Lithological characterization plays a crucial role in geological studies, and outcrops serve as the primary source of geological information. Automatic identification of lithologies enhances geological mapping and reduces costs and risks associated with mapping less accessible outcrops. These outcrops are typically imaged using remote sensing techniques, enabling the identification of geological structures and lithologies through computer vision and machine learning (ML) approaches. In this context, convolutional neural networks (CNNs) have significantly contributed to lithological characterization in outcrop images. Recent advancements include novel architectures based on residual and attention blocks, which improve upon base CNN models. In addition, transformer-based architectures have surpassed CNNs in various tasks. Taking a step further, we propose a novel architecture that incorporates Fourier operators. Our proposed architecture builds upon the Transformer model, utilizing a sequential combination of Fourier neural operators (FNOs) and channelwise self-attention layers. To train our model, we adopt a transfer learning strategy, initially training it on a texture dataset with 47 classes. Subsequently, we fine-tune the same model to classify five specific lithologies in our custom dataset. These lithologies include sandstone, gray and brownish-gray shale, limestone, and laminated limestone images from the Tres Irmãos quarry within the Araripe Basin—an outcrop analogous to oil exploration reservoirs. The proposed architecture achieved an F1-score of up to 98%, performing better than reference CNN and ResNet models. This advancement holds promise for accurate lithological characterization, benefiting geological research and exploration efforts.

Virtual reality systems have been extending their usability in many areas that could not be contemplated before. One of these fields is paleontology, which can now use virtual reality to build realistic models of real fossils with many goals, including preservation of originals, optimized visualization, or even for restoring missing parts. In this paper we present an immersive system where a entire scenario is reconstructed using digital photogrammetry and Mosis LAB application. The system has shown useful for many applications from touristic purposes and paleontological studies, since the immersive system provides an increased sense of manipulation, as well as the possibility of detailed inspection both inside and outside structures.

The Mean Tropospheric Temperature (Tm) is important for the sake of the conversion between Zenith Path Delay (ZPD) to Precipitable Water Vapor (PWV) because of the proportionality constant that takes Tm to be calculated. In Brazil, Tm modeling’s been traditionally performed using Multiple Linear Regression Models (MLR). However, recent works suggest the use of Deep Learning methods to model Tm values. In this work, we propose models based on Convolutional Neural Networks (CNN) using radiosonde data from 1961 to 2010. The results show that CNN models can outperform the traditional methods considering different statistic metrics like R, standard deviation, and RMSE.

Most fracture properties, such as orientation and density, are acquired by interpreting data obtained at the wellbores, while fracture properties between wells are typically derived from seismic data. However, this information is sparse or has low resolution leading to the study and analysis of outcrops. The data acquisition in outcrops is facilitated by its 3D representation in Digital Outcrop Models (DOM) obtained from LiDAR and Photogrammetry. It also allows virtual interpretation and fracture detection. In either case, the 3D fracture data need to be clustered in fracture sets to allow the statistical analysis necessary to upscale and resample the DFN. This clusterization is carried out by methods like k-means and fuzzy, however with caveats, like the prior definition of the number of clusters. In this work, we propose the use of agglomerative hierarchical clustering to cluster 3D fracture data obtained by a KD-Tree segmentation algorithm. Results showed that when compared to k-means, the proposed method presented more compact clusters and balance when considering Fisher’s statistics of dispersion.

Hyperspectral images often have low spatial resolution due to the sensor sizes required to capture the required spectral response. Super-resolution (SR) techniques try to mitigate this by injecting more detail in the upscaled image, either with numerical methods or deep learning and Convolution Neural Networks. In the deep learning methods, the models learn image details by inferring a high-resolution (HR) image from a synthetic low-resolution (LR) image that simulates the natural degradation of sensors by applying resampling (to reduce the image detail) and noising (to add small errors and interference). Often disregarded in the literature, the resampling method applied to generate the synthetic image can impact greatly the deep learning model training. This work, evaluate several resampling techniques to measure this impact using the Harvard hyperspectral dataset. Results showed that the Lanczos filter was the best among eight other resampling methods. The Nemenyi and Friedman ranking statistical tests also indicated that the Cubic-Spline, Bicubic, and RMS achieved good results.

