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Introduction: Transportation barriers can affect travel needs and quality of life. Methods: This survey examined transportation, routine activities, and unmet travel needs among older Vietnamese immigrants, focusing on gender differences. Results: Women were more likely to ride with others, less likely to drive, had fewer types of routine activities, and went out for activities less than men. Over 1/4 of the sample had at least one unmet travel need in the previous month (the most common was for visiting family and friends). Discussion: Interventions to enhance mobility should address age- and gender-based transportation differences and assess for unmet travel needs. [ABSTRACT FROM AUTHOR]

The communication paradigm was adopted by cartographic researchers in the 1960s as a means of describing and explaining the nature of mapping; the interaction amongst cartographers, their maps, and map users; and the way in which maps 'work'. This paper considers some of the shortcomings of the cartographic communication paradigm, but proposes that enhancing it by incorporating modifications to its rigid linear and component-based model is useful, in trying to explain the success of maps in human society. The enhancements discussed all contribute to a deeper investigation of the 'context' of cartographic activity, the model space which the traditional graphical representations of cartographic communication occupy. Thus, we present an exploration of several topics which it is felt could be incorporated into a deeper analysis of context: the concept of affordances; the role of cognition in human engagement with geospatial data and with maps; the nature of communication through the map medium; and the adoption of carto-pragmatics, a human-centred approach to map use. It is concluded that there are inherent relationships among these topics and each suffuses the total cartographic communication system, and each of its previously identified elements, therefore affecting the definition of 'context'. [ABSTRACT FROM AUTHOR]

A resilience analysis of a water distribution system is greatly enhanced by the integration of up-to-date geospatial data describing the water system, hazards, and surrounding community. The Water Network Tool for Resilience (WNTR), an open-source Python package designed to simulate and analyze the resilience of water distribution systems, was recently updated to incorporate geographic information system (GIS) data into the resilience analysis. This paper describes the GIS capabilities and includes a case study using the drinking water distribution system model for a large city in Pennsylvania. The case study focuses on potential pipe damage from landslides and on pipes that are particularly difficult to repair. The analysis couples data on hazards, social vulnerability, and the location of emergency services to identify and prioritize high-impact critical infrastructure for mitigation. Results demonstrate that pipes can be prioritized for mitigation based on water shortage and vulnerable populations that are affected. The methods can be adopted for general use and are available as part of the WNTR software. [ABSTRACT FROM AUTHOR]

Background: The National Expanded Program on Immunization in the Democratic Republic of the Congo implemented a program in 9 Provinces to generate georeferenced immunization microplans to strengthen the planning and implementation of vaccination services. The intervention aimed to improve identification and immunization of zero-dose children and overall immunization coverage. Methods: This study applies a mixed-methods design including survey tools, in-depth interviews and direct observation to document the uptake, use, and acceptance of the immunization microplans developed with geospatial data in two intervention provinces and one control province from February to June 2023. A total of 113 health facilities in 98 Health Areas in 15 Health Zones in the three provinces were included in the study sample. Select providers received training on gender-intentional approaches for the collection and use of geospatial data which was evaluated through a targeted qualitative study. A secondary analysis of immunization coverage survey data (2020–2022) was conducted to assess the associated effects on immunization coverage, especially changes in rates of zero dose children, defined as those aged 12–23 months who have not received a single dose of Pentavalent vaccine. Results: This research study shows that georeferenced microplans are well received, utilized, and led to changes in routine immunization service planning and delivery. In addition, the gender intervention is perceived to have led to changes in the approaches taken to overcome sociocultural gender norms and engage communities to reach as many children as possible, leveraging the ability of women to engage more effectively to support vaccination services. The quantitative analyses showed that georeferenced microplans may have contributed to a dramatic and sustained trend of high immunization coverage in the intervention site of Haut-Lomami, which saw dramatic improvement in coverage for 3 antigens and little change in Pentavalent drop-out rate over three years of implementation. Conclusion: The overall study identified positive contributions of the georeferenced data in the planning and delivery of routine immunization services. It is recommended to conduct further analyses in Kasai in 2024 and 2025 to evaluate the longer-term effects of the gender intervention on immunization coverage and equity outcomes. Trial registration: The study was registered and given BMC Central International Standard. Randomised Controlled Trial Number ISRCTN65876428 on March 11, 2021. [ABSTRACT FROM AUTHOR]

Introduction: Virtual reality (VR) used for healthcare, particularly through exergames, is promising for improving therapeutic outcomes. However, effectively engaging patients and providing realistic environments for everyday situations remain major challenges. The technical aspects of developing engaging VR applications for rehabilitation are largely unexplored. This research presents the development of a head-mounted display VR (HMD-VR) exergame for gait therapy. The novelty lies in the use of high-fidelity immersive environments implementing 3D geospatial data and motion to create targeted therapeutic applications that closely mimic reality while harnessing the environment's restorative functions. Methods: We integrated 3D point clouds from laser scans and geolocated ambisonic sound recordings into a game engine. We combined different techniques for user motion tracking, while we used point cloud manipulation for integrating specific training elements. Feedback on the quality of the HMD-VR exergame was received from the first implementations. Results: Our methodology demonstrates the successful, highly realistic VR replication of restorative real-world environments using 3D point clouds and environmental sounds. We illustrate the adaptability of the environment for therapeutic use through manipulation of the 3D point cloud, facilitating customizable training difficulty levels while promoting immersive experiences. Participant feedback (sample size: 49 sessions) confirms the HMD-VR exergame's applicability as a restorative experience (ClinicalTrials.gov NCT06304077). Discussion: Our research introduces a pioneering HMD-VR game for gait rehabilitation, leveraging immersive VR environments grounded in the real world. This innovative approach offers new possibilities for efficient and effective rehabilitation interventions. Future studies will analyze effects on gait patterns across different environments and their restorative functions and evaluate the HMD-VR xergame in clinical settings. [ABSTRACT FROM AUTHOR]

