Abstract in English:

Abstract: Tomatoes are widely cultivated, both by family farmers and corporate producers. During the tomato growth cycle, several diseases can affect the plant. The identification of these diseases through short-range images is significant, and computer vision techniques are commonly used to identify diseases in plant leaves. In this paper, a hybrid model that combines a convolutional neural network (CNN) and a Random Forest (RF) decision tree is used for foliar spot detection in tomato leaves. High-level features learned and extracted from CNN are used as input for the RF classifier. To evaluate the proposed model’s performance for plant disease identification, a case study of 2480 low-cost digital RGB images collected in actual field conditions, under different intensities of light exposure, were used, including healthy tomato leaves and leaves with visible symptoms of powdery mildew fungus, which attacks the tomato leaf. The results were compared with six conventional machine learning classifiers: Logistic Regression (LR), Linear Discriminant Analysis (LDA), K- Nearest Neighbors (KNN), Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). The results show that the proposed model outperformed conventional classifiers, reaching an accuracy of 98%. The results highlight the importance of fusing models to improve the detection plant´s diseases.

Abstract in English:

Abstract: Positional accuracy of cartographic products is typically evaluated using positional discrepancies and point-based techniques. However, using linear features has some advantages over the point-based method, such as a greater amount of geometric and positional information and the fact that approximately 80% of the features on a cartographic basis are lines. Despite these advantages, important parameters for evaluating accuracy using lines have not yet been established or determined, such as the spatial distribution pattern, although it is a relevant factor that can affect the results and determine the validity of an evaluation process. This study proposes a method based on the modification of the Nearest Neighbor Method for points, which can be used to evaluate the spatial distribution pattern of linear features. Instead of the traditional Euclidean distance used by the method for points, the method proposes using the Hausdorff Distance as a measure of the spacing between lines. The proposed method, called Nearest Neighbor Method for Linear Features (NNMLF), was applied to simulated and real data. All experiments with simulated data showed that the NNMLF was effective in estimating spatial distribution pattern up to the third order. Its use on real data showed NNMLF is simple to apply.

Abstract in English:

Abstract: This study aimed to evaluate the performance of eight digital elevation models (DEMs), i.e., one digital terrain model (DTM) obtained from airborne P-band radar (BCDCA DTM), one digital surface model (DSM) obtained from airborne X-band radar (BCDCA DSM) and six DSMs from orbital sensors (AW3D30, ASTER GDEM, Copernicus DEM, NASADEM, SRTM, Topodata), for the morphological characterization of the floodplains of Amapá (Brazil). All DEMs were resampled to the same mesh size and compared by visual and statistical analysis in terms of elevation and slope. The comparison demonstrated that the DTM obtained from P-band radar images was the most consistent one in representing the landforms, as it is less sensitive to vegetation. The behavior of the automated hydrographic network extraction was also analyzed, showing that even the DTM was not able to detect drainage lines across flat landscapes with centimeter elevation variations. As the comparisons were made with a common 30 m grid, the conclusions are limited to this scale and the effect of a change of scale is discussed. In view of the difficulty of automatically extracting the network in a plain, the possibility to reduce the modelling to a 2D approach, based on external hydrographic data, is also discussed.

Abstract in English:

Abstract: Groundwater resources, a crucial water supply in the Sahel regions of northern Africa, have to be evaluated to ensure its sustainability. In the last decade, Lake Chad Basin (LCB), experienced extreme variabilities in the surface water, which impacted groundwater levels as studied by different authors. Consequently, evaluating the modes of variability of each compartment of the terrestrial water storage (TWS) plays an essential role in LCB water resources management. Therefore, the main objective of this study is to invert groundwater storage (GWS) over LCB using surface water storage (SWS) derived from satellite altimetry and imagery, soil moisture storage (SMS) from the Global Land Data Assimilation System hydrological model (GLDAS), and TWS inferred from Gravity Recovery and Climate Experiment (GRACE) measurements. Here we apply a proper signal separation approach to extract GWS from TWS, which is taken as observations in a least-squares sense to reconstruct GWS. The analysis of the reconstructed GWS series using correlation coefficient, root-mean-square error, and the Nash-Sutcliffe efficiency, shows an improvement of 114%, 26%, and 147%, respectively, compared to the GRACE-based results. These findings could be crucial in the surface and sub-surface water management over LCB.

