Abstract: This paper presents the approach proposed by UFPR for calculating a quasi-geoid model within the Colorado Experiment (CE) based on the scalar-free solution of the Geodetic Boundary Value Problem (GBVP), using Molodensky series and numerical solutions of Stokes integrals. The study area covers part of the state of Colorado, USA, where fourteen different research groups sought to model the Earth’s gravity field with centimeter precision. The model was calculated from residual Molodensky gravity anomaly values, calculated using available ground and airborne data, using the Remove-Compute-Restore (RCR) technique. Tests were conducted to identify the optimal Wong and Gore (WG) modification degree and cap size to best fit the model to the GSVS17 reference data. Analysis of the results revealed that the model had a standard deviation of 2.7 cm, an RMSE of 86.4 cm, and lower variability compared to solutions from the other 12 institutions. It was also found that the solution is significantly influenced by changes in the Wong and Gore modification degree. The study concludes that the solution proposed here is an effective alternative for calculating quasi-geoid models and for point-based geopotential modeling within the scope of International Height Reference Frame (IHRF) implementation, even in areas with complex topography.
This study aims to compute two gravimetric quasi-geoid models for the Paran & aacute; state, Brazil, called PR_QG_A and PR_QG_B. The first referenced to the International Height Reference System and the second to the Brazilian Vertical Local Datum in Imbituba. A dataset of 67,618 terrestrial and oceanic gravimetric data has been used. The Remove-Compute-Restore technique was employed in conjunction with the scalar-free solution of the Geodesy Boundary Value Problem, taking into account the zero and first-order terms of Molodenskii's series. Before computing the models, an optimal configuration in terms of transition degree/order, modification degree of Stokes' kernel, and cap size was identified. Iterative multiple pointwise solutions were calculated for GNSS/leveling co-location stations. After validating the quasi-geoid models against GNSS/leveling data, both presented a standard deviation of 0.199 m in terms of height anomaly. For additional validation purposes, the results were compared with those obtained from the SAM_QGEOID2023 model. The differences in terms of standard deviation were approximately 2 mm. However, the values can differ by up to 0.707 meters in certain regions. The computed models will be able to provide normal height values more practically and economically, representing an alternative to leveling, within the indicated accuracy limit.
Urban public space is a fundamental component of the built environment, yet it remains predominantly shaped by infrastructure for motorized transport, often to the detriment of pedestrian accessibility. Walking, however, contributes significantly to physical and mental well-being, social cohesion, and urban sustainability. In this context, walkability has emerged as a key parameter in planning strategies aimed at reducing traffic congestion, promoting health, and enhancing quality of life. This study presents and validates a novel methodological framework for assessing walkability through remote analysis of street-level imagery (SLI). The method is based on an adaptation of the Walkability Index (iCam, initially developed by the Institute for Transportation and Development Policy (ITDP)) to a virtual environment, utilizing publicly accessible Google Street View imagery. Notably, the index was expanded to include a dedicated Accessibility category, addressing the often-overlooked needs of individuals with disabilities or reduced mobility. The methodology was applied to a pilot area in Salvador, Brazil. The results classified the average walkability condition as "sufficient", with a composite score of 1.75, and confirmed through field validation (score: 1.86). Categories such as Mobility, Attraction, and Public Safety performed well, while Accessibility and Environmental Quality revealed areas that require targeted intervention. Despite limitations related to image temporality, spatial coverage gaps, licensing constraints associated with proprietary imagery, and subjectivity in interpretation, the proposed framework demonstrates high potential for cost-effective, scalable, and transferable urban diagnostics. It enables broader spatial coverage and supports periodic monitoring of pedestrian infrastructure. The findings provide actionable insights for urban planners, policymakers, and researchers committed to building more inclusive, walkable, and sustainable cities.
Spatial interpolation of categorical variables is a challenge in geosciences and other applied fields, especially when attempting to predict categories in unsampled locations. In this study, we evaluated the performance of generalized multiquadric radial basis functions (GM RBFs) in the interpolation of categorical variables, considering different parameters (a and b), local neighborhoods (k neighbors), and number of categories (2, 4, and 8). Simulated scenarios with 200 points in a two-dimensional grid were used to control the distribution of categories, allowing the comparison of 12 versions of the GM RBFs using metrics such as accuracy, mean of F1 score, and global variance (GV). The results showed that the best performance occurred for local neighborhoods (k = 10), with a parameter values close to zero. For two or four categories, b = 0 presented the best results, while for eight categories, b =-1 was more efficient. Increases in the number of categories increased GV and reduced accuracy, demonstrating greater complexity in spatial prediction. The results reinforce the importance of adjusting RBF parameters and the number of neighbors according to the context, in addition to highlighting the impact of the number of categories and data imbalance on the interpolation efficiency.