The European Ground Motion Service (EGMS) and geospatial data are integrated in this paper to evaluate ground deformation and its effects on critical infrastructures in the Preveza Regional Unit. The EGMS, a new service of the Copernicus Land Monitoring Service, employs information from the C-band Synthetic Aperture Radar (SAR)-equipped Sentinel-1A and Sentinel-1B satellites. This allows for the millimeter-scale measurement of ground motion, which is essential for assessing anthropogenic and natural hazards. The study examines ground displacement from 2018 to 2022 using multi-temporal Synthetic Aperture Radar Interferometry (MTInSAR). The Regional Unit of Preveza was selected for study area. According to the investigation, the area's East–West Mean Velocity Displacement varies between 22.5 mm/y and −37.7 mm/y, while the Vertical Mean Velocity Displacement ranges from 16 mm/y to −39.3 mm/y. Persistent Scatterers (PSs) and Distributed Scatterers are the sources of these measurements. This research focuses on assessing the impact of ground deformation on 21 school units, 2 health centers, 1 hospital, 4 bridges and 1 dam. The findings provide valuable insights for local authorities and other stakeholders, who will greatly benefit from the information gathered from this study, which will lay the groundwork for wise decision-making and the creation of practical plans to strengthen the resistance of critical infrastructures to ground motion. [ABSTRACT FROM AUTHOR]

This study presents an integrated framework that combines spatial clustering techniques and multi-source geospatial data to comprehensively assess and understand geological hazards in Hunan Province, China. The research integrates self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) to explore the relationships between environmental factors and the occurrence of various geological hazards, including landslides, slope failures, collapses, ground subsidence, and debris flows. The key findings reveal that annual average precipitation (Pre), profile curvature (Pro_cur), and slope (Slo) are the primary factors influencing the composite geological hazard index (GI) across the province. Importantly, the relationships between these key factors and GI exhibit spatial variability, as evidenced by the random intercept and slope models, highlighting the need for customized mitigation strategies. Additionally, the study demonstrates that land use patterns and stratigraphic stratum lithology significantly impact the cluster-specific relationships between the key factors and GI, emphasizing the importance of natural resource management for effective geological hazard mitigation. The proposed integrated framework provides valuable insights for policymakers and resource managers to develop spatially-aware strategies for geological hazard risk reduction and climate change adaptation. [ABSTRACT FROM AUTHOR]

Arctic permafrost is undergoing rapid changes due to climate warming in high latitudes. Retrogressive thaw slumps (RTS) are one of the most abrupt and impactful thermal-denudation events that change Arctic landscapes and accelerate carbon feedbacks. Their spatial distribution remains poorly characterised due to time-intensive conventional mapping methods. While numerous RTS studies have published standalone digitisation datasets, the lack of a centralised, unified database has limited their utilisation, affecting the scale of RTS studies and the generalisation ability of deep learning models. To address this, we established the Arctic Retrogressive Thaw Slumps (ARTS) dataset containing 23,529 RTS-present and 20,434 RTS-absent digitisations from 20 standalone datasets. We also proposed a Data Curation Framework as a working standard for RTS digitisations. This dataset is designed to be comprehensive, accessible, contributable, and adaptable for various RTS-related studies. This dataset and its accompanying curation framework establish a foundation for enhanced collaboration in RTS research, facilitating standardised data sharing and comprehensive analyses across the Arctic permafrost research community. [ABSTRACT FROM AUTHOR]

This study uses the Fire Weather Index (FWI) from the Canadian Wildfire Hazard Rating System (CFFDRS) to assess the danger of future wildfires in the Zagros Mountains under climate change. We use CanESM5 model data for the SSP245 and SSP585 scenarios to estimate FWI changes in the past (1960–2020), near future (2031 − 2017), and distant future (2061–2090). Statistical analysis at the 95% confidence level verifies significant differences in FWI values, especially in the Northwest. We explore spatial and temporal trends using Emerging Hot Spot Analysis (EHSA). The results show that cold spots are decreasing, and warm spots are increasing over time. The results of this study highlight the continuing dangers of wildfires in the region. The study underscores the significance of implementing preventive fire management strategies that consider the dynamic nature of weather patterns. [ABSTRACT FROM AUTHOR]

Data dashboards provide a means for sharing multiple data products at a glance and were ubiquitous during the COVID-19 pandemic. Data dashboards tracked global and country-specific statistics and provided cartographic visualizations of cases, deaths, vaccination rates and other metrics. We examined the role of geospatial data on COVID-19 dashboards in the form of maps, charts, and graphs. We organize our review of 193 COVID-19 dashboards by region and compare the accessibility and operationality of dashboards over time and the use of web maps and geospatial visualizations. We found that of the dashboards reviewed, only 17% included geospatial visualizations. We observe that many of the COVID-19 dashboards from our analysis are no longer accessible (66%) and consider the ephemeral nature of data and dashboards. We conclude that coordinated efforts and a call to action to ensure the standardization, storage, and maintenance of geospatial data for use on data dashboards and web maps are needed for long-term use, analyses, and monitoring to address current and future public health and other challenging issues. [ABSTRACT FROM AUTHOR]