Abstract in English:

Abstract: Urban infrastructure element detection is important for the domain of public management in large urban centres. The diversity of objects in the urban environment makes object detection and classification a challenging task, requiring fast and accurate methods. Advances in deep learning methods have driven improvement in detection techniques (processing, speed, accuracy) that do not rely on manually crafted models, but, instead, use learning approaches with corresponding large training sets to detect and classify objects in images. We applied an object detection model to identify and classify four urban infrastructure elements in the Mappilary dataset. We use YOLOv5, one of the top-performing object detection models, a recent release of the YOLO family, pre-trained on the COCO dataset but fine-tuned on Mappilary dataset. Experimental results from the dataset show that YOLOv5 can make qualitative predictions, for example, the power grid pole class presented the mean Average Precision (mAP) of 78% and the crosswalk class showed mAP around 79%. A lower degree of certainty was verified in the detection of public lighting (mAP=64%) and accessibility (mAP=61%) classes due to the low resolution of certain objects. However, the proposed method showed the capability of automatically detection and location of urban infrastructure elements in real-time, which could contribute to improve decision-making.

Abstract in English:

Abstract: Mapping the submerged bottom is a hampered task when traditional vessels are inserted in shallow places that pose a danger to navigation. In this sense, current research has sought optical remote sensing to obtain bathymetry estimates in larger locations in less time. However, most studies employ a large sample of bathymetric points to estimate depth with orbital images. The need for a large amount of random bathymetric points can make these procedures less viable and unattractive. Thus, the present work proposes a methodology to estimate bathymetric depths from orbital images using sections of bathymetric points previously spaced in the study area, avoiding the need for a high amount of points collected through traditional bathymetric surveys. This work also compares this methodology using the NDWI index and the ANNs. Furthermore, the study showed that the points contained in the sections are quite efficient for extracting bathymetry with orbital images, especially through the implementation of neural networks, achieving a volume estimation accuracy within 4% of the actual volume of the reservoir in question.

Abstract in English:

Abstract: Augmented Reality (AR) seamlessly integrates the real environment with virtual objects, enriching users’ information perception. The application of AR in personal and vehicular navigation tasks facilitates the evolution of navigation systems from two-dimensional (2D) to three-dimensional (3D) representations, incorporating an egocentric viewpoint. Spatial knowledge, specific navigation objectives, navigational skills, and the navigational symbols plays pivotal roles in map utilization, encompassing self-localization, proximity, navigation, and event awareness. The representation of symbols in navigation tasks relies on static and dynamic or animated visual cartographic variables. In this context, our research focuses on evaluating symbols used for depicting routes and landmarks in AR systems during personal navigation tasks. The experiments investigated the representation of routes, exploring variations in route line thickness, the presence or absence of border-color, fill color, and the speed of arrows moving along the route line. The findings of the study reveal that thinner route lines significantly contribute to enhance perception of surroundings, blue fill color exhibit superior performance compared to traditional navigation system colors, and volunteers noted that the arrow animation speed noticeably affected their perceptions of elements within the scenario. These insights contribute to understanding of how visual variables influence user preferences and experiences in AR-based navigation tasks.

Abstract in English:

Abstract: The determination of the position and velocity of the GLONASS user’s begins with the GLONASS satellite’s orbit computation. In this paper, using the positions and velocities of GLONASS satellites given in the broadcast ephemerides file every 30 minutes as initial conditions, we study the effect of the order and the step size in the integration of the differential equation of satellite motion by the Runge-Kutta method to get the positions and velocities of GLONASS satellites at any time. This method consists of several orders, including order four recommended in the GLONASS Interface Control Document; order five; and order four and five. These three orders are tested in this study using two distinct step sizes (1 sec and 0.5 sec). In terms of the differences obtained between the forward (+15 min) and the backward (-15 min) integration processes and the runtime, order four is the most suitable for the determination of GLONASS orbits compared to the other orders employed in this study. The data used is the positions, velocities, and luni-solar acceleration of all GLONNAS satellites on April 20, 2021, given in the broadcast ephemerides file.

Abstract in English:

Abstract: Map evaluation is a fundamental and challenging aspect of cartography. This is especially true for digital maps that can be accessed via mobile devices (hereafter referred to as «mobile digital maps»). Researchers often use a combination of methods adapted from computer science to evaluate these maps in order to obtain comprehensive and reliable information. However, the evaluation of digital maps is a complex task that requires a multidimensional approach to understand the different perspectives and needs of users in different usage contexts. By analyzing scientific articles, this study aims to explore and identify the most commonly used methods for evaluating mobile digital maps. The goal is to provide an analytical review of the methods, test configurations, and application locations of mobile digital map evaluations. For this research, 195 scientific articles published in international journals between 2010 and 2023 were evaluated. Of these, 53 articles were selected, all of which dealt with the evaluation of digital maps accessed by mobile devices. The study answers the question of which methods are recommended depending on the context. The types of data collected cover both qualitative and quantitative aspects, while the evaluations tend to be moderate in nature.

