Articles containing geographic information are widely distributed and commonly used in daily life, frequently incorporating geographic visualizations as illustrations. However, the creation of such articles remains cumbersome, necessitating authors to switch between authoring text and illustrations, thereby disrupting immersive writing. Our interviews corroborated this observation and revealed the primary challenge in the traditional process stems from the low synchronization frequency between text and geographic visualizations during creation, coupled with weak visual links, forcing users to mentally maintain this synchronization and thereby increasing their cognitive burden. In response, we developed GeoAuthor, which facilitates the interactive creation of geographic articles by automatically synchronizing text creation with geographic visualizations with rich visual links. This bidirectional approach ensures that the written content and visual representations remain consistent and mutually informative throughout the creation process. Our evaluation demonstrated the efficacy of GeoAuthor, indicating its capacity to streamline the process of creating geographic articles.

Research on machine learning prediction of biochar yield based on “geographic three-dimensional information”

Visual experience is a primary channel through which the values of tangible cultural heritage are perceived and governed, making visual evaluation and management central to conservation and to Sustainable Development Goal (SDG) 11.4. However, practice remains fragmented across scales, and many statutory toolkits lag behind advances in geographic information systems (GIS)-based visibility analysis, 3D visualization, remote sensing, and perception-based evidence. We compile, code, and cross-analyze a multi-level corpus spanning 26 international instruments, 293 national items from 112 countries, and 867 World Heritage properties. Using a four-dimensional framework (values, typology, visual-evaluation methods, and visual-management strategies), we apply k-medoids clustering with multidimensional scaling (MDS) at the national level, mask-aware association mapping at the property level, and cross-level diagnostics. Across levels, practice converges on a technical-spatial regime. At the property level, GIS-based viewshed and visual sensitivity analysis, verified visuals and 3D visualization techniques, and GIS-based spatial-historical analysis form a near-universal methodological core and are most frequently translated into zoning and spatial regulation and height or massing controls. Participatory and perception- or experience-based methods remain sporadic. Value framings are dominated by Historic, Social and Political, and Aesthetic emphases, while Ecological and Scientific are comparatively marginal. Cross-level coherence is strongest where governance frameworks are mature, and portfolios are coherent; it weakens where portfolios are heterogeneous or in federated or lower-capacity settings. National portfolios cluster into four method-strategy regimes that explain characteristic object-method-strategy sequences. In response, we outline operational bridges including tiered standards for visibility and 3D evidence, deployable perception protocols, participation modules linked to Heritage Impact Assessment (HIA) or Visual Impact Assessment (VIA) triggers, and auditable communication packages. These are organized within a Global Peer Network aligned to portfolio archetypes and method-strategy regimes. The study contributes a reusable global dataset and map of visual-heritage practice and an integration framework that supports more transparent, comparable, and context-sensitive decisions across levels. • Mapped heritage visual practice across international, national, and site levels. • Used a harmonized 4D framework (type/scale, values, evaluation, management) to compare practice across levels. • Proposed an integration framework linking analytical and visualization techniques to routine governance, planning and design. • Proposed an integration framework linking evidence to zoning/design controls to strengthen visual heritage governance.

Textual spatial correlation quantifies the degree of association between knowledge described in text and specific geographic spaces. For example, the statement ‘the nutria is an invasive species’ is valid only in certain regions. In geographic information science, measuring textual spatial correlation is not only a fundamental prerequisite for spatiotemporal computing, but also a cornerstone for high-precision geo-artificial intelligence applications. To address the limitations of annotation dependency and low computational efficiency in the existing methods, this study proposed a Spatial Correlation Index and corresponding calculation method. This method fully leverages implicit spatial knowledge from a large-scale knowledge base and through a semantic-matching mechanism, enabling the efficient calculation of textual spatial correlation. The effectiveness of the method was evaluated based on the spatial correlation calculations of textual data and entities in a knowledge graph. Results demonstrate that the proposed method achieves performance comparable to few-shot prompted GPT-4.1 and DeepSeek-V3, while outperforming their zero-shot counterparts, with average F1-score improvements of 18.65% and 27.27%, respectively. Meanwhile, the proposed method reduces the average calculation time by 77.37% and 77.74%, respectively. This study achieved a breakthrough in efficiently measuring textual spatial correlations, thereby providing an essential technical foundation for spatial-related intelligent computing.