The key to geospatial data integration lies in identifying corresponding objects from different sources. Aiming at the problem of the low matching accuracy of geospatial entities under a single feature attribute, a geospatial entity matching method based on multi-feature value calculation is proposed. Firstly, when dealing with POI (point of interest) data, the similarity of POI data in terms of name, address, and distance is calculated by combining the improved hybrid similarity method, the Jaccard method, and the Euclidean metric method. Secondly, the random forest algorithm is utilized to dynamically determine the information weights of each attribute and calculate the comprehensive similarity. Finally, taking the area within the Second Ring Road in Beijing as the experimental area, the POI data of Tencent Maps and Amap are collected to verify the method proposed in this paper. The experimental results show that, compared with the existing POI matching methods, the accuracy and recall rate of the results obtained by the POI matching and fusion method proposed in this paper are significantly improved, which verifies the accuracy and feasibility of the matching. [ABSTRACT FROM AUTHOR]

Sustainable Development Goal 15 (SDG 15) specifically targets the protection, restoration, and sustainable use of terrestrial ecosystems, including forests, wetlands, mountains, and drylands, along with their biodiversity. This study localizes the SDG 15 indicator system and integrates geospatial and statistical data to construct an enhanced evaluation framework for assessing the sustainable development of terrestrial ecosystems at the county level. The proposed system encompasses key indicators such as forest coverage rate, terrestrial biodiversity, sustainable forest management, land degradation neutrality, mountain biodiversity, and mountain green cover index. Using Guilin City as a study area, the ecological status of each county was assessed over the period 2010 to 2020, providing valuable insights to guide ecological conservation and sustainable development efforts. The main results are as follows: (1) Spatial heterogeneity is evident in the distribution of key biodiversity areas, which are concentrated in the northern and southeastern mountainous regions of Guilin. (2) Land degradation during the assessment period is notably smaller than during the baseline period, though a significant gap remains toward achieving land degradation neutrality. (3) Sustainable development scores for terrestrial ecosystems show an overall upward trend across counties, but the poor performance in sustainable forest management affects the comprehensive sustainable development of terrestrial ecosystems in Guilin. The localized SDG 15 indicator system proposed in this paper can effectively quantify changes in terrestrial ecosystems and visualize their spatial distribution, and can effectively serve as a model for other sustainable development areas. [ABSTRACT FROM AUTHOR]

The evolving capabilities of Geographic Information Systems (GISs) are transforming various industries, including e-commerce, by providing enhanced spatial analysis and precision in customer targeting, and improving the ability of recommender systems. This paper proposes a novel framework that integrates mobile agents with GISs to deliver real-time, personalized recommendations in e-commerce. By utilizing the OpenStreetMap API for geographic mapping and the Java Agent Development Environment (JADE) platform for mobile agents, the system leverages both geospatial data and customer preferences to offer highly relevant product suggestions based on location and behaviour. Mobile agents enable real-time data collection, processing, and interaction with customers, facilitating dynamic adaptations to their needs. The combination of GISs and mobile agents enhances the system's ability to analyze spatial data, providing tailored recommendations that align with user preferences and geographic context. This integrated approach not only improves the online shopping experience but also introduces new opportunities for location-specific marketing strategies, boosting the effectiveness of targeted advertising. The validation of this system highlights its potential to significantly enhance customer engagement and satisfaction through context-aware recommendations. The integration of GISs and mobile agents lays a strong foundation for future advancements in personalized e-commerce solutions, offering a scalable model for businesses looking to optimize marketing efforts and customer experiences. [ABSTRACT FROM AUTHOR]

Prior research has assessed the ways in which neighborhoods promote or inhibit children's development but has paid less attention to delineating the particular processes through which neighborhoods are linked to child outcomes. This study combines geospatial data with survey data from the Early Childhood Longitudinal Study Kindergarten Cohort of 2010–2011, a nationally representative sample of kindergarteners followed through 5th grade (N ~ 12,300), to explore how differences in neighborhood resources (parks and services) and stressors (crime and neighborhood disadvantage) are associated with variations in parental inputs—school involvement and provision of out‐of‐home enrichment activities. Using multilevel models assessing within‐ and between‐family associations, we found mixed evidence concerning how neighborhood features are linked to parental inputs. Considering within‐family changes in neighborhood contexts, concentrated disadvantage negatively predicted parental inputs, particularly following a move to a more disadvantaged neighborhood. Results were more consistent between families: concentrated disadvantage was associated with lower school involvement and out‐of‐home enrichment, while community services and parks were associated with more involvement and enrichment. Neighborhood crime was not associated with parental inputs. Results shed light on methodological limitations of neighborhood effects research and suggest the need for more rigorous methods, such as natural experiments which can capture exogenous changes in neighborhood processes. [ABSTRACT FROM AUTHOR]

Accurately estimating atmospheric chloride deposition can offer important insights into metal corrosion. Corrosion of metals causes high associated costs in engineering work. Hence, there are standardized models to estimate the corrosivity of environments. One entry variable of such models is airborne salinity that comes from the sea, collected with the wet candle method and measured in chloride ion dry deposition rate. This method has some limitations because it is complex and demands long periods of exposure to environments. This work proposes a new data-driven approach for the prediction of dry deposition of chloride ions using reanalysis and geospatial data. Hence, we have developed a machine-learning regression model based on Random Forests for the prediction of airborne salinity. This work also analyzes variables and compares the proposed approach with other available models. The Random Forest obtained the best results with an r 2 of 0.82. [ABSTRACT FROM AUTHOR]

