Museum, who anticipated the possibility of using the proposed tools in the management of other labor camps. The proposed methodology can be replicated at other locations and easily implemented by other martyrdom museums involved in the preservation of cultural heritage. [ABSTRACT FROM AUTHOR]

Mass movements in the form of rock, debris, and earth are affecting mountainous areas worldwide. The present research is dedicated to identifying areas at risk of landslides along National Highway 37, a vital corridor that links the states of Manipur and Assam. To create a comprehensive database of regions impacted by landslides, a combination of on-site field surveys, satellite imagery, and Google Earth was employed. Categorization of the road stretch polygon has been done into five vulnerable zones based on “frequency ratio, Shannon’s entropy, and weight of evidence” models. The AUC and landslide density methods have been utilised in the process of validating these models. The overall maximum accuracy of 86.95% has been observed for frequency ratio model which is more than weight of evidence (84.98%) and Shannon’s entropy (80.47%) models. The existing model based on information value method was 0.81 showing 81% overall prediction rate. This map will contribute for upcoming developments to facilitate public in terms of safe transportation and infrastructure. [ABSTRACT FROM AUTHOR]

Landslides are among the most critical geo-environmental disasters for both humans and the environment. This study intended to create a landslide susceptibility map (LSM) for the Akre District of Kurdistan Region, Northern Iraq. In this paper, 15 landslide causative criteria including elevation, slope, curvature, aspect, topographic wetness index, topographic roughness index, steam power index, lithology, lineament density, soil types, land use/cover, normalized difference vegetation index, distance to roads, rainfall, and distance to streams were analysed. For this purpose, frequency ratio (FR), analytic hierarchy process (AHP), and ensemble FR-AHP models based on geoinformatics techniques were applied. The LSMs were then assigned into five categories based on their susceptibility levels: very low, low, medium, high, and very high. The results showed that the high and very high landslide susceptibility categories for the FR, AHP, and hybrid FR-AHP models were 27.75% (732,01 km2), 28.44% (750,36 km2), and 28.84% (761 km2), respectively. The results showed that the majority of historical landslide incidents occurred in mountainous terrain in the northern parts of the study area, which are classified as high and very high susceptibility zones. The predicted rate curves for the FR, AHP, and hybrid FR-AHP models had areas under curve of the receiver operating characteristic curve (AUC-ROC) values of 93.4%, 89.3%, and 93.8%, respectively, which indicate that the ensemble FR-AHP model provides more reliable and accurate results for LSM. The LSM generated via the hybrid FR-AHP model can be utilised by local authorities, managers, and decision-makers in further land use/cove planning to mitigate the devastating influences of landslides in the area. [ABSTRACT FROM AUTHOR]

With strong technology improvement, especially GNSS, coordinate transformation has become an essential tool in everyday practice. For this reason, it is beneficial to have a good understanding of the mathematical process of coordinate transformation, as well as the method for calculating transformation parameters. The purpose of this article is to provide a detailed explanation of the coordinate transformation procedure and the calculation of transformation parameters for seven transformation models: 3-parameter, 5-parameter standard and abridged, 7-parameter, 8-parameter, 9-parameter, and 12-parameter. For each transformation model, a basic transformation equation, a procedure for calculating transformation parameters, inverse expression for reversible transformation, as well as characteristics of each model are provided. As an addition, for each model, a numerical example of calculating transformation parameters for the territory of the Republic of Croatia is provided. Additionally, for the example of the 7-parameter transformation, importance of reversible transformation, rotation convention, as well as the form of the rotation matrix used during transformation are demonstrated. [ABSTRACT FROM AUTHOR]