Abstract in English:

Abstract: Pan-sharpening (PS) consists of combining a high spatial resolution (HR) panchromatic image (PAN) and a low spatial resolution (LR) multispectral image (MS) to generate an MS-HR image. However, some PS methods have spectral and spatial distortions that influence subsequent analyses. Thus, this study aimed to develop a PS method based on convolutional autoencoder (CAE) for Landsat 8 images and evaluate its performance in calculating spectral indices. In the PS process, we trained a CAE network and used a multiscale-guided filter. The performance of the proposed method was analyzed using the Kolmogorov-Smirnov (K-S) statistic of the empirical cumulative distribution function (eCDF) between the values of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Moisture Index (NDMI) of the PS and MS-LR images. The results show that the proposed method is effective for calculating the indices. Therefore, we conclude that it has great potential for preserving the spatial information of the PAN image and the spectral information of the MS-LR image during the PS process for calculating spectral indices.

Abstract in English:

Abstract: This research aims to evaluate the preference for cartographic symbol designs that depict urban buildings for topographic maps at a 1:10,000 scale, to define symbols that may represent part of the western urban landscape of the state of São Paulo. Three proposals were designed to represent buildings: Pink visually coherent with the national cartographic standards; Orange visually coherent with the urban landscape of the western portion of São Paulo state; Gray visually coherent with the digital navigational maps used frequently. Map evaluation considered the preference for the symbol designs between architects and non-architects. The Orange design was the most preferred in all aspects. Despite that, Orange and Pink designs did not present statistically significant differences, but Gray was the least preferred. Furthermore, from the statistical analysis, map users with a background in architecture and those with other backgrounds could be considered as part of the same population.

Abstract in English:

Abstract: Multipath significantly degrades GNSS positioning quality, especially in smartphones due to their inferior GNSS antennas. To mitigate this, the Laboratory of Space Geodesy and Hydrography (LAGEH) developed the Multipath Effect Attenuator (AEM), which was used for the first time in smartphone applications. A new prototype, AEM Smart, was also created for use in smartphones. The smartphone used in this research was the Xiaomi Mi 8, with a dual frequency GNSS sensor and allows access to raw GNSS data. The multipath effect was evaluated based on the MP1 and MP5 indices, referring to the L1 and L5 carrier phases, respectively. The positioning quality was assessed using Precise Point Positioning (PPP) GNSS data processing. This contribution shows that L5 is significantly superior to L1 measurements, with more than 73% improvement in multipath effect suppression. Furthermore, this study indicates that attenuators can reduce the multipath effect in L1 by more than 24%. Also, a strong correlation of about 94% was found between the multipath effect on L1 and the 2D and 3D positional accuracies. Finally, through the PPP technique in combination with the Xiaomi Mi 8 and AEM 3, it was possible to obtain a 2D and 3D accuracy of approximately 0.24 and 0.60 m, respectively.

Abstract in English:

Abstract: This research study deals with improving the network cadastre of a sanitation company through the inclusion of three-dimensional data in the geographic database, which can represent the embryo of a 3D cadastre. To investigate the collection and inclusion of the depth of underground pipelines, a methodology was tested for obtaining 3D data using the Ground Penetrating Radar - GPR, regarding the triplication project of highway BR-232, located in Recife-PE. The study noted that the GPR survey also had some limitations, due to the pedological, geological and topographic characteristics of the site, and that the electromagnetic properties of the soil and minerals also need consideration, as well as the characteristics of the terrain to carry out the displacement of the equipment on the ground. The results demonstrated that, despite the limitations found in the GPR survey, where there were sections in which it was not possible to identify the pipelines, for the application analyzed and for the tested study area, the use of georadar in obtaining pipeline depth data was considered satisfactory.