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Invasive pests cause major ecological and economic damages to forests around the world including reduced carbon sequestration and biodiversity and loss of forest revenue. In this study, we used Random Forest to model forest mortality resulting from a 2015–2017 Spongy moth outbreak in the temperate deciduous forests of Rhode Island (northeastern U.S.). Mortality was modeled with a 100 m spatial resolution based on Landsat-derived defoliation maps and geospatial data representing soil characteristics, drought condition, and forest characteristics as well as proximity to coast, development, and water. Random Forest was used to model forest mortality with two classes (low/high) and three classes (low/med/high). The best models had overall accuracies of 82% and 65% for the two-class and three-class models, respectively. The most important predictors of forest mortality were defoliation, distance to coast, and canopy cover. Model performance improved only slightly with the inclusion of more than three variables. The models classified 35% of forests as having canopy mortality >5 trees/ha and 21% of Rhode Island forests having mortality >11 trees/ha. The study shows the benefit of Random Forest models that use both defoliation maps and geospatial environmental data for classifying forest mortality caused by Spongy moth. [ABSTRACT FROM AUTHOR]

Humans set boundaries on land for thousands of years. Cadastre emerged as a system for registering them, whether they are marked on the ground (visible) or unmarked (invisible). Parcel boundary data stored in geospatial databases and supported with registration documents are legally binding in many countries. They are however subject to change physically due to anthropogenic activities and natural processes. Seismic activities inducing surface rupture, lateral spread, landslide, liquefaction, and rockfall are among the main natural causes yielding physical boundary alterations. Spatial pattern and magnitude of alterations depend on the geohazard type and geological characteristics of the area. If a cadastral database is not updated, the physical and registered boundary begins to diverge, leading to uncertainty that needs to be understood by both people and authorities. As a prominent example, the 6 February 2023 Kahramanmaras (Turkiye) earthquakes (Mw7.7 and Mw7.6), which affected a very large region covering approximately 100,000 km2, caused enormous alterations on the physical boundaries of approximately 5 million land parcels. In this study, we analyzed different boundary changes caused by this major event and proposed a conceptual framework based on physical, documentary, and spatial boundary definitions of cadastral parcels with examples from the Kahramanmaras earthquakes. Considering the size of the area and the immense cost of cadastral renovation projects, we analyzed the deformation patterns and possible magnitudes from an interdisciplinary perspective and presented an overview. The study findings provide insights for addressing boundary discrepancies, although additional research is required for comprehensive quantitative assessments across the entire area. [ABSTRACT FROM AUTHOR]

Highlights: Introducing a novel Fuzzy Inference System (FIS) approach that integrates satellite and GIS data for precise earthquake risk mapping and minimizing uncertainty. Achieving comprehensive earthquake risk assessment through the integration of diverse data sources. Enhancing the credibility of modeling results by validating with historical earthquake data, providing valuable insights for policymakers addressing natural hazards. Earthquakes pose significant natural hazards and impact populations worldwide. Iran is among the most susceptible countries to seismic activity, making comprehensive earthquake risk assessment crucial. This study employs geospatial methods, including integrating satellite, ground-based, and auxiliary data to model earthquake risk across this country. A Fuzzy Inference System (FIS) is used to generate earthquake hazard probability and vulnerability layers, considering factors such as slope, elevation, fault density, building density, proximity to main roads, proximity to buildings, population density, and earthquake epicenter, magnitude, proximity to the epicenter, depth density, peak ground acceleration (PGA). The results highlight high-risk areas in the Alborz and Zagros Mountain ranges and coastal regions. Moreover, the findings indicate that 39.7% (approximately 31.7 million people) of Iran's population resides in high-risk zones, with 9.6% (approximately 7.7 million) located in coastal areas vulnerable to earthquakes. These findings offer valuable insights for crisis management and urban planning initiatives. [ABSTRACT FROM AUTHOR]

Background: Malaria is a major public health issue in Nekemte City, western Ethiopia, with various environmental and social factors influencing transmission patterns. Effective control and prevention strategies require precise identification of high-risk areas. This study aims to map malaria risk zones in Nekemte City using geospatial technologies, including remote sensing and Geographic Information Systems (GIS), to support targeted interventions and resource allocation. Methods: The study integrated environmental and social factors to assess malaria risk in the city. Environmental factors, including climatic and geographic characteristics, such as elevation, rainfall patterns, temperature, slope, and proximity to river, were selected based on experts' opinions and literature review. These factors were weighted using the analytic hierarchy process according to their relative influence on malaria hazard susceptibility. Social factors considered within the GIS framework focused on human settlements and access to resources. These included population density, proximity to health facilities, and proximity to roads. The malaria risk analysis incorporated hazard and vulnerability layers, along with Land use/cover (LULC) data. A weighted overlay analysis method combined these layers and generate the final malaria risk map. Results: The malaria risk map identified that 18.2% (10.5 km2) of the study area was at very high risk, 18.8% (10.9 km2) at high risk, 30.4% (17.8 km2) at moderate risk, 19.8% (11.5 km2) at low risk, and 12.6% (7.3 km2) at very low risk. A combined 37% (21.4 km2) of Nekemte City was classified as at high to very high malaria risk, highlighting key areas for intervention. Conclusions: This malaria risk map offers a valuable tool for malaria control and elimination efforts in Nekemte City. By identifying high-risk areas, the map provides actionable insights that can guide local health strategies, optimize resource distribution, and improve the efficiency of interventions. These findings contribute to enhanced public health planning and can support future regional malaria control initiatives. [ABSTRACT FROM AUTHOR]