Nonthaburi, a suburban province adjacent to the Bangkok Metropolis, has experienced a reduction in green spaces due to urban expansion. This study quantified Nonthaburi’s green space through visual interpretation of land use and land cover (LULC) using THEOS and Sentinel-2. Areas of green space were extracted using remote sensing indices and pixel-based classification based on THEOS. The extracted green area was then integrated with the existing LULC patterns to align with the green space characteristic established by Thailand’s Office of Natural Resource and Environmental Policy and Planning. This includes public services, functional utility, median strips, community economics, fallow, and natural green space. The analysis of green space management and planning utilized the Urban Green Space Index (UGSI), Per Capita Green Space (PCGS), and accessibility to public green space. The results revealed that Nonthaburi comprises a green space area of 465.29 km2 or 73.06%, exhibiting a higher prevalence within its western region while displaying a relatively lower extent in the urban zone adjacent to the Bangkok Metropolis. The per capita green space is 367.71 m2 but decreases to 255.82 m2 when accounting for the latent population, meaning it still meets the World Health Organization (WHO) criteria. Currently, only six parks (single and clusters) meet the criteria for public green space. Additionally, both fallow and median strip green spaces (at road interchanges) need to be considered for their potential use in new public service. Furthermore, very high-resolution imagery from unmanned aerial vehicles (UAVs) should be used for green space planning by the organization.

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

Making paleoecological data accessible, usable, and useful to a global and diverse community of researchers, educators, and students across Earth sciences is an essential yet challenging task. Multiple data access and discovery tools must co-evolve with ever-changing user needs through an iterative, open-ecosystem software development approach. We employ a user-centered design study to evaluate one such ecosystem, the Neotoma Paleoecology Database, whose mission is to advance understanding of global-change processes by providing an expertcurated data resource. Neotoma contains over 11 million observations from 30,000 + datasets across 20,000 + sites, representing proxies of environmental and ecological conditions of the past. Neotoma comprises three interrelated software components -- an interactive web mapping application, statistical programming package, and API -- that provide different levels of functionality to different audiences. Although development efforts of these software components have involved the user community, Neotoma lacks a systematic evaluation process, which is important for informing the gap between perceived potential and actual utility. We address this knowledge gap using scenario-based design exercises and a usability/utility assessment. Major contributions include actionable insights for enhancing Neotoma's user experience and a user-centered design model that can be adapted to advance the development of other Earth and environmental science software ecosystems. [ABSTRACT FROM AUTHOR]

A multi-disciplinary method has been applied for the subsurface mapping of a diesel (refined petroleum product) spillage in the Apapa area of Lagos, Nigeria. The methods include geological, geophysical, hydrogeological, and geoinformatics approaches. Apapa is located within the eastern sector of the Dahomey Basin, Nigeria. Twelve profiles were probed, adopting a 2D Wenner configuration. Three 3D resistivity images were produced from the 2D profiles using ZONDRES 3D and SURFER 16. Additionally, six boreholes were drilled for lithological information and the determination of groundwater flow directions of the area. The study area is characterized by loose, fine to coarse grained sand underlain by peat. The sands were observed to be partly contaminated by diesel (spilled diesel). The 3D resistivity images revealed that the area is composed of 3 geoelectric layers; an organic debris layer (<15 Ωm), overlain by a clean sandy layer (15-54 Ωm), as well as a contaminated sand layer (>55 Ωm). The results showed that higher resistivity values were obtained at shallow depths ≤3 m which depicts the diesel contaminated sand. The 3D model also showed that the magnitude of contamination decreases from the southern part (close to the source of contamination) towards the north of the area. These results are in tandem with the principal flow direction of water which is from south to north, confirming that the pollution source is from the southern part of the area. The combination of the resistivity, groundwater flow direction, and lithological information, therefore, unraveled the source, direction, and extent of contamination in the study area for a successful remediation plan. [ABSTRACT FROM AUTHOR]

A significant amount of energy is released from sub-surface during an earthquake which is caused by seismotectonic activity. The occurrences of earthquake and its distribution pattern are necessary to correlate with concentrated and deviated energy in the sub-surface for better understanding the seismotectonic activity. Hence, the main aim of the present research is to develop hierarchical sub-surface energy anomaly index and its significant on seismotectonic activity by geospatial analysis of the seismic cluster zone and active tectonic signature through geoinformatics for Lower Tista sub-basin, India. In the present research, sub-surface energy has been quantified and threshold energy has been computed to estimate sub-surface energy anomaly in various hierarchical order. Also, the hierarchical sub-surface energy anomaly index has been computed and compared with the seismic cluster zone, seismotectonic activity, and geological formation. The analysis shows that the north and central part of the study region along Rathong chhu, Kalet Khola and Great rangit micro basin denotes as HSEAI Class V which represents low intensity earthquake with clustered concentration and includes in least to weak shaking anomaly zone. This zone is located in the fringe of greater and lesser Himalayan sequence along Main central thrust and inside Ramgarh thrust of lesser Himalayan sequence. It suggests higher energy concentrated and released through micro earthquakes along the active thrust with less energy anomaly. The southern part of the study region in quaternary surface adjacent to Himalayan foothills along Lish, Gish, Chel, and Dharala micro basin denotes as HSEAI Class II which are not resemble with any recent earthquakes and lies on least intensity sub-surface energy anomaly zone. This region intersects the frontal thrust and adjoins with Gish fault and Tista lineament which is presently not in active even though higher energy is concentrated in the sub surface. It suggests the energy anomaly is less even though energy accumulated in the sub-surface which signifies the energy concentrated in the sub-surface but not yet released. It predicts the energy may release with the displacement of thrust in future with higher magnitudes. [ABSTRACT FROM AUTHOR]