This study addresses the challenge of accurately classifying land use and land cover (LULC) changes in landscapes influenced by anthropogenic activities. By leveraging multi-temporal satellite imagery and a hierarchical multistep classification approach, we enhance the differentiation of LULC transitions, improving model accuracy and environmental monitoring. This study presents an enhanced LULC classification framework that uses multi-temporal satellite imagery and hierarchical, multistep analysis to improve the accuracy of class detection in landscapes. We compare three supervised, pixel-based classification approaches-Single-step, Sequential Binary, and Accuracy-based Binary Classification-across a case study in the Furnas Reservoir Watershed, Southeast Brazil. The Accuracy-based Binary Classification method achieved the highest overall accuracy (87.37%), outperforming the other approaches by prioritizing classes with higher classification accuracy. Seasonal composite imagery and feature-engineering, such as spectral indices and quality mosaics, improved classification precision, particularly in heterogeneous and seasonally variable landscapes. The findings underscore the importance of integrating temporal dynamics in LULC mapping to inform sustainable land management in regions undergoing rapid environmental change.
This paper presents the application of Design Thinking to the participatory development of geoinformation systems in Brazil and South Africa. The study explores how empathy-based, iterative design methods can elicit not only functional requirements but also reveal semantic and terminological variations among stakeholders with diverse backgrounds. Through structured workshops, participants collaboratively defined user needs, mapped spatial concepts, and co-designed system features for two distinct scenarios: a national land management platform in Brazil and a geospatial dashboard for Sustainable Development Goals (SDGs) in South Africa. As part of the process, a reusable script for participatory sessions and a comparative vocabulary of 91 geospatial terms were developed. Results show how shared core concepts coexist with region-specific terminologies, underscoring the need for standardisation to enhance interoperability. The study demonstrates that Design Thinking can facilitate semantic alignment and functional adequacy in multilingual and multi-institutional environments, offering replicable tools for inclusive, user-centred geospatial system design.
This study aims to optimize the planning of forest road networks using the Analytic Hierarchy Process (AHP) integrated into a Geographic Information System (GIS) environment. The variables used in this study were: permanent preservation area (PPA), slope (S), slope orientation (SO), commercial volume of timber (CV), and land cover class (LC). The best scenario was achieved when the five variables were employed in the AHP analysis, with a coherence index (CI) of 0.04%, and a consistency ratio (CR) of 3.9%. Then, the suitability of the existing forest roads was verified using the least-cost path tool associated with the map provided by the best AHP scenario. The results indicate that only 1/3 of the roads are placed in areas classified as 'good' or 'optimal', suggesting that the current road network can be improved. Adopting the road network proposed by this study, the length and density of roads would decrease by 10%. Consequently, a reduction in transport costs and an increase in the planted area are expected. We propose that this cost-effective approach can be leveraged for the reappraisal and suitability assessment of forest road density in areas exhibiting substantial timber resources, including those managed by mature forest enterprises.
For gravity field modelling, gravity disturbance inputs play a role once they are effortlessly determined through coordinates derived from the Global Navigation Satellite System (GNSS) and gravity acceleration measurements. With this anomalous quantity, the fixed GBVP can be directly solved using Hotine's function. This paper intends to present the Hotine-modified kernel using Van & iacute;& ccaron;ek and Kleuberg's (1987) approach and the software developed. This methodology uses non-gridded residual gravity disturbances as input. Normal height values were determined for the evaluation process, first using Hotine's function and second recovering the gravity potential from a geoid model determined by Least Squares Collocation (LSC). The same data was used for both approaches, and the solutions were compared in eight Brazilian stations, including one IHRS. Hotine's solutions demonstrated consistent convergence in the order of-9 cm (MGIN) and 14 cm (MGUB), RBMC stations, using the LSC method. When evaluating benchmarks, the Hotine method shows a difference of 12 cm at PPTE, 8 cm at MGUB, and 14 cm at MGIN.