In optical remote sensing images, clouds exhibit irregular scales and boundaries that vary with elevation across diverse geographical locations. To accurately capture the diverse visual patterns of clouds, we propose a cloud image segmentation approach named GS-CDNet (Geographic Spatial Data-Cloud Detection Network), which is based on the integration of geospatial data with multifaceted self-attention feature extraction, multi-scale feature aggregation, and boundary clarification techniques.Firstly, we utilize geographical coordinates from optical remote sensing images to extract a raster DEM (Digital Elevation Model) from SRTM3. This process creates a dataset consisting of elevation images, longitude, and latitude maps as geospatial data, enhancing the model's capability in spatial positioning for cloud detection. Secondly, the proposed method consists of three interconnected modules within the cloud detection network: the Interleaved Self-Attention module(ISAM) utilizes a variety of self-attention mechanisms in an interleaved manner to extract multi-scale feature information.The Bidirectional Multi-Scale Feature Fusion Module(BIMFM) is responsible for integrating features, enabling a more comprehensive contextual understanding. The Boundary Extraction Module(BEM) utilizes a residual structure to generate a boundary cloud mask, effectively addressing the common issue of boundary blurring in multi-scale cloud masks. Finally, we compared and evaluated GS-CDNet with other cloud detection methods and conducted an ablation study on the key components of the method. The validation of generalization performance demonstrates the exceptional performance of the proposed model in cloud mask generation. Geospatial data and the different modules of the method play a significant role in the model. [ABSTRACT FROM AUTHOR]

This work presents the mathematical definition and programming considerations of an efficient geometric algorithm used to add roofs to polyhedral 3D building models obtained from OpenStreetMap. The algorithm covers numerous roof shapes, including some well-defined shapes that lack an explicit reconstruction theory. These shapes include gabled, hipped, pyramidal, skillion, half-hipped, gambrel, and mansard. The input data for the developed code consist of latitude and longitude coordinates defining the target area. Geospatial data necessary for the algorithm are obtained through a request to the overpass-turbo service. The findings showcase outstanding performance for buildings with straightforward footprints, but they have limitations for the ones with intricate footprints. In future work, further refinement is necessary to solve the mentioned limitation. [ABSTRACT FROM AUTHOR]

In recent years, the growth of digital data has been unimaginable. This also applies to geospatial data. One of the largest data types is LiDAR point clouds. Their large volumes on disk, both at the acquisition and processing stages, and in the final versions translate into a high demand for disk space and therefore electricity. It is therefore obvious that in order to reduce energy consumption, lower the carbon footprint of the activity and sensitize sustainability in the digitization of the industry, lossless compression of the aforementioned datasets is a good solution. In this article, a new format for point clouds—3DL—is presented, the effectiveness of which is compared with 21 available formats that can contain LiDAR data. A total of 404 processes were carried out to validate the 3DL file format. The validation was based on four LiDAR point clouds stored in LAS files: two files derived from ALS (airborne laser scanning), one in the local coordinate system and the other in PL-2000; and two obtained by TLS (terrestrial laser scanning), also with the same georeferencing (local and national PL-2000). During research, each LAS file was saved 101 different ways in 22 different formats, and the results were then compared in several ways (according to the coordinate system, ALS and TLS data, both types of data within a single coordinate system and the time of processing). The validated solution (3DL) achieved CR (compression rate) results of around 32% for ALS data and around 42% for TLS data, while the best solutions reached 15% for ALS and 34% for TLS. On the other hand, the worst method compressed the file up to 424.92% (ALS_PL2000). This significant reduction in file size contributes to a significant reduction in energy consumption during the storage of LiDAR point clouds, their transmission over the internet and/or during copy/transfer. For all solutions, rankings were developed according to CR and CT (compression time) parameters. [ABSTRACT FROM AUTHOR]

Massive image datasets are often required for the proper functioning of Machine Learning (ML) and Computer Vision (CV) applications. This paper offers a solution to computational challenges in the Image Processing of satellite imagery, by proposing an optimization procedure. The presented approach is verified by an exemplary Python implementation, constituting a standalone tool for automating the dataset creation and labeling, including the extraction of road network data from the national satellite cartography provider. The collected data include detailed road maps along with the parcel information obtained via WebMapService endpoints. The method presented in this paper involves three basic steps: road segmentation (using the Shapely module) to facilitate handling high-resolution orthoimagery, and then a modified Region-of-Interest approach, i.e., removing irrelevant areas, with only roads remaining. This results in obtaining file sizes that are significantly smaller. The presented algorithm also involves asynchronous tile downloading, which, combined with the masking of irrelevant areas, improves not only the efficiency but surprisingly also the accuracy of subsequent ML/CV procedures. The research results of the paper reveal substantial file size reduction, and improved processing efficiency, thus making the optimized geospatial graphical data more practical for ML/CV applications, while still maintaining the original data quality and relevance of the analyzed parcels or infrastructure. [ABSTRACT FROM AUTHOR]