Improvement in spatial resolution of remote sensing images will always be a hot-topic since the sharpening of image objects is mandatory for many appications. Even thought image instruments have received great improvements in the recent years, super-resolution techniques are welcome to increase even more the level of quality of the data for further interpretations. In this paper we present a multi-frame super-resolution approach that uses a set of UAV images acquired at the same spot, but with slight different perspectives caused by random fluctuations. The small off-sets between consecutive pixels of the low spatial resolution images are considered at subpixel level to feed an arithmetic system of equations able to produce high resolution pixels, which are the unknowns of the system. The results are soundness and could show visual enhancement. However, further developments is need to in deep undertanding and possible advancement of the designed approach.

The Precipitable Water Vapor is an important quantity for both meteorological and GNSS satellite-based positioning applications. Its quantification is closely related to the mean temperature of the tropospheric vertical column along the height, which is often obtained by using radiosonde database-based models. However, radiosonde profiles are generally not available when acquiring satellite-based positioning data. Usually, the mean temperature model is adopted to overcome this problem. The mean temperature models have been developed on the basis of the application of the classical linear regression. In that case, radiosonde profiles-based mean temperatures are linearly related to pressure and temperature surface measurements. Here, on the other hand, we purpose to use machine learning algorithms to model the mean temperature, namely Long-Short Term Memory Neural Networks (LSTM) and Random Forest Regressor (RF). For this study, we use a dataset with 30 years of radiosonde observations over the Brazilian region. In general, the results are consistent with those provided in the literature [1].

The study of outcrops is one of the main ways of carrying out research in geology, because it offers direct analysis on the behavior of rocks. Thus, the study of analogous outcrops has been used by the oil industry, as they can be associated with other data and offer a more effective analysis in geological modeling of reservoir rocks. With the advancement of technology in recent years, the use of virtual environments has been gaining more space in geosciences, as they allow the user to analyse areas that are difficult to access and allow structural and geometric analysis of real-scale outcrops. Even though the use of Digital Outcrop Models (DOM) is becoming popular, the industry still suffers from a lack of software appropriate to interpret digital outcrops. In this paper we assess the performance of DOM to classify Lithofacies on out-crop of carbonate rocks in northeastern Brazil by using Mosis XP tools. Mosis XP is a software developed by Vizlab - X-Reality and GeoInformatics Lab specifically to perform DOM analysis and interpretation. Results confirm that texture and color attributes were satisfatory preserved in the virtual representation. Also, interpretation tasks were promising since allowed correctly identification of at least four distinct facies over the outcrop profile.

The present work proposes a unified methodology for the three-dimensional reconstruction of geological samples using structure-from-motion (SfM) as preparation for the creation of a Digital Rock Samples database. The proposed methodology aims at the generation of a 3D model with geometric quality and sub-millimetric spatial resolution through a simple workflow, integrating all the necessary techniques for solving the main failures in the reconstruction of homogeneous or reduced size samples. The methodology was tested with four geologists with different graduation levels, ranging from undergraduate, who receive the complete methodological procedures to a Post-Doctor, who did not receive any information about the procedures. The results reveal that the available bibliography does not adequately address the photo collection procedures for 3D reconstruction. This work demonstrates that the lack of a unified and solid methodology for photo collection can influence the quality of the generated 3D models, geometrically and resolution-wise.