Snakebites are classified as a neglected tropical disease and are associated with poverty and climatic oscillations. Accidents caused by snakes can lead to death and cause serious sequelae. This study aims to analyze the spatial distribution patterns of snakebites caused by the genera Bothrops and Crotalus in the State of Sao Paulo, between 2013 and 2022. Snakebite data were gathered from the National System of Notifiable Diseases (SINAN), cartographic and demographic data from the Brazilian Institute of Geography and Statistics (IBGE), and climatic data from the WorldClim platform. All data were organized according to the four seasons (spring, summer, fall, and winter). Spatial data analyses were conducted using ArcGIS Pro 3.4 and GeoDa 1.22, employing univariate and bivariate spatial autocorrelation techniques based on Global and Local Moran's I indices. The results revealed spatial clustering patterns for both Bothrops and Crotalus in all seasons. The main clusters for Bothrops were in the southern and northwestern regions, while Crotalus clusters were concentrated in the central, northwestern, and northeastern regions. A positive spatial autocorrelation between precipitation and Bothrops incidence rates was observed in three seasons. Non-parametric statistical tests also indicated significant seasonal differences in incidence rates for both snake genera.
Predicted Global Ionospheric Maps (GIMs) are widely used in single-frequency Global Navigation Satellite Systems (GNSS) applications to correct ionospheric delays and enhance receiver positioning accuracy. In this work, we employed the Encoder-Decoder Convolutional Long-Short Term Memory for the Next Day (ED-ConvLSTM-ND) recurrent neural network to predict next-day GIMs based on GIMs from previous days. Although the model was originally evaluated in the ionospheric map domain-by comparing the Vertical Total Electron Content (VTEC) of the predicted maps with post-processed GIMs-such assessments may not fully capture the impact on positioning performance. To address this, we evaluated whether the Ionosphere Map Exchange (IONEX) files generated by the ED-ConvLSTM-ND network improve the accuracy of single-frequency single-point positioning (SF-SPP). We compared time series generated using various ionospheric correction approaches: the Klobuchar model (BRDC), the Center for Orbit Determination in Europe (CODE) 1-day predicted GIM (C1PG), the ED-ConvLSTM-ND GIM, and the final CODE GIM (CODG), adopted as the reference ionosphere. Data from three continuously operating stations in Brazil, collected during 2015, were used. The ED-ConvLSTM-ND GIM achieved a mean absolute error (MAE) 7.7% lower than that of C1PG. ED-ConvLSTM-ND predicted GIMs obtained MAE at least 0.76 m better than Klobuchar in every station, indicating that neural networks could improve real-time GNSS positioning.
Toponyms play a crucial role in the identification and singularisation of geographic features. While traditional sources include gazetteers, official records, and historical maps, collaborative mapping platforms such as OpenStreetMap (OSM) offer a dynamic alternative by capturing local knowledge. However, validating the existence of OSM toponyms through external and up-to-date sources remains a challenge. This study proposes an automated framework for validating OSM toponyms using street-level imagery (SLI). The methodology integrates advanced computer vision and artificial intelligence techniques, combining the YOLOv11 model for text region prediction with the Keras-OCR framework for text recognition. Textual evidence extracted from SLI platforms, Mapillary and Google Street View (GSV), was analysed and compared to OSM toponyms using the Index of Collaborative Toponym Validation by Accumulated Evidence (ICTVAE), a metric designed to balance similarity and coverage in validation scores. The results reveal that SLI is a viable source for confirming the existence of OSM toponyms, with variations depending on image quality, visibility, and contextual factors. The proposed ICTVAE index effectively consolidates accumulated evidence from multiple detections, mitigating issues related to incomplete or partial recognitions. This approach provides a practical and scalable solution for validating collaborative toponyms, especially in regions where authoritative datasets are limited or unavailable.