Across the western North American boreal region, networks of narrow clearings called seismic lines from oil and gas exploration fragment forests. Restoration of seismic lines for habitat recovery of threatened woodland caribou has been prioritized, but there is little guidance on temporal and spatial targets for boreal forest recovery. Between 2016 and 2022, we sampled regenerating trees on 344 seismic lines with limited re‐disturbance across the oil sands region of Alberta, Canada. We modeled growth relationships for regenerating trees, including regeneration lags, using field and geospatial data to predict passive forest recovery on seismic lines. Recovery on seismic lines in peatland and transitional forests could take >30 years, due to longer regeneration lags (8–13 years) and slower‐growing tree species (>25 years to reach 3 m). Recovery in xeric and mesic uplands was nearly half that, due to shorter regeneration lags (3–5 years), faster‐growing species (9–13 years to reach 3 m), and recent wildfires. Over half of seismic lines in upland forests had predicted regeneration lags ≤5 years, including many seismic lines that burned after initial seismic line clearing, indicating regeneration was not delayed. However, all seismic lines in transitional and peatland forests were predicted to have regeneration lags >8 years. Slower recovery on seismic lines is associated with the compounding effects of longer regeneration lags and slower growth rates of dominant tree species. Restoration efforts should prioritize seismic lines where active treatment can significantly reduce regeneration lags and expedite growth. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer's accuracies, class-level precisions or user's accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development. [ABSTRACT FROM AUTHOR]

Mobile mapping systems integrate multiple sensors to collect large volumes of geospatial data in motion. With the growing demand for mapping enclosed and hard-to-reach areas, there has been significant advancement in handheld mobile mapping systems utilizing SLAM (Simultaneous Localization and Mapping) technology. To ensure their data is efficiently usable, these systems should produce oriented images, which are essential for visualization, point cloud colorization, and monoplotting. Achieving so requires precise extrinsic camera calibration. This research provides a comprehensive overview of existing extrinsic camera calibration methods and evaluates their suitability for application in handheld mobile mapping systems. The goal is to identify a method that meets the accuracy and practical needs of these systems, facilitating more effective data processing and utilization in challenging environments. [ABSTRACT FROM AUTHOR]

Walking is a sustainable, safe, and active mode of transportation. The benefits that the elderly in particular gain from outdoor walking are manifold, be it free and independent access to stores and services of all kinds or the opportunity to socialize, enjoy parks, et cetera. This article depicts one particular factor that affects outdoor walkability, namely the gradient of walking paths. Steep slopes can be a serious obstacle to walkability, primarily for older people. The evaluation of available geospatial data sources formed the basis for a geospatial analysis of walkability in the larger city of Kaiserslautern, located in southwest Germany. The concept of Walk Score was used to quantify the results obtained. The results demonstrate that the Walk Score can be refined to better address the mobility needs of older adults. The methodology was implemented for the German city of Kaiserslautern by integrating volunteered geographic information with high-quality official datasets. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has irreversibly altered consumer shopping patterns, engendering a paradigm shift in business-consumer dynamics. Leveraging geospatial tools enables businesses to pinpoint customer travel patterns, forecast demand, and augment both efficiency and customer satisfaction. This study introduces innovative GIS and spatial marketing methodologies to amass and analyze geospatial data, thereby enhancing business operations and customer engagement in southern United States cities. We devised a heatmap detailing the travel and nocturnal locations of Walmart patrons during pandemic, utilizing geolocational data to assist businesses in refining market segmentation and identifying target demographics. Our research reveals significant shifts in consumer behavior due to the pandemic, underscoring an immediate necessity for businesses to acclimate to these evolutions. Providentially, geolocational data emerges as an indispensable asset for retailers, facilitating a competitive stance through meticulous customer base analysis—crucial for discerning consumer necessities and apprehending societal tendencies. [ABSTRACT FROM AUTHOR]

Definition: Geomasking is a set of techniques that introduces noise or intentional errors into geospatial data to minimize the risk of identifying exact location information related to individuals while preserving the utility of the data to a controlled extent. It protects the geoprivacy of the data contributor and mitigates potential harm from data breaches while promoting safer data sharing. The development of digital health technologies and the extensive use of individual geospatial data in health studies have raised concerns about geoprivacy. The individual tracking data and health information, if accessed by unauthorized parties, may lead to privacy invasions, criminal activities, and discrimination. These risks underscore the importance of robust protective measures in the collection, management, and sharing of sensitive data. Geomasking techniques have been developed to safeguard geoprivacy in geospatial health data, addressing the risks and challenges associated with data sharing. This entry paper discusses the importance of geoprivacy in geospatial health data and introduces various kinds of geomasking methods and their applications in balancing the protection of individual privacy with the need for data sharing to ensure scientific reproducibility, highlighting the urgent need for more effective geomasking techniques and their applications. [ABSTRACT FROM AUTHOR]

Although Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research. [ABSTRACT FROM AUTHOR]

This study presents a method based on retrofitted low-cost and easy to implement tracking devices, used to monitor the whole harvesting process in viticulture, to map yield and harvest quality parameters in viticulture. The method consists of recording the geolocation of all the machines (harvest trailers and grape harvester) during the harvest to spatially re-allocate production parameters measured at the winery. The method was tested on a vineyard of 30 ha during the whole 2022 harvest season. It has identified harvest sectors (HS) associated with measured production parameters (grape mass and harvest quality parameters: sugar content, total acidity, pH, yeast assimilable nitrogen, organic nitrogen) and calculated production parameters (potential alcohol of grapes, yield, yield per plant) over the entire vineyard. The grape mass was measured at the vineyard cellar or at the wine-growing cooperative by calibrated scales. The harvest quality parameters were measured on grape must samples in a commercial laboratory specialized in oenological analysis and using standardized protocols. Results validate the possibility of making production parameters maps automatically solely from the time and location records of the vehicles. They also highlight the limitations in terms of spatial resolution (the mean area of the HS is 0.3 ha) of the resulting maps which depends on the actual yield and size of harvest trailers. Yield per plant and yeast assimilable nitrogen maps have been used, in collaboration with the vineyard manager, to analyze and reconsider the fertilization process at the vineyard scale, showing the relevance of the information. [ABSTRACT FROM AUTHOR]