Consideration of social and environmental determinants of maternal and perinatal health in the geographic information systems era: a primer.

Remote sensing techniques typically capture flood extent at discrete time points but cannot directly estimate flood depth. Social media, as an emerging source of volunteered geographic information (VGI), provides real-time visual evidence of fine-grained flood severity. However, this potential remains underexplored. In this study, we propose Social-VFG, an interpretable framework for assessing road inundation using vehicle wheels observed in images. To avoid training annotation-intensive wheel segmentation models, wheel contours above floodwater are efficiently extracted by coupling a trained wheel detector with the Segment Anything Model (SAM). Based on these contours, an interpretable rule-based indicator is proposed to characterize wheel inundation status as water levels rise. To mitigate the indicator’s sensitivity to segmentation noise and scale variations, four complementary features are further integrated into a random forest classifier to improve the robustness of inundation classification. Using the 2023 Zhuozhou flood as a case study, road inundation risk maps derived from Weibo images show strong spatial consistency with flood extents extracted from GF-3 SAR imagery. Importantly, social media observations provide fine-grained information on flood severity and effectively compensate for spatial gaps caused by satellite imagery limitations. The resulting road inundation risk maps can support flood-aware route planning under extreme rainfall conditions.

• GIS-driven aerial remote sensing supports large-scale power distribution monitoring. • Satellite images enable detection of clandestine areas linked to energy theft. • Vegetation management can benefit from GIS-driven aerial remote sensing. • Automatic estimation of rooftop PV rated capacity to update utility database. Solutions enabled by recent technological advancements and the increased availability of free geospatial images and open-source tools have demonstrated the potential to enhance tasks in various areas; however, electric power delivery remains insufficiently explored. This paper presents a proof-of-concept study exploring novel integrations of satellite imagery and geographic information systems (GIS) data to support three key tasks of distribution utilities worldwide: identifying clandestine connections to the system (electricity theft), mapping vegetation encroachment that poses risks to the network, and detecting and estimating the installed capacity of rooftop photovoltaic systems for automatically feeding or updating the utility database. The solutions rely on open-source tools, including artificial intelligence, image processing, and color segmentation, and are validated using real data from a Brazilian utility. The results demonstrate that the integration of GIS-driven and image-based aerial remote sensing techniques offers scalable and cost-efficient alternatives to conventional inspection methods.

Land use/land cover (LULC) change is one of the most important indicators of environmental transformation and regional landscape dynamics. Monitoring these changes is essential for understanding the impacts of urbanization, agricultural activities, and land conversion processes on semi-arid environments. This study aims to investigate the spatio-temporal LULC dynamics of Yozgat Province, Türkiye, between 2000 and 2024 using multi-temporal Landsat satellite imagery and Geographic Information Systems (GIS)-based spatial analysis techniques. Multi-temporal Landsat-5 TM, Landsat-7 ETM+, and Landsat-8/9 OLI images with 30 m spatial resolution were obtained from the USGS Earth Explorer platform. Image preprocessing, clipping, classification and temporal change detection analyses were performed in ArcGIS/ArcMap 10.8. Four major land use classes were identified: agriculture, settlement, bare land and vegetation/water. Multi-temporal classification results were compared to quantify long-term land transformation patterns. The classification accuracy was evaluated using confusion matrix analysis, Overall accuracy and Kappa metrics. The results revealed significant changes in the spatial structure of Yozgat Province over the 24-year period. Settlement areas exhibited the most substantial increase, rising by 114.38%, indicating strong urban growth and infrastructure expansion. In contrast, bare land decreased by 54.13%, suggesting progressive conversion into agricultural and built-up surfaces. Agricultural land remained relatively stable with a moderate increase of 9.08%, while vegetation/water areas increased by 67.72%. The most pronounced transformation periods were identified between 2006–2012 and 2012–2018, during which major land conversion processes occurred. The findings demonstrate that urban expansion and the reduction of bare land are the dominant drivers of landscape transformation in Yozgat Province. This study provides valuable scientific evidence for sustainable land use planning, regional environmental management, and future GIS-based monitoring studies in semi-arid regions of Central Anatolia. The results demonstrate that remote sensing and GIS techniques provide reliable tools for long-term monitoring of LULC dynamics in semi-arid environments.