Floods are frequent disasters in the urban areas of the Amazon region. Accurate delineation of flood extents is essential for effective disaster prevention and response; however, despite technological advancements, significant challenges remain in processing Sentinel-1 SAR (Synthetic Aperture Radar) data to produce reliable inundation maps. In 2017, the municipality of Alenquer in the state of Par & aacute; declared a state of emergency due to severe flooding, which caused substantial harm to the local population. This article aimed to analyze the potential of SAR data Sentinel-1 images in mapping flood extent and structures exposed in the urban area of Alenquer. Geoprocessing and remote sensing techniques were applied from the Google Earth Engine to obtain the flood extent mask. The area and quantification of the affected buildings and roads were conducted in QGis. The results obtained for the vertical-vertical (VV) and vertical-horizontal (VH) polarizations produced flood extents of 1.81 and 2.21 km2, respectively. Through VV polarization extension, we detected 21 buildings and seven affected roads, whereas through VH polarization, we detected 12 buildings and seven roads. The methodology proved to be efficient but the methodological reproduction in other area in other Amazonian and Brazilian cities must consider seeking support from field data whenever possible.
The NORTE (Reference Center for Space Technologies) is a project between ITAIPU Binational, UTFPR (Federal University of Technology-Parana)-Santa Helena Campus-and FUNTEF-PR (UTFPR Support Foundation). Conceived to support the implementation of the RAIB (ITAIPU Binational High Precision Vertical Network), the project aims to build classrooms/research laboratories, implement GNSS (Global Navigation Satellite System) station infrastructure, develop applied research in process improvements in geodetic surveys, operate laboratories for teaching practices and contribute to the training of human resources. The project currently manages 3 GNSS-MET (Geodesic and Meteorological sensors) stations: GUAI (Guaira/PR), ITAI (Foz do Igua & ccedil;u/PR) and STHA (Santa Helena/PR), with data used in 4 thematic axes: Vertical Reference System, GNSS Surveys, Development of Applied Solutions for Geospatial Data in GIS (Geographic Information System) and LiDAR (Light Detection and Ranging) Survey Applications. Since 2023, seven research projects have been under development, four of which will have their initial results presented: the development of a technical specification for gravimetric densification, programs for monitoring ionospheric and tropospheric activities, a geospatial data management platform and three-dimensional modeling of the hydroelectric plant based on LiDAR data.
Geographical challenges have long influenced the development of geometry. Ancient Greek mathematicians like Thales and Ptolemy were also geographers, and later, geometers such as Gauss and Laplace made key contributions to mapmaking. Since the Earth is approximately spheric, no map can perfectly preserve all geographic properties, making map projections essential. These involve two steps: scaling the Earth's shape (sphere or ellipsoid) and transforming it onto a flat surface (plane, cone, or cylinder). However, all map projections introduce distortions and can be classified into various types, including conformal, equal-area, equidistant, and others, depending on the method and orientation used. The Mercator projection, a conformal cylindrical projection, revolutionized navigation by preserving angles and directions. Its variant, the transverse Mercator projection, introduced by Lambert in 1772, that did not use colatitudes, rotates the Mercator projection to align with a central meridian, minimizing distortions in nearby regions. This paper derives the transverse Mercator projection equations on a sphere from the Mercator projection equations using colatitudes, offering a rigorous yet pedagogically valuable formulation. It fills analytical gaps often left in classical literature, validates its results by deriving the classical equations, and presents a practical application using 5,556 geographic points, confirming no significative differences.
Threshold selection plays a crucial role in detecting complex and irregular surface features, such as the Swiss Cheese formations found in the south polar region of Mars. This study aims to evaluate and compare the performance of manual and automatic thresholding strategies for detecting Swiss Cheese features. The automatic strategies tested include adaptive thresholding (with average and median-based variants), Otsu's method, and multilevel thresholding, all integrated into a detection workflow based on digital image processing and mathematical morphology. These approaches were applied to orbital images with spatial resolutions of 0.25 m and 1.5 m. Manual thresholding achieved the highest precision (97.59%) and overall quality (83.80%). Among the automated strategies, multilevel thresholding and Otsu's method yielded the best results, with multilevel thresholding reaching 87.29% precision and 28.38% quality, while Otsu's method reached 78.39% precision and 30.91% quality. These findings highlight the challenge of defining a global threshold due to illumination variability, contrast differences, and the irregular morphology of the Swiss Cheese formations. The results support the development of a more systematic and reproducible workflow for planetary surface analysis.