The Dorling map, a special type of thematic map, is a widely employed tool for visualizing geospatial statistical data. It transforms regions into circles with areas proportionate to statistical values while endeavoring to maintain regional spatial relationships. Notably, two types of Dorling maps emerge, distinguished by their consideration of regional continuity: continuous and discontinuous Dorling maps. However, as of yet, the discrepancies between these two types remain inconclusive. In this paper, we employ an eye‐tracking method to investigate the efficacy of Dorling maps in two common application scenarios, namely unpurposed browsing tasks and purposeful reading tasks. To this end, we administer tasks involving region search, attribute comparison/recognition/memory, conditional selection, relationship judgment, summary, and subjective evaluation. Subsequently, we perform a statistical analysis of the eye movement data of participants when they complete the above tasks in the continuous and discontinuous Dorling maps. The results indicate that the discontinuous Dorling maps are significantly better than the continuous ones in interpretation time for forward and reverse region search, selecting conditions, and judging adjacent relationships. Continuous Dorling maps significantly outperform discontinuous maps in terms of search efficiency during attribute comparison. Moreover, continuous maps significantly outperformed discontinuous maps in terms of cognitive supplementation or reprocessing of previous regions during conditional selection. This study can help users choose the right form of Dorling map visualization according to their needs. [ABSTRACT FROM AUTHOR]

Multi-platform geospatial data synergy is critical for complete and reliable surveys. This study investigates various methods for combining data from terrestrial laser scanning (TLS), orthophotos, databases of topographic objects, utility databases, tacheometry, and GNSS to assess and improve positioning accuracy and consistency of data in spatial databases. The study highlights the challenges and solutions regarding integrating various datasets to yield a complete and reliable geospatial database for building surveys and the construction and keeping of spatial databases. Input from diversified surveying technologies, such as TLS, GNSS, and orthophotos, offers detailed and precise data necessary to create and update accurate base maps and databases of topographic objects. A complete survey of a structure and its surroundings demonstrates how the synergistic application of diverse data sources helps improve the positioning accuracy and consistency of spatial databases. The results indicate the importance of multi-platform collaboration for high data quality standards in surveying, which is critical for effective planning and implementation of surveying projects. [ABSTRACT FROM AUTHOR]

Coal mining activities require the establishment of good and correct mine geometry to support effective and safe operations. However, many companies face challenges in ensuring that the geometry formed conforms to set standards. This research highlights the limited use of UAV technology in mine geometry optimization. This research aims to analyze geometry shaping at mine sites using geospatial data and raise awareness of the importance of good geometry in mining. The methods used include a combination of geographic information systems (GIS) and aerial surveys with UAVs. The mine geometry information was presented in map form to improve understanding of the geometry formation at each mine site. The results showed a 14% increase in geometry achievement, from 67% to 81%, after the application of the geospatial-based mine geometry information map. This reflects a significant increase in the formation of geometry in accordance with applicable rules. This finding shows the great potential of utilizing geospatial methods in the mining industry to improve the achievement of good geometry. In addition, this research can improve mine safety, the effectiveness of mine site inspections, and more organized repair planning, thus providing practical benefits to PT AMM's operations. [ABSTRACT FROM AUTHOR]

• Introducing a novel way to classify vehicle–pedestrian interactions at uncontrolled crosswalks. • Danish Offset design achieves better motorist yield rates than traditional refuge islands. • Reveals directional differences in motorist yield rates at crosswalks. Introduction : Pedestrian crossing safety has gained increased attention due to the high rate of pedestrian fatalities and injuries, especially at uncontrolled crosswalks. Method : In this study, we proposed a novel GIS-based method for detecting motorist yield behaviors using multi-modal trajectory data collected from LiDAR (Light Detection and Ranging) sensors at uncontrolled crosswalks. The approach classifies diverse types of motorist-pedestrian interactions and calculates motorist compliance rates, enabling us to assess the safety performance of different geometric crossing treatments. The method was applied to four uncontrolled crosswalks in midtown Reno, NV to analyze the impact of different crossing treatments, including curb extensions, pedestrian refuge islands, and Danish Offset, on motorist yield rates. Results : The findings indicated that refuge islands significantly improve driver yield rates, with further improvement observed when implementing Danish Offset designs. Among the four sites, the highest motorist yield rate (78.0%) was observed at Taylor (Danish Offset), followed by St. Lawrence (refuge island) with 71.9%. Martin and LaRue (curb extension only) exhibited lower yield rates of 57.9% and 61.3%, respectively. Practical applications : This study emphasized the importance of considering different directions when evaluating pedestrian safety at crosswalks, an aspect currently not considered in the latest Highway Capacity Manual (HCM). This research also provides valuable insights into applying multimodal all-road-user geospatial trajectory data for initiative-taking traffic safety performance evaluation of pedestrian crossing facilities at uncontrolled crosswalks and can guide future efforts in improving pedestrian safety. [ABSTRACT FROM AUTHOR]