The article discusses the impact of artificial intelligence (AI) on computer vision, natural language processing, and geoinformatics. It highlights the advancements in tasks such as text classification, named entity recognition, machine translation, and machine reading comprehension. The article also explores the use of logical reasoning and graph structures to enhance AI models' performance in natural language processing tasks. Additionally, it discusses the application of AI in text classification, image classification, medical image processing, and sports analysis. The article concludes by emphasizing the ongoing research and development in AI and its potential for solving complex problems in various industries. [Extracted from the article]

Using of Geo-Informatics and Google Earth Engine (GEE) technology provide the smart system to access the land information that is very important to achieve the country's goals on Climate change. The purpose of this research were to develop the land use change monitoring system by integration of Geo-Informatics and the GEE technology and to create the spatial GHG emissions and removals datasets in Chiang-Mai. Four periods (2019-2021) of Landsat-8 and Sentinel satellite imagery were used to interpret land use and land use changes. Main types of land use change based on amount of biomass were analyzed: forest land, cropland, grassland, wetland, settlement and others land. Cropland are divided into 4 sub-groups base on amount of biomass: rice fields, rubber, oil palm and other agriculture. Then the datasets of land use change were used to assess the amount of greenhouse gas emissions and reduction using the calculation method referring to the IPCC Good Practice Guidance for LULUCF (GPC) and 2006 IPCC Guidelines for National Greenhouse Gas Inventory. The results of the study are summarized as follows: Chiang Mai Province abundant with forest areas accounted for 69.9 % of the total area. There is a national forest national park and many wildlife sanctuaries. According to the interpretation of satellite images, it was found that Chiang Mai has continued to expand urban areas and agricultural areas. Most of the land has been transformed from abandoned grasslands. It wasn't modified from the forest area as in the past. This is due to the measures of forest preservation and reforestation. As a result, GHG emissions are reduced. The analysis results show that when deducting the emission value from the amount of greenhouse gas storage, Chiang Mai Province has GHG storage of -967,349.16 tCO2 -1,308,790.00 tCO2 -1,277,804.35 tCO2 in 2019, 2020 and 2021 respectively. The results of this study are used as supporting information for the project to develop an approach to reduce GHG at the area base. This is another step forward in using geospatial technology to assess and manage carbon. This makes it possible to show the amount of GHG in the form of a map. Including the ability to identify emissions or reservoirs of GHG as well. The developed GHG monitoring system will benefit government agencies or the private sector to formulate more efficiently targets for reduction implementing projects. [ABSTRACT FROM AUTHOR]

Over the last three decades, Tehsil Mardan has gone through an incredible expansion of its built-up layer. This study explored the land use land cover changes in Tehsil Mardan from 1990 to 2021 along with population dynamics by applying geographic information systems and remote sensing techniques. Landsat satellite images for the years 1990, 1995, 2000, 2010, 2015, and 2021 were used for land use land cover classification. A maximum likelihood supervised algorithm and confusion matrix were applied for classification and accuracy assessment, respectively. The classification results outlined that there has been a substantial increase in the built-up layer from 37 km2 to 188 km2 and a significant decrease in bare land class from 437 km2 to 252 km2 from 1990 to 2021. The classification proces's overall accuracy ranged from 87.42% to 98.30%, and the Kappa Coefficient ranged from 0.82 to 0.97. Population dynamics were also studied in the present study, and it was found that the total population of Tehsil Mardan was 502,435, 864,017, and 1,403,002 in 1981, 1998, and 2017, respectively, and its population was further forecasted based on historical trends until 2027. Statistical analysis revealed a strong positive correlation (0.98) between the built-up layer and population and a significant negative correlation (−0.91) between population and bare land. Based on the findings of this study, policymakers should be able to better plan future land use and account for associated factors while keeping environmental threats and opportunities in mind. [ABSTRACT FROM AUTHOR]