UFPR CampusMap (UCM) is a web-based Geographic Information System (WebGIS) that provides information about the campuses of the Federal University of Paran & aacute;, both indoors and outdoors. Previous research revealed that the user experience when using UCM on mobile devices is not satisfactory. To address this gap, this research proposes a version of UCM's interface, specially designed for mobile devices, following the mobile-first concept. The methodology used followed an iterative and cyclical process of requirements engineering combined with the design thinking approach to problem-solving. This methodological strategy enabled the efficient use of time and resources, while actively engaging key stakeholders throughout the process, including both system developers and end users. The obtained results include a requirement document that details the functional and nonfunctional requirements of the system, as well as a high-fidelity prototype of the system interface. This study highlights the significance of applying mobile-first design in a WebGIS context, laying the groundwork for future usability testing and improved user satisfaction.
Advances in geoprocessing techniques and geospatial data manipulation have optimized natural resources and enhanced environmental services globally. The agricultural sector, traditionally associated with intensive land use, is now benefiting from these technologies, leading to improved productivity aligned with better environmental conditions. Mechanization in agriculture is crucial for optimizing processes like soil preparation, planting, and harvesting. This study introduces a geoprocessing-based methodologyto create a mechanization indexfor agricultural production by integrating land slope, land use, and soil classes using Digital Elevation Models (DEMs) and publicly available spatial data. Applied in Rio de Janeiro State, Brazil, a region with diverse altimetry and land use, this workflow uses open-source tools (QGIS) and Python. The results highlight the potential for expanding mechanizable areas and can guide public and private initiatives. Suitability for mechanization was determined for 7936.82 km2, or 18.12%, and 5720.84 km2, representing 13.06% of Rio de Janeiro's territory, depending on, respectively, SRTM and RJ25 data resolution and accuracy.
The Land Administration Domain Model (LADM) standardizes land management by integrating legal, spatial, and administrative information. This study examines LADM-related research using Structural Topic Modelling (STM) on 199 publications (2008-2024). Seven dominant topics emerged: land administration systems, property valuation, 3D cadastral modelling, LADM extensions, building and spatial rights, cadastral systems, and land object modelling. Key findings highlight sustained interest in spatial modelling, legal frameworks, and cadastral data integration, alongside emerging trends such as country-specific LADM profiles (e.g., China, Kenya, Malaysia) and technological advancements like BIM and marine georegulation models. Challenges persist in data complexity, semantic interoperability, and 4D cadastres. The study recommends expanding semantic models, fostering interdisciplinary collaboration, and developing tailored national profiles to enhance LADM’s applicability and promote sustainable land management practices globally.
Updating maps in Brazil is hindered by considerable obstacles, primarily to the high costs associated with it and the difficulty of accessing the regions. Moreover, the accelerated rate of environmental transformation, particularly in rural settings, represents an additional challenge. This study proposes to use high-resolution satellite images from Planet constellation, in conjunction with artificial intelligence, specifically UNet, to automatically identify rural roads in the metropolitan region of Curitiba, Paran & aacute;, Brazil. The objective is to identify the optimal parameters for automating the detection of rural roads. The UNet, with its distinctive U-shaped architecture, is highly effective in segmenting and detecting targets while simultaneously preserving the feature maps in each convolution. In this study, the network was trained on satellite images containing rural roads, resulting in segmented maps with an encouraging 91.95% accuracy in road detection. Nevertheless, further improvement is possible, as evidenced by the method's precision of 75.83% and F1-Score of 69.07%. These outcomes indicate the possibility of enhancement through the expansion of the training dataset, thereby better addressing the network's recognition constraints. One potential avenue for optimizing detection using the methodology would be the incorporation of supplementary training samples, which could potentially mitigate the network's recognition limitations.
The term City Information Modeling - CIM is a recent concept that encompasses, among other aspects, the integration between Building Information Modeling and Geographic Information Systems. CIM has been predominantly applied in cities; however, since the environment of a university campus possesses characteristics similar to a municipality, CIM can be a promising tool for managing these institutions. This paper proposes a data organization model for CIM, based on CityGML extensions. A methodology was proposed for the creation of extensions that can be applied to activities requiring spatial and building data. As an example, the activity of developing a technical project for fire and disaster prevention was used, in the environment of the Federal University of Paran & aacute;. To create the method, the available data and its format were studied, as well as its geometric and semantic correspondence with CityGML. The result was a conceptual model for the application domain extension (ADE) of CityGML for the development of firefighting projects, referred to in this work as ADE_FirePrev.