Mapping pavement types, especially in sidewalks, is essential for urban planning and mobility studies. Identifying pavement materials is a key factor in assessing mobility, such as walkability and wheelchair usability. However, satellite imagery in this scenario is limited, and in situ mapping can be costly. A promising solution is to extract such geospatial features from street-level imagery. This study explores using open-vocabulary classification algorithms to segment and identify pavement types and surface materials in this scenario. Our approach uses large language models (LLMs) to improve the accuracy of classifying different pavement types. The methodology involves two experiments: the first uses free prompting with random street-view images, employing Grounding Dino and SAM algorithms to assess performance across categories. The second experiment evaluates standardized pavement classification using the Deep Pavements dataset and a fine-tuned CLIP algorithm optimized for detecting OSM-compliant pavement categories. The study presents open resources, such as the Deep Pavements dataset and a fine-tuned CLIP-based model, demonstrating a significant improvement in the true positive rate (TPR) from 56.04% to 93.5%. Our findings highlight both the potential and limitations of current open-vocabulary algorithms and emphasize the importance of diverse training datasets. This study advances urban feature mapping by offering a more intuitive and accurate approach to geospatial data extraction, enhancing urban accessibility and mobility mapping. [ABSTRACT FROM AUTHOR]

The aim of this work is to identify a suitable methodology capable of integrating multiscale spatial information about an historic center drawn from a Geographic Information System (GIS) and Historical Building Information Modeling (HBIM). The method is based on a multiscale development system that can efficiently handle different types of geospatial information by exploring geomatic techniques and software for semantic and parametric modeling. Our case study of the historic center of Popoli (Italy) shows the quality of the proposed model and HGIS-BIM integration for building geometric models rich in semantic and parametric information, taking into account development at different levels of detail. [ABSTRACT FROM AUTHOR]

Recent archaeological and remote sensing research in the Maya Lowlands has demonstrated evidence for extensive modification of the landscape in the forms of channeled fields and upland terraces. Scholars often assume these measures were taken primarily to intensify maize production; however, paleoethnobotany highlights a greater diversity of crops grown by the precolonial Maya. This study combines the growth requirements of 18 crops cultivated by ancient Maya farmers with lidar and other geospatial data in a suitability model that maps optimal areas for growth. These 18 crops cluster into five groups of crops with similar growth requirements. Across the study region, different groupings of crops had different suitability in and around different ancient Maya centers and agricultural features. This spatial variation in suitability reflects the heterogeneity of land resources and adaptations and contributes to existing conversations about economic and settlement organization in the study area. The results of this study serve as a foundation for future field studies and more complex spatial models. [ABSTRACT FROM AUTHOR]

Accurate three-dimensional mapping and digital twinning provides a powerful tool for effective maintenance of civil infrastructure and supports efficient future planning of new developments. Three-dimensional mapping can be efficiently performed with a Mobile Mapping System (MMS) that records geospatial data from platform-mounted sensors. However, it is expensive to continuously update datasets by re-capturing with MMS. This paper outlines a novel method allowing camera-only approaches for updating and change detection. It resolves key issues with inherent resolution differences between MMS laser scanner point-clouds and camera images. An intermediary is used to register two disparate datasets. This novel approach to synthetic camera images (SCIs) bridges the differences between MMS point-clouds and camera images and aid in coarse registration of camera images to an outdoor MMS point-cloud. SCI coarse registration precision is maximized by generating surfaces, interpolating intensity values, and reducing noise with a median filter. Landmark features coarsely register the camera image to the MMS point-cloud. The coarse registration is most precise when the whole scene is captured either from the same location as the SCI or further from the scene. Landmarks precisely detect scenes when changes are less than 20%, and foliage does not exceed 20% of the camera image. [ABSTRACT FROM AUTHOR]

Digital Earth vision (DEvision) is an initiative aiming at the integration of geospatial online and blended learning modules into curricula of multiple disciplines. The Austrian Development Cooperation with its APPEAR program managed by ÖAD therefore supports the DEvision initiative in partnership with academic institutions in Kyrgyz Republic and Armenia. Based on an extensive needs survey across multiple actors in these partner countries, learning modules on Digital Earth Basics, Geospatial Models and Representations, Geovisualisation and Communication, Remote Sensing and Image Analysis, and Spatial Analysis are under development. These modules are designed for bachelor or master-level programs according to requirements identified by partners as well as for qualification improvement training. Digital Earth and lifelong education are interconnected concepts that emphasize the use of digital technologies to support continuous learning. The synergy between Digital Earth and Lifelong Education provides access to a wealth of geographic and environmental data, which can be used for educational purposes. Digital Earth tools provide students with access to cutting-edge geospatial technology and data, which enhance traditional and introduce new ways of learning methods. The Razzakov Kyrgyz State Technical University hosted two “Digital Earth Qualification” training sessions and the feedback from the trainees was analyzed, which provided valuable insights into the effectiveness of the training programs and areas for improvement. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) is a high-accuracy data source for geospatial providers that is displayed in two dimensions (2D) or three dimensions (3D). It is used to measure the distances or 2D or 3D maps of the environment. This study examines a random sample consensus (RANSAC)- based room mapping approach utilizing LiDAR. The RANSAC is used to achieve line fitting as a solution to acquire missing or incomplete point cloud data during the process of room scanning. The maximum x-y distance is proposed to achieve a proper model to fix the missing line during the LiDAR scanning process. Data retrieval uses ground-based LiDAR located in the middle of a certain room with the dimension of 5.76×4.95 m2. To explore a room mapping, a 2D LiDAR YDLIDAR G4 with an operating frequency of 7 Hz is used. The derived raw data is then visualized with MATLAB. The results show that the RANSAC can perform line-fitting for missing or illegible LiDAR point cloud data during the scanning process due to reflection or obstacles. The increase in the amount of data used is then directly proportional to the probability of the number of correct models. [ABSTRACT FROM AUTHOR]