Monitoring the available volume of raw water in reservoirs is essential to ensure security of supply to the population. Climate change and global warming had substantially changed the weather bringing constant droughts and unexpected rainfalls, making the correct estimation of water bodies an important matter. The technique for recognizing water bodies is called bathymetry, where by a sonar and embedded global positioning system (GPS) collect data to recreate the depth topography. However, this work is costly and exposes the assessment team to health risks. Therefore, we are proposing a prototype of a semi-automated portable bathymetry equipment to facilitate data collection and thus enable more frequent updates of reservoir volume. The objectives of this work were to construct a reduced model of a vessel and to calibrate an adapted ultrasonic sensor for depth measurement. The ultrasonic sensor mounted on an ARDUINO micro-controller was tested in the FATEC-Jahu's test tank and its readings were compared against measurements made manually. We conclude that the reduced model is stable for data collection with an operational range between 1.0 and 20.0 meters when considering a linear adjustment of the sensor readings achieving an R2 score of 0.9998.

Reservoir rocks have intrinsic properties of porosity and per-meability that make them natural hosts for hydrocarbons, and the estimation of these properties is key to simulating reser-voir models. Additionally, fractures can greatly influence the fluid flow within the reservoir. However, fracture data from reservoirs is often disregarded due to its sub-seismic scale. In this matter, analogue outcrops play an important role in Dis-crete Fracture Network (DFN) modeling which represents the fracture data in terms of their attribute distribution allowing the scaling of the fracture data to reservoir scale which is done mostly by stochastic methods. Recent works have used Gen-erative Adversarial Networks (GANs) to scale porosity data from images, and we propose its use in fracture data from images acquired from Unmanned Aerial Vehicles (UAV) em-ploying a Deep Convolutional GAN (DC-GAN). We obtained similar attribute distribution for fracture aperture showing the viability to replicate statistical behavior.

Exposed rock formations, also know as outcrops, are a great source of geological data, enabling the characterization of fracture networks that are less visible when analyzing reservoir data from seismic and well logs. Remote sensing techniques like LiDAR and UAV Photogrammetry allow the generation of Digital Outcrop Models (DOM) that provide accurate outcrop geometry enabling further investigation of fracture attributes, necessary when creating Discrete Fracture Network (DFN) models and reservoir simulation models. Besides the manual fracture interpretation in DOMs, automatic fracture plane segmentation and clusterization can provide significantly more data. However, the identification of family sets is still done visually in most cases. In this work, we develop a modification to the elbow method using spherical Fisher statistics of dispersion to determine the ideal number of clusters or fracture family sets. We employed this method to cluster fracture families in a point cloud DOM obtained from UAV-SfM processing of a flight over a carbonate outcrop in the Jandaira formation, Northeast Brazil, obtaining clusters with low dispersion values.

Carbonate outcrops are widely studied due to their character-istic analogous to oil and gas reservoirs. Analyzes made with data from these outcrops are used to assist in the description of reservoirs located at great depths. These rocky expo-sures can be easily detected in satellite images using machine learning. Considering the diversity of techniques used in the identification/classification of features on the satellite image data, this work aimed to evaluate the best machine learning technique for identifying carbonate outcrops in Landsat 8 data, considering the carbonate outcrops from the Jandaíra formation, in northeast Brazil. We employed techniques like Grid Search, cross-validation, and validation statistics like Friedman and Nemenyi. Considering the metrics F1 score and Mathews Correlation Coefficient, the MLP technique presented the best results, achieving a F1 score of 0.823, against the SVM with a F1 score of 0.810 and the Random Forest technique with a F1 score of 0.812.

Differently form RGB photos, multi-frame acquisitions of hyperspectral data are unfeasible due to the pushbroom mechanisms of hyperspectral sensors. Therefore, 3D digi-tal surface models including good quality hyperspectral in-formation are available only by integrating 2D images to the point cloud generated using photogrametric cameras or laser scanners. However, geometric differences presented by sen-sor mechanisms resulting in varied perspectives of the same objects present on the scene deeply harm the spatial registration, and thus, the post integration procedure. In this paper we suggest a pre-processing step aiming to adjust objects to the same geometric perspective in order to later integrate the RGB cloud mesh to the hyperspectral data in a more efficient manner. We firstly build the RGB point cloud for later simu-lating the same perspective of push-broom sensor generating a RGB orthomosaic similar to the hyperspectral image. Pre-liminary results indicate an increasing in the number of tie points when registering is performed using the resulting syn-thetic sampling compared to the registration of original data.