Abstract in English:

Abstract: Soil moisture is a crucial parameter in hydrology and meteorology, impacting water management and plant survival. Traditional methods of retrieving soil moisture, such as gravimetric, lack spatial-temporal variations and are labor-intensive. Remote sensing offers global coverage but suffers from low spatial resolution. In the past decade, a new method, Global Navigation Satellite Systems Interferometric Reflectometry (GNSS-IR), has been developed. It provides continuous soil moisture measurements with improved spatial and temporal resolution but is yet to be employed in Tanzania. This study processed a total of 21 Continuously Operating Reference Stations (CORS) stations available in Tanzania, installed from various scientific projects since 2005. The suitability of these stations for the GNSS-IR method was evaluated based on the reflecting surface and the presence of an L2C signal capable of penetrating ~5 cm below the soil. The evaluation leaves only one site (OLO6), suitable for soil moisture determination. One year of daily L2C Signal to Noise Ratio (SNR) data for OLO6 was processed using the GNSSREFL software. The results were validated using Soil Moisture Active Passive (SMAP) satellite data. The GNSS-IR method showed good agreement with SMAP mostly during the dry season, with RMSE 0.038 cm³/cm³ and SDD 0.036 cm³/cm³, compared to the wet season (when soil moisture is above 0.25 cm³/cm³). Despite its limitations during the wet season, this study provides valuable insights into the GNSS-IR method for soil moisture estimation in Tanzania.

Abstract in English:

Abstract: With the rise of high-rise residential buildings in urban areas, effective maintenance management is increasingly crucial. This study introduces a 3D-STBMM model that integrates 3D visualization and temporal analysis to enhance maintenance management in Malaysian high-rises. The model aims to improve decision-making, problem-solving, and resource utilization. Using digital reconstructions of maps and maintenance records from January 2020 to January 2024, the study uncovers patterns in the timing and frequency of civil and electrical maintenance activities. Civil tasks peaked in 2022 before declining, while electrical tasks showed a consistent pattern with a decrease in 2023. Spatial analysis highlights the prevalence of these activities across maintenance spread and frequency. The integrated model demonstrates potential to transform maintenance practices, enabling a data-driven, strategic approach that enhances the performance and sustainability of urban high-rise buildings.

Abstract in English:

Abstract: The Story Map Of Evandro Case - Development And Creation Of An Interactive Cartographic Narrative Story maps allow us to present new perspectives on stories, providing a broader understanding of the events and places involved. This approach allows users to navigate time and space, connecting emotionally with the narrative. This paper presents an innovative approach to designing and developing interactive story maps, drawing on agile methodologies widely used in software development and adapting them for map projects. The study examines the process of creating an interactive Story Map for the ‘Evandro Case’, a famous criminal case in Brazil, as a central narrative theme. The methodology employed combines traditional cartographic principles with modern storytelling techniques. This approach enhances maps’ informational value and establishes a more profound emotional resonance with users. The paper highlights the importance of user immersion, advocating for future studies to include user testing and statistical analysis to validate usability and effectiveness. Through the case study of the Evandro Case, the paper demonstrates how story maps can transcend conventional map design, offering fresh insights into narratives. It argues that story maps are a versatile tool applicable to various themes beyond criminal cases. The study concludes that the fusion of spatial representation with narrative elements can create compelling and informative visual narratives, making complex stories accessible and engaging to a wide audience.

Abstract in English:

Abstract: Ionospheric scintillation affects GNSS signals that provide many essential services, making the monitoring of scintillation an important issue. This work presents a system for the real-time acquisition, generation and online dissemination of the S4 scintillation index maps covering Brazil using data from two major networks of GNSS monitoring stations, LISN and INCT. The maps are made using an innovative pre-processing and interpolation scheme. The system is already implemented and tested, being composed of a single real-time server with a database and modules that perform reception from the GNSS stations, data processing, and the online dissemination. All these tasks are executed asynchronously in a pipeline manner using the database as a central hub without any loss of data. The challenges that must be overcome to have real-time capability were: (i) to configure GNSS stations to send S4 data to a real-time server able to (ii) receive S4 data from the GNSS stations, (iii) generate sequences of S4 scintillation maps, and (iv) make these maps available in a web server. The implemented system was able to acquire data from all available stations of the monitoring networks, being robust concerning interruption of connections or different processing times of the tasks.

Abstract in English:

Abstract: This study investigates the influence of geodetic network configuration, stochastic model, and the approach local or global on the determination of minimum detectable displacements (MDD) using sensitivity analyses and significance tests. The proposed approach integrates sensitivity characteristics to establish confidence regions based on MDD. In addition, we examine the equality between the critical value of a significance test and the non-centrality parameter derived from a chi-square distribution to compute concentric ellipsoids representing sensitivity and accuracy. The analyses were focused on evaluate how variations in network configuration, stochastic model, and the type of analysis (if global or local) affect the relationship between sensitivity and accuracy. Our results showed the importance of considering these factors, providing valuable insights for robust network design and analysis in practical applications.

