Resumen en Inglés:

Abstract: Spatial effects are intrinsic to studies on travel behavior, including modal choice problems. Among spatial dependence and heterogeneity, the resulting spatial variation of estimated coefficients is one of the most significant gains in local spatial models. Thus, this article aims to analyze a modal choice problem, considering spatial dependence and variability simultaneously. Additionally, a test is proposed to evaluate and validate the spatial variability, obtaining disaggregated results. A database adapted from the household origin-destination survey was used, which was carried out in 2007/2008 in the city of São Carlos - São Paulo, Brazil. The proposed spatial variability test uses the estimated parameters of the GWLR model (main database and 200 spatially randomized databases), compared to the confidence intervals of the coefficients in the non-spatial logit model for the spatial variability hypothesis. The results of the proposed test are similar to the reference test in the case study. The disaggregated results can be used to verify if there are certain subgroups that are more likely to be spatially stationary and if these groups exhibit a spatial pattern. Moreover, it can be observed that the local spatial model provides a better fit and estimates when compared to the non-spatial model.

Resumen en Inglés:

Abstract: The present paper addresses the relevance of incorporating terrain data for analyzing satellite images in mapping land use and land cover in the Cerrado biome. Assuming that terrain influences the dynamics of landscape changes, the present investigation evaluates three machine learning algorithms: Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) in a watershed with significant topographic heterogeneity. The present study evaluated variations in image classification using Sentinel-2 satellite data. It also included a composite analysis blending Sentinel-2 data and information derived from the Shuttle Radar Topography Mission (SRTM). The results indicate that SVM exhibited the best performance, both with and without terrain data. Although DT demonstrated satisfactory results, the performance was inferior to SVM. However, DT´s significantly shorter processing time presents an advantage in scenarios involving large territorial extents or computational constraints. Conversely, RF had a processing time similar to DT but recorded the lowest statistical indices among the three algorithms. Additionally, including the data cube containing elevation data and its derivatives yielded improved land use and land cover classification results for all evaluated algorithms compared to images without terrain data. This demonstrates the robustness of the process and the significant improvement in the quality of the final product.

Resumen en Inglés:

Abstract: 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 Paraná. 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.

Resumen en Inglés:

Abstract: 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, Paraná, 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.

Resumen en Portugués:

Abstract: 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.

Resumen en Inglés:

Abstract: 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 methodology to create a mechanization index for 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.

Resumen en Inglés:

Abstract: UFPR CampusMap (UCM) is a web-based Geographic Information System (WebGIS) that provides information about the campuses of the Federal University of Paraná, 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.

Resumen en Inglés:

Abstract: 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.

Resumen en Inglés:

Abstract: 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 Pará 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.

Resumen en Inglés:

Abstract: 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.

Resumen en Inglés:

Abstract: 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 Iguaçu/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.

Resumen en Inglés:

Abstract: 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.

Resumen en Inglés:

Abstract: 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.

Resumen en Inglés:

Abstract: 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.

Resumen en Inglés:

Abstract: 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 Vaníček 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.