Oil and gas reservoirs are well studied in petroleum engineering, using seismic data to estimate fluid flow and well placement. However, seismic data cannot capture fractures due to their scale, and fractures may affect rock porosity and permeability. Consequently, rock fracture segmentation and quantification from aerial images of analogous outcrops can input essential information into those studies. This paper presents a new method, namedinteractive Forest Growing(iFG), for fracture segmentation. The image is initially segmented into superpixels, defining a superpixel graph. The user selects seed superpixels, a path-cost threshold, and fractures are delineated by growing one optimum-path tree from each seed with path costs limited to the selected threshold. iFG considerably increases efficiency while reducing human effort in fracture segmentation compared to pixel-by-pixel manual annotation. We evaluate iFG with three specialists and against aninteractive Region Growing(iRG) method using 15 images to measure bias in user interpretations, verify efficiency gain over a similar approach, and generate a dataset with consolidated annotation for future work. The experiments show that iFG reduces user interventions from 19% to 33% compared to iRG, users with more experience in fracture analysis complete segmentation 4-5 times faster, and segmentation effectiveness is independent of user experience since the average F1 scores between users uing both methods ranged from 0.966 to 0.979, allowing us to create a consolidated segmentation.

Image segmentation is a challenging and essential task in remote sensing. Deep neural networks (DNNs) have successfully segmented images from different domains, but the models usually require time-consuming and expensive pixel-level data annotation. In this letter, we exploit a recent technique to learn features (an encoder) from a few markers placed by the user in relevant image regions, build an encoder–decoder model from a small set of regions delineated by click-based segmentation, and use that model to annotate the remaining pixels. Such user-guided data expansion modeling can be repeated as the encoder–decoder network improves, and by selecting well-annotated regions, the user considerably expands the pixel set to train DNNs with little supervision. We show the role of feature learning from image markers (FLIM) and that our data expansion model can significantly improve the generalization performance of a state-of-the-art DNN when segmenting buildings in aerial images of distinct cities.

Kerogen type in source rocks is directly related to its hydrocarbon generation potential. Its determination is often carried out with destructive methods. This study presents a non-destructive technique as an alternative to determine kerogen type using hyperspectral data and machine learning techniques. To present the technique, models were training using Support Vector Machines, K Nearest Neighbors, and Random Forest classifiers on spectral data collected in rock samples acquired from Taubaté Basin, Brazil, of an outcrop with high hydrocarbon generation potential. The models were trained and evaluated using spectral signatures measured with a spectroradiometer and the results were also tested on hyperspectral images of the samples. The experiments described here achieved accuracy above 0.8 with precision and recall above 0.62 and 0.8, respectively, for every kerogen type, indicating the soundness of the classification.

The digital geoscience revolution is modifying the technologies geoscientists use to acquire and process data with the coming of digital outcrop models, hyperspectral data among many methods. These improvements in technology create new challenges in visualization, manipulation, and modeling of the data acquired, opening new research possibilities. In this paper, we present a novel system to visualize, manipulate and correlate geochemical and hyperspectral data, Digital Outcrop Models, and 3D rock samples using state-of-the-art immersive virtual reality techniques. We present a study case using a visualization and data set on an open pit quarry outcrop of a potential analog for hydrocarbon source rocks from Tremembé Formation (Taubaté Basin, Brazil).