This paper briefly presents the current influence of geoinformatics on geography teaching in the Hungarian secondary schools. First, the main characteristics of geography teaching at both levels (elementary and secondary) are described, then the skills and competences that geography should develop at this level and how geoinformatics can help develop them. This is followed by an overview the current presence of basic cartographic and geoinformatics concepts found in the Geography textbooks and some examples of Hungarian research on the subject. Finally, some ideas are proposed to increase the presence of geoinformatics in secondary geography teaching. [ABSTRACT FROM AUTHOR]

Based on an extensive survey of demand (‘needs’) and supply (existing study programs) a framework for transdisciplinary capacity building in Digital Earth concepts, methods and technologies was developed by the authors. In collaboration with universities from the partner countries Armenia and Kyrgyzstan project funding from the Austrian Development Cooperation was acquired to support implementation. Recognizing the need for an explicit spatial approach from disciplines reaching far beyond the traditional ‘geospatial’ subjects like Geography, Geoinformatics, Geodesy or Planning, the DEvision project addresses study programs like Economics, Ecology, Engineering or Journalism through a focused set of foundational modules implemented as a minor subject or certificate. This vision of a Digital Earth is aligned with general digital transformation in society and business, with implementation of sustainable development goals, and a live-long learning paradigm. Based on modern curriculum concepts and pedagogical concepts like active and blended learning, the DEvision initiative provides a set of five modules as teaching and learning resources. These are primarily set up as open educational resources (OER) supporting teachers, and not intended as self-learning media. Instructors within the respective study programs are expected to guide, mentor and monitor students through assigned online learning materials, or using these resources within existing syllabi and curricula. Overall DEvision follows several clearly defined principles: (i) blended learning based on online resources, (ii) instructor-led pathways aligned with students’ majors, (iii) a modern approach to GIS entirely based on cloud platforms, and (iv) orienting students towards a continuous professional development principle based on online communities. [ABSTRACT FROM AUTHOR]

This study delves into the patterns of urban expansion in Kabul, using Landsat and Sentinel satellite imagery as primary tools for analysis. We classified land use and land cover (LULC) into five distinct categories: water bodies, vegetation, barren land, barren rocky terrain, and buildings. The necessary data processing and analysis was conducted using ERDAS Imagine v.2015 and ArcGIS 10.8 software. Our main objective was to scrutinize changes in LULC across five discrete decades. Additionally, we traced the long-term evolution of built-up areas in Kabul from 1973 to 2020. The classified satellite images revealed significant changes across all categories. For instance, the area of built-up land reduced from 29.91% in 2013 to 23.84% in 2020, while barren land saw a decrease from 33.3% to 28.4% over the same period. Conversely, the proportion of barren rocky terrain exhibited an increase from 22.89% in 2013 to 29.97% in 2020. Minor yet notable shifts were observed in the categories of water bodies and vegetated land use. The percentage of water bodies shrank from 2.51% in 2003 to 1.30% in 2013, and the extent of vegetated land use showed a decline from 13.61% in 2003 to 12.6% in 2013. Our study unveiled evolving land use patterns over time, with specific periods recording an increase in barren land and a slight rise in vegetated areas. These findings underscored the dynamic transformation of Kabul’s urban landscape over the years, with significant implications for urban planning and sustainability.

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the ‘r.learn.train’ module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data.