Digital service innovation for climate-resilient urban pavements: Decision-as-a-service linking UHI-aware ML to environmental-economic performance.

Sustainable utilization of wild medicinal plant resources requires reproducible approaches for locating and managing natural populations. In Japan, exploration and resource planning for wild medicinal plants rely heavily on expert knowledge, and reproducible approaches remain scarce. We developed a species distribution model (SDM) for Uncaria rhynchophylla (Rubiaceae), a botanical source of the crude drug Uncaria Hook used in Kampo medicine, to estimate its potential distribution and identify major environmental correlates in the Kyushu region, southern Japan. Using 122 occurrence records collected from 2021 to 2023 and nine environmental predictors, MaxEnt models were trained with background points and bootstrap replicates. Because mean minimum winter temperature in January and mean maximum summer temperature in August were highly correlated, three candidate models were compared using the small-sample corrected Akaike information criterion (AICc), test omission rates, and test area under the receiver operating characteristic curve (AUC). The model including winter minimum temperature was best supported (test AUC = 0.82; test omission rate = 24%), whereas adding summer maximum temperature provided limited improvement (ΔAICc = + 75.75). Variable importance and jackknife tests ranked winter minimum temperature as the most influential predictor, followed by slope angle and distance to the nearest rivers. The suitable area, defined using the maximum training sensitivity plus specificity (MTSS) threshold, covered 10,595km² (25.9% of the terrestrial area) and formed continuous zones across hilly and low-montane regions. Targeted surveys in high-suitability areas confirmed three additional sites in the Kirishima Mountains. Overall, integrating SDM and geographic information systems provides a reproducible, data-driven, decision-support framework for the exploration, planning of sustainable harvesting, and resource management of wild medicinal plants.

Agricultural land transition in rapidly urbanizing coastal regions poses significant challenges for sustainable land use planning and long-term food security. This study examines the driving forces behind agricultural land conversion in Cilegon City, Banten Province, Indonesia as one of Southeast Asia's most industrialized coastal cities by integrating Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) with structured local knowledge elicitation. Land cover classification was performed using Random Forest machine learning applied to multi-temporal Landsat imagery (2011 and 2023), revealing substantial encroachment of non-agricultural land uses. Through pairwise comparison interviews with six domain experts, AHP weighting assigned the highest influence to rainfall (18%), soil quality (15%), and road accessibility (14%) as transition drivers. The resulting transitional suitability map, validated against observed land cover change, achieved an overall accuracy of 88.70% and a Kappa coefficient of 0.86, demonstrating the model's strong predictive capacity. The findings underscore that environmental, infrastructural, and socio-economic factors collectively govern land conversion dynamics. This study contributes a replicable, participatory spatial framework that bridges objective geospatial data with community-embedded knowledge, supporting more inclusive, evidence-based urban planning and agricultural land management in fast-growing coastal cities. Keywords: analytical hierarchy process, coastal city, land use change, local knowledge, remote sensing.