The present study explores the capabilities of a low-cost and standard drone for the generation of a mixed dataset processed as an orthophotomosaic, and surface exposure mesh over the inaccessible wave-washed Wildlife Refuge of Ilha dos Lobos in the southern Brazilian coast. The area comprises an important Marine Protected Area used as a seasonal resting refuge of South American sea lions and fur seals that is continuously washed by wave actions, restricting human access for conventional field mapping. The results have estimated a total surface exposure of the island of 20,806.8 m2, which is an increase of 14.01 % on the total area relative to the most exposed single survey done so far. In futures works, these results will provide data for the geological characterization of the island, which will increase the information on the environment given support to the Wildlife Refuge of Ilha dos Lobos management plan.

This paper presents VizSpectralData, a web based application that runs entirely in the front end and allows spectral data from csv files to be opened, visualized and processed. The system is presented together with the algorithms it implements using real data collected from several carbonate rock samples. It is an open source alternative for simple visualization and processing to proprietary softwares, it is developed in javascript, html and css. It has features to visualize the reflectance, continuum removed spectra, and the derivative of the spectra. It allows to import and export spectral libraries in CSV format.

Fractures affect the intrinsic properties of permeability and porosity of reservoir geobodies, making its network characterization an important task for fluid flow modeling. Direct acquisition of data on reservoirs is labor-intensive and generally produces sparse information. Thus, the study of analogue outcrops with similar characteristics is often carried out by using unmanned aerial vehicle image acquisition and digital photogrammetry. However, the accurate automatic recognition of the fractures network over the outcrop images remains a challenge. Image segmentation methods based on convolution neural networks (CNNs) were successfully applied in medicine, biology, and other areas, however, not yet in geological fracture detection. This work proposes the validation of two popular CNNs - Segnet and Unet - for pixel-to-pixel segmentation targeting fracture detection. Initial results showed acceptable scores of the metrics mean intersection over union (mIoU) and dice intersection (F1) in both CNNs.

Very high spatial resolution data seems to reach its maximum for orbital images due to unavoidable atmospheric interactions. At the same time, special hyperspectral cameras are being developed to operate on-board manned or unmanned aircrafts at a fixed optics, which prevents its using for imaging near objects in laboratory conditions. Both limitations can only be surpassed by using super-resolution principles. In this paper, we present a multi-looking approach for enhancing the spatial resolution of images acquired by systems that exhausted their natural ability to provide finer images. The method exploits multiple image takes with controlled spatial differences to produce a higher resolution output. Experiments with static hyperspectral sensor and synthetic data have proven the approach is sound and robust to many applications (e.g., rock samples).

This work proposes a deep learning and GIS based workflow to assess the influence of highway barriers on wildlife collisions. Our work consists of using Convolutional Neural Networks to classify images extracted automatically from Google Street View to determine the type of barrier, and using geoprocessing tools to estimate parameters as barrier length and location. The method was applied in a real dataset, classifying correctly the barriers in the road-kill points with accuracy of 84.44%. Statistical tests were used to evaluate the influence of each type of barrier on the road-kills.

New sensors aboard recently launched satellites have induced the development of several measures aimed to indicate the presence of many materials over the Earth. Karsts are places rich in carbonate rocks and present large economic and environmental importance. This paper aimed at assessing the performance and consistency of different carbonate estimators derived from orbital images acquired over a controlled karst area. Experiments were assisted by a multi-scaled reference data built through a high spatial resolution Unmanned Aerial Vehicle (UAV) image acquired over the selected area. Results show a considerable unconformity among selected measures and better performance presented by indices exploiting measures along visible and infrared spectral regions.

In this paper we propose a new approach to automatically detect and extract fractures as well as estimate the aperture measures from orbital/aerial images. We show, in a first step, the capability of a translation invariant wavelet multiscale decomposition (NDWT) to separate the information related to the fractures from other features in the image. In the second stage, the aperture size (widths of the fractures) in different positions are estimated across scales using curvature analysis. In a third step, the fractures can be automatically extracted using a growing algorithm. Besides the measures of the fracture apertures in an image of Thingvellir in Iceland, we showed the correlation between the fractures of interest extracted automatically and the respective extracted manually were high (0.9) while only 51 % of then were extracted using just curvature analysis and growing algorithm (without NDWT).