In the context of ecological civilization construction in China, interregional ancient trees are important natural and cultural resources, and it is vital that they are effectively protected and utilized according to their spatial distribution and regional conditions in order to provide a balance between local economic development and the ecological environment. This article analyzes the spatial heterogeneity of 26,842 ancient trees and explores the underlying natural and human factors by using geoinformatics–based techniques (i.e., the nearest neighbor index, kernel density, spatial autocorrelation, and the geographically weighted regression model) in Henan Province, Central China. The research findings conclude that: (1) The interregional ancient trees show positive spatial autocorrelation, and the aggregation effect in Western Henan is the most significant. (2) The growing environment and elevation exert a more obvious influence on the spatial pattern of interregional ancient trees. (3) The main influencing factors per region are different, for example, historical culture in Western Henan, water source and elevation in Southern Henan, and the growing environment in Eastern Henan, while ancient trees in Northern Henan have a weak relationship with each factor. This research facilitates political strategy making for sustainable development and the protection of interregional ancient trees.

The article is devoted to the study of regional features of the steppe landscape structure of the Aktobe region. For a comprehensive analysis of the studied territory, a specialized landscape geoinfor-mation system of the regional level was formed using GIS technology. The spatial data base was based on the characteristics of the components of the natural environment of the studied territory, exposed to annual anthropogenic impact. For the purposes of landscape analysis of the territory, we have formed a specialized landscape geoinformation system at the regional level of GIS generalization, based on the ArcGIS10.4.1 software. By such a GIS system, we mean an interactive system capable of collecting, systematizing, storing, processing, evaluating, displaying and distributing data and acting as a means of obtaining on its basis new information and knowledge about space-time phenomena. The study consists of several stages: generalization of analytical data, creation of a landscape map using the tools of overlay layers of ArcGIS, where the layers characterize the landscape structure component-by-component, as well as SRTM satellite images were used to create a digital relief model of the Aktobe region. The obtained research results mapping the steppe landscapes of the Aktobe region serve as a basis for studying the ecological situation of the territory, as well as assessing the structure of the studied territory. [ABSTRACT FROM AUTHOR]

Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m2) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas. [ABSTRACT FROM AUTHOR]

Geoinformatics is spatial information based on characteristics and analysis regarding spatial data. Water is the main component of human life with clean water, especially drinking water found from various sources, including spring water. Water quality is a global standard as a chemical, physical, and biological parameter. The study aims to determine the potential spring water based on spatial analysis of water quality standards for potential use. The method used to determine the potential utilization of springs using geographic information system analysis. Data is based on the difference in height, slope, and distance from springs to settlements and discharge. Water quality samples are analyzed using mobile and laboratory tests. The result found that physical parameters from five locations, Sirah Citanggulun, Cikerebek, Cijati, Cijati 2, and Galumpit, have good quality to meet the utilization requirements, especially for water designation class. Chemically parameters, water quality, is relatively good based on several parameters such as pH, Nitrite, dissolved iron, and zinc, which do not exceed the required quality standard threshold. The biological parameters, five samples are contaminated by bacteria Escherichia coli and total coliform. Regarding the literature review, this research found that biological contaminants are unsuitable for drinking water, but it is still good to become another used. The research concluded that the spring water in a small village could become potential uses for drinking water based on the parameter of water quality for daily use, especially for drinking water, regarding biological parameters that spring water must be boiled to drink. [ABSTRACT FROM AUTHOR]

One of the most damaging natural disasters that occurs anywhere in the world is flooding. Flood hazard mapping is essential to determining the flood risk zone areas. In the present study, the analytical hierarchical process (AHP) and geographical information system (GIS) were used for flood hazard mapping of the lower Damanganga River basin from the Madhuban Dam to the Arabian Sea. The major factors affecting floods are hydrological characteristics, which are the combination of meteorological and basin characteristics. A total of seven parameters were selected, and a thematic map for each parameter was prepared. AHP was used to find the weights of the selected parameters using a questionnaire survey. Thematic maps were then integrated with weighted parameters obtained to create the flood hazard map. The abovementioned study's findings show that approximately 32% of the total area in the lower Damanganga River basin is prone to high floods, whereas 33% of the total area is prone to moderate floods. This study will be helpful to policy makers for reducing the risk of flooding. [ABSTRACT FROM AUTHOR]