This study aimed to analyze soil erosion rates, erosion hazard levels, and to identify appropriate land conservation measures in the upstream area of the Unda Watershed using a geospatial information approach. The study employed the Universal Soil Loss Equation (USLE) method integrated with Geographic Information Systems (GIS). The analysis was conducted by combining rainfall data, soil type, land use, slope gradient, and land management factors to estimate soil erosion and determine erosion hazard levels. The results showed that soil erosion rates in the upstream area of the Unda Watershed varied from low to very high categories. The highest erosion rate ranged from 483.36 to 11,499.13 tons ha⁻¹ year⁻¹, covering an area of 1,469.98 ha (14.28% of the watershed area), while the lowest erosion rate ranged from 0 to 14.27 tons ha⁻¹ year⁻¹, covering 4,222.66 ha (41.02% of the watershed area). Based on the erosion hazard level (EHL) classification, different land conservation measures were recommended. Areas with Very Slight EHL require maintenance of land conditions through litter return, crop rotation, and simple ground cover. Slight EHL areas are recommended to apply contour planting, mulching, and cover crops. Moderate EHL areas require simple terracing, strip cropping, and a combination of vegetative and mechanical conservation techniques. Severe EHL areas should implement intensive terracing, reforestation with terrace-strengthening vegetation, and drainage channels. Meanwhile, Very Severe EHL areas require total land rehabilitation, permanent revegetation, and designation as protected areas. The integration of the USLE model and GIS proved effective in identifying spatial patterns of erosion risk and providing appropriate land conservation recommendations. Therefore, this approach can be used as a valuable tool for sustainable watershed management and soil conservation planning in the upstream area of the Unda Watershed.

The state of the road infrastructure in Iraq has severely declined due to the long-term conflict, the instability of the institutions, and the lack of investments, thus interrupting the logistical processes and the development of the country. This study examines the main barriers to the rebuilding of the road system in Iraq and proposes a coherent framework that would combine project-management and logistical paradigms in order to reduce these obstacles. Using a comparative approach to determine international best practices in road-infrastructure delivery, the paper evaluates the appropriateness of the PMBOK, PRINCE2, and Agile approaches in the post-conflict context in Iraq. Such frameworks are assessed together with the enabling technologies, such as Geographic Information Systems (GIS) and Artificial Intelligence (AI), and participatory models of stakeholder-engagement. The research suggests a hybrid model of logistics aligned strategy to fill the identified gaps, in which localisation of PMBOK, PRINCE2, and Agile solutions is the key, with material, information, financial, and human flows optimisation provided by GIS/AI decision support. The approach enhances more transparency, efficiency, and sustainability in the governance of infrastructure. The value of the research is twofold: (i) the combining of project-management stage-gates with the transport-logistics flow control in a post-conflict setting, (ii) the flow based blueprint of delivering, and maintaining the Iraqi road assets.

ABSTRACT Heavy seasonal rainfall in Eritrea generates substantial runoff, accelerating topsoil loss, particularly on deforested and poorly managed slopes. This study evaluates and maps the soil erosion risk in the Tsmieti catchment, within Nuès Zoba (sub-region) of Adi Quala, using the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS). The results indicated that annual soil loss ranges from 0 to 78 t ha−1 yr−1, with a mean of 13.56 t ha−1 yr−1. Approximately 34% of the catchment exceeds the tolerable erosion limit (11 t ha−1 yr−1). High (11–17 t ha−1 yr−1), very high (17–36 t ha−1 yr−1), and extreme (>36 t ha−1 yr−1) erosion classes account for 9%, 15%, and 10% of the area, respectively. Cropland constitutes the largest area subjected to elevated erosion rates, accounting for 17.85%. Class-wise, grazing and degraded bare lands are facing erosion rates exceeding the tolerable limit in over half of their respective areas. Slope gradient exerts a dominant influence on soil erosion rates, whereas rainfall exhibits no clear spatial pattern, suggesting localized spatial variability. The results provide an erosion risk map for prioritizing conservation measures, facilitating targeted land management interventions, and supporting resource planning in the Tsmieti catchment.

The article presents the results of the analysis of the use of geographic information systems (GIS) in the management of agricultural land resources in the Omsk region. A list of organizations using GIS is identified, including Rosreestr, Roskadastr, the Ministry of Agriculture, the Agrochemcenter and private geodetic enterprises. The dynamics of the number of organizations using GIS for 2020–2024 is shown, which decreased by 3.1% due to economic factors, but the share of farms using open-source software is growing. The land management structure, including federal, regional and municipal bodies, is described, and key problems are identified: departmental disunity, outdated cartographic materials, lack of field measurements and low data integration. Solutions are proposed, including the creation of a unified geoinformation environment based on the integration of remote sensing data, unmanned aerial vehicles, IoT sensors and field surveys. The expected effectiveness of the proposed technology will increase the efficiency and reliability of information, reduce land management costs and improve control over the use of agricultural land.