In conventional work environments, visualization and integration of correlated data has always been limited by the amount of software windows a regular PC can display. On the other hand, immersive virtual environments present possibilities of interaction that fit scalable and adaptable three-dimensional space, allowing the integration of different types of data, and relating information. This article describes VROffice, an immersive virtual reality office to handle and correlate georeferenced data. VROffice is an immersive and interactive virtual reality office used to handle and correlate georeferenced data, providing access to 2D and 3D elements (i.e rocks, lab samples, organisms) organized as a virtual library. These objects work as data containers ready to store and display data from its location. In the virtual office, data is visualized in a way that allows different forms of interactions, such as observation, free-hand manipulation, inspection of details and object location in a 3D georeferenced space. Users can also organize datasets, perform analysis between different types of objects, combine characteristics, and interpret data in a Geographic Information System (GIS) environment. It enables different forms of interaction with potential to improve insights over observations, instantly turning multivariable datasets into intuitive immersive displays. Different forms of interaction, as well as UI (user interface) and UX (user experience) decisions have been customized to ensure that the experience in using the system is as comfortable as possible. Therefore, in order to demonstrate the possibilities of its use, case studies were created in two different fields of knowledge, geology and biology, which have georeferenced data available.

The Total Electron Content (TEC) derived from Global Navigation Satellite System (GNSS) data processing has been used as a tool for monitoring earthquakes. The purpose of this study is to bring an alternative approach to the prediction of earthquakes and to determine their magnitudes based on Artificial Neural Networks (ANN) and ionospheric disturbances. For this, the Vertical Total Electron Content (VTEC) data from the National Oceanic and Atmosphere Administration (NOAA) were used to train the ANN. Results show that the ANN process achieved an accuracy of 85.71% in validation assessment to predict Tres Picos Mw=8.2 earthquake from 1:30 UTC to 04:00 UTC, approximately 3 hours before the seismic event. For magnitude classification, the ANN achieved an accuracy of 94.60%. The Matthews Correlation Coefficient (MCC) which takes into account all true/false positives and negatives was also evaluated and showed promising results.

This work proposes a workflow methodology that acquires and processes Google Earth imagery to generate 3D Virtual Field Environments (VFE) using SfM algorithm processing software. These models can be visualized in MOSIS V2 (Multi Outcrop Sharing and interpretation System - Vizlab) software and can transport earth science academics to any high-resolution environment from Google Earth in immersive virtual reality (iVR) with a set of geological tools for interpretation and teaching.

This work evaluates robust statistical assessments for 3D models generated by the process of photogrammetry using images captured from Google Earth screenshots in a method named Printgrammetry. This method can make 3D models more accessible to be explored in other ways, including virtual reality and use in other programs with incorporated tools that are not available in the Google Earth. To verify the quality of the Printgrammetry model we proposed the use of skewness-adjusted robust statistics, an approach that avoids the exclusion of relevant data. The results confirmed that the proposed outlier detection method is more realistic to the skewness of the data than the usual outlier detection methods. The robust statistical methods adequate to our data assessed the quality of the Printgrammetry model.

The virtual reality is becoming an important asset in the recent years bringing immersion to simple tasks as gaming, modeling, and learning. Following this trend, the MOSIS (Multi Outcrop Sharing and Interpretation System) was created to help earth scientists and other users to visualize and study outcrops, important sources of information to the Oil & Gas industry. Basing on the user's feedback some updates were identified to improve the user experience. Thus, a new version of the software (MOSIS V2) was developed, with updates in both, interface and toolset. New possibilities and the model scalability were introduced in the toolset. MOSIS V2 was well graded in SUS (System Usability Scale) [1] which has validated the changes that were made.

For the past decades, environmental studies have been mostly a field activity, especially when concerning geosciences, where