This study delves into the patterns of urban expansion in Kabul, using Landsat and Sentinel satellite imagery as primary tools for analysis. We classified land use and land cover (LULC) into five distinct categories: water bodies, vegetation, barren land, barren rocky terrain, and buildings. The necessary data processing and analysis was conducted using ERDAS Imagine v.2015 and ArcGIS 10.8 software. Our main objective was to scrutinize changes in LULC across five discrete decades. Additionally, we traced the long-term evolution of built-up areas in Kabul from 1973 to 2020. The classified satellite images revealed significant changes across all categories. For instance, the area of built-up land reduced from 29.91% in 2013 to 23.84% in 2020, while barren land saw a decrease from 33.3% to 28.4% over the same period. Conversely, the proportion of barren rocky terrain exhibited an increase from 22.89% in 2013 to 29.97% in 2020. Minor yet notable shifts were observed in the categories of water bodies and vegetated land use. The percentage of water bodies shrank from 2.51% in 2003 to 1.30% in 2013, and the extent of vegetated land use showed a decline from 13.61% in 2003 to 12.6% in 2013. Our study unveiled evolving land use patterns over time, with specific periods recording an increase in barren land and a slight rise in vegetated areas. These findings underscored the dynamic transformation of Kabul's urban landscape over the years, with significant implications for urban planning and sustainability. [ABSTRACT FROM AUTHOR]

Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world’s largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta.

Rational forest planning and management is the key to a forest’s systematic construction. It is beneficial to many aspects, such as the cultivation and preservation of a forest’s ecological resources, sustainability, forest fire prevention, and others. In recent years, some effective strategies and tactics for the planning and management of forests’ systematic construction have been established. Among them, the application of geoinformatics in forest planning and management (AGFPM) is one of the most effective and promising strategies. Therefore, it is necessary to conduct a comprehensive summary and analysis of the current situation. AGFPM has effectively applied in logging operations, forest road development, forest material transport, and forest fire prevention. An analysis of the research results in the past 20 years showed that decision support tools are the most used solutions to problems related to forest planning and management, especially the analytic hierarchy process (AHP). Light detection and ranging (LiDAR) is the second most popular method. With the development of geoinformatics, it will play an increasingly important role in forest planning and management in the future.

The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a deep learning neural network (DLNN) model are the benchmark models used to compare their performance with that of a 1D-CNN model. Remote sensing (RS) techniques are used to collect the necessary flood related data, whereas thirteen flash-flood-related variables were used as predictive variables, such as elevation, slope, plan curvature, profile curvature, topographic wetness index, lithology, silt content, sand content, clay content, distance to faults, and distance to river network. The Weight of Evidence method was applied to calculate the correlation among the flood-related variables and to assign a weight value to each variable class. Regression analysis and multi-collinearity analysis were used to assess collinearity among the flood-related variables, whereas the Shapley Additive explanations method was used to rank the features by importance. The evaluation process involved estimating the predictive ability of all models via classification accuracy, sensitivity, specificity, and area under the success and predictive rate curves (AUC). The outcomes of the analysis confirmed that the 1D-CNN provided a higher accuracy (0.924), followed by LR (0.904) and DLNN (0.899). Overall, 1D-CNNs can be useful tools for analyzing flood susceptibility using remote sensing data, with high accuracy predictions. [ABSTRACT FROM AUTHOR]

Block Kriging (a spatial interpolation method) and log10 transformation were compared for their effectiveness in reducing relative variance (coefficient of variance: CV) and estimate mean values in all harvested maize plants grown in three randomly taken field plots and for harvested plants after removing the "edge or margin" ones. The results showed that log10 transformation reduced CVs of all harvested original fresh weight (FW) plant data in the three plots from 35.6–41.6% (original data) to 6.0–7.5%, while the respective CVs due to Block Kriging were reduced to 14.5–19.9%. The back-log10-transformed means of all harvested FW plant data were reduced by 6.8–9.4%, while the respective reduction for plants excluding the margin ones was 1.3–8.3%. The Block Kriging means for all harvested FW plant data were reduced only by 0.3–0.4%, while the respective means of the harvested plants excluding margin ones were increased by 0.4–4.3%. These findings strongly suggest that Block Kriging should be preferred over the log10 transformation method (used so far by agroscientists) as it managed to effectively reduce variability in crop data and estimate missing values that provide more precise and reliable estimates of corn yield for farmers. [ABSTRACT FROM AUTHOR]