Background. The growth of urban populations leads to swift urban development. Consequently, much of the unplanned expansion of new neighborhoods results in the loss of public open space. The deficiency of public open space, particularly Urban Green Space (UGS), diminishes the quality of life and adversely affects community health. This study seeks to provide an overview of the significance of UGS. Furthermore, it aims to evaluate the accessibility of UGS in the context of Famagusta city in Cyprus.Method. The methodology employed in this study integrates geographical information systems, questionnaires, and field surveys to assess UGS accessibility within the study area. Additionally, the principles of English Nature concerning UGS accessibility were utilized as a foundation for this evaluation.Result. The findings of the study indicate that the rapid urbanization of the area has led to a reduction in UGS. Indeed, the existing UGS in Famagusta is both physically and visually inaccessible. The absence of paved pedestrian pathways, lighting, recreational amenities, and seating furniture renders the UGS ineffective. The current circumstances necessitate immediate action from the relevant authorities.

Municipal solid waste planning in a rebuilding city is not only a question of cost or technology choice. It also depends on access, operational feasibility, environmental exposure, and the policy conditions under which the system can be rebuilt. This study develops a policy-driven mixed-integer linear programming (MILP) model for designing a low-carbon municipal solid waste (MSW) network that includes transfer stations, mechanical-biological treatment (MBT) facilities, waste-to-energy (WtE) plants, and landfills. The model evaluates four planning concerns together: total cost, carbon emissions, implementation feasibility risk, and site environmental risk. In addition to the four-goal baseline formulation, the study explicitly examines three carbon-policy regimes: carbon-tax internalization, carbon tax combined with processing subsidy, and cap-and-trade. The model is tested in Idlib Governorate in northwest Syria, where formal municipal data are scarce. For this reason, the case-study dataset combines field-based information, input from eleven experts, geographic information system (GIS)-based risk assessment, fuzzy Full Consistency Method (Fuzzy-FUCOM) weighting, and weighted goal programming. The baseline solution opens three transfer stations, two MBT facilities, one WtE plant, and two landfills, generating about 141,128 MWh of electricity per year at an annual cost of USD 216.8million. The sensitivity results show that waste supply has the largest effect on cost, while the MBT-to-WtE fraction has the strongest effect on emissions and energy recovery. The policy scenarios show a mainly financial effect under the tested settings: carbon tax increases the monetized cost of emissions, processing subsidy reduces part of the treatment cost, and cap-and-trade creates either permit costs or trading revenue depending on the cap. Overall, the framework provides a practical planning structure for MSW network design where data, infrastructure, and governance capacity are limited.

Modelling the spatial patterns and recent trends of multiple air pollutants in Romania based on long-term gridded EMEP data.

ABSTRACT This study seamlessly integrates Geographic Information System (GIS) with an enhanced Particle Swarm Optimization (PSO) algorithm to conduct a detailed geospatial analysis of pine wood nematode disease, a severe forest affliction. Our research team curated a comprehensive disease database and utilized GIS technology to delve into the spatial distribution characteristics of the disease. By merging this data with the PSO algorithm, we developed a disease recognition model capable of precise detection and analysis of the disease. Experimental outcomes highlight that the fusion of GIS and Memetic Computing (MC) methods provides a more accurate reflection of the spatiotemporal distribution of the disease. Notably, the recognition model forged with the PSO algorithm achieved an exceptional accuracy rate of 98.41%, outperforming other methodologies. This research not only deepens the understanding of the spatiotemporal patterns of pine wood nematode disease but also offers a scientific foundation for the formulation of more effective prevention and control strategies, significantly contributing to the monitoring and management of forest diseases.

The effect of shadowed areas on smothering risk in three commercial free-range layer poultry farms, Australia 2019-2022.