Abstract in English:

Abstract: The Brazilian Pantanal biome, known for its rich biodiversity and wetlands, is experiencing frequent and destructive fires. Detecting and monitoring burnt areas is vital for comprehending their present ecological condition, a key indicator for climate change and protective measures. Optical remote sensing methods, traditionally used in fire mapping, have limitations due to atmospheric conditions. Microwave Synthetic Aperture Radar (SAR) is a promising alternative, excelling in challenging environments and demonstrating sensitivity to surface properties. This study aimed to assesses the potential of SAR images for detecting burnt areas in a conservation unit inserted in the Brazilian Pantanal after intense fires in 2020. For this, the Normalized Burn Ratio (NBR) index was calculated from Sentinel-2 images before and after fire, and then the difference between these images (dNBR). Differences in backscatter coefficients of pre- and post-fire SAR/Sentinel-1 images in the two polarizations (dVH and dVV) were also calculated. To detect burnt areas, the three differences were classified using the Random Forest algorithm. The results showed adequate coincidence of burned areas between dVH and dVV compared to dNBR and high accuracy values of the algorithm model, indicating consistency between SAR and optical data in identifying burnt areas.

Abstract in English:

Abstract: The evolution of Global Navigation Satellite System (GNSS) positioning has greatly benefited several areas of knowledge. For Hydrography, an application improved by this science is the measurement of sea level oscillations resulting from tides. However, to satisfactorily retrieve this information, it is necessary to use low-pass filters (LPF) to match high frequency signals resulting from variation of the vertical component of the GNSS positioning to those of low frequency that characterizes tidal waves. Currently, there is a wide variety of LPF, which are selected according to the required purpose. Thus, the objective of this study is to obtain tidal height variations with high accuracy by applying LPF in GNSS positioning vertical coordinates tracked by an onboard GNSS receiver. For this purpose, field research and the processing of obtained data was performed. Then, two data filters were tested: the Simple Moving Average (SMA) Filter and wavelet compression. In both options, the results reached centimetric accuracy when compared to the real tide in the region of study. However, through quantitative and qualitative evaluations, it was verified that the SMA filter was considered more advantageous because, in addition to its high accuracy, it has a simpler application and less expensive in computational terms.

Abstract in English:

Abstract: The use of the ellipsoidal heights of a vessel for measuring the tide allows the diminution of uncertainties of sounding reduction in hydrographic surveys. In this article, the Precise Point Positioning (PPP) and Post-Processing Kinematic (PPK) methods were evaluated by comparing them with data from a tide gauge station while the boat remained moored nearby. When traversing sounding lines, the PPK solution was used as a reference to validate the PPP solution. In all, 12 different periods of surveys were analyzed, distributed throughout the year 2021, in the Guanabara and Ilha Grande Bays, in Rio de Janeiro. The results showed that in comparison with the tide station, when the vessel was moored, the PPK was able to fulfill the strictest criteria (Exclusive Order) of the IHO (International Hydrographic Organization) in 100% of the surveys, while the PPP in 50%. Regarding the use of the PPK as a reference, with the boat both moored and sailing, the PPP met these criteria in 67% for Exclusive Order, 25% for Order 1A and 8% for the 1B. Reducing the vertical uncertainties of hydrographic surveys in shallow waters is useful for all marine and coastal vertical positioning applications, such as integrating terrestrial and marine vertical references.

Abstract in English:

Abstract: The socio-environmental impacts caused by the collapse of a mining dam can be irreversible. In Brazil, the dam collapse at the Córrego do Feijão Mine was considered one of the worst disasters in the country. Remote sensing-based approaches have been used to detect and monitor areas affected by tailings from dam rupture. Therefore, it was proposed to identify the area affected by mining tailing mud, in Brumadinho, Minas Gerais State, through the analysis of three different spectral indices: Normalized Difference Vegetation Index (NDVI), Ferrous Minerals Ratio (FMR) and Clay Minerals Ratio (CMR). These indices were computed from the Operational Land Imager (OLI) images of Landsat-8. Different thresholds were tested to define the best range for delineating the affected area. For validation, the limits of the affected area, obtained from a higher resolution sensor, GeoEye-1, were used as reference. The methodology demonstrated great potential for detecting areas affected by dam failure. The indices NDVI and FMR delimited the area of interest with high performance, with precision varying between 95% and 92%; recall between 88% and 87%; F-score between 91% and 89%; and global accuracy between 84% and 80%, showing to be suitable mapping such disasters.