An approach that integrates the Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) techniques was used to determine the soil erosion vulnerability of a forested mountainous watershed. The spatial pattern of annual soil loss rate was obtained by integrating geo-environmental variables in a raster data format based geoinformatics methods and tools. Thematic layers including rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C), and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the Mitrovica city. The serial thematic map of annual soil erosion shows a maximum soil loss of 112.61 ton·ha-1·y-1 with a close relation to grass land areas, degraded forests and deciduous forests on the steep side-slopes (with high LS). The geographic age of the region shown by the hypsometric analysis was mature to old stage. The serial erosion maps compiled with the RUSLE model and GIS can serve as effective inputs in deriving strategies for land planning and management in terms of environment concerns. [ABSTRACT FROM AUTHOR]

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The Arctic zone of the Russian Federation is one of the most intensively developing regions of the country. Amongst the major domains of economic and industrial growth and improvement is transport infrastructure and particularly the railway network. This area is being exposed to negative factors of rapid climate change that can significantly affect and compromise this activity. Thus, it is vital to take them into account during design, construction, and operation of the railway infrastructure facilities. This work details the production of a digital atlas comprising the 1950–2021 dynamics of the main hydrometeorological parameters: air and soil temperature, precipitation, wind speed, air and soil humidity, and snow cover thickness. The maps are based on climatic data derived from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) reanalysis. In total there are 459, which are arranged into 7 chapters. The atlas geographically covers the western part of the Russian Arctic encompassing the regions of quite intensive transport development, which includes the construction of the Northern Latitudinal Railway. Original algorithms of geospatial data processing and their further representation as well as the maps compiled in GIS environment are discussed. Comprehensive analysis of climatic changes in the region of the Russian Arctic including detailed quantitative evaluation over 40 years is given. In the Discussion, we focus on those changes of the regional climate which, from our point of view, are the most significant for consideration by railway operators. The obtained results contribute to framing the theoretical basis of design, development, and sustainable operation of the railway infrastructure in the Arctic and facilitate the decision-making process. This is the first experience of building a specialized climatic cartographic product for the needs of the Russian railways, and to our knowledge the first atlas such as that in the world. In the future, the amassed experience may be transferred to other regions of the Russian Federation as well as similar regions in Canada, Sweden and Highland China that are also subject to significant climate change. [ABSTRACT FROM AUTHOR]

The topic of this scientific paper is the detection of traffic signs based on the data obtained using modern geodetic methods (LiDAR, mobile laser scanning), by using geospatial tools. In a total of 100 photos taken in an urban area in Germany, in a length of 500 m, 75 traffic signs were detected. Each sign is assigned a corresponding code from the catalog of German traffic signs, after what the photos are connected to the point cloud. Then the extraction of bounding boxes and central points of traffic signs was done. The visualization was done in two softwares -Bentley MicroStation and QGIS, which completed the detection process. The accuracy of traffic sign detection in photos is 83% (62/75). The extraction of bounding boxes and central points on the point cloud was performed with a high degree of accuracy (85%).

Abstract Background The study aim was to analyse how mental health services are used in different parts of the Kainuu region in Finland and whether travel time to primary health care services is associated with the use of different contact types (in-person visits, remote contacts, home visits). Methods The study population included adults who had used mental health services under primary health care (N = 7643) between 2015 and 2019. The travel times to the nearest health centre in a municipality were estimated as the population-weighted average drive time in postal code areas. The Kruskal–Wallis test and pairwise comparisons with Dunn-Bonferroni post hoc tests were used to assess the differences in mental health service use between health centre areas. A negative binomial regression was performed for the travel time categories using different contact types of mental health service use as outcomes. Models were adjusted for gender, age, number of mental health diseases and the nearest health centre in the municipality. Results Distance was negatively associated with mental health service use in health centre in-person visits and in home visits. In the adjusted models, there were 36% fewer in-person visits and 83% fewer home visits in distances further than 30 min, and 67% fewer home visits in a travel time distance of 15–30 min compared with 15 min travel time distance from a health centre. In the adjusted model, in remote contacts, the incidence rate ratios increased with distance, but the association was not statistically significant. Conclusions The present study revealed significant differences in mental health service use in relation to travel time and contact type, indicating possible problems in providing services to distant areas. Long travel times can pose a barrier, especially for home care and in-person visits. Remote contacts may partly compensate for the barrier effects of long travel times in mental health services. Especially with conditions that call for the continuation and regularity of care, enabling factors, such as travel time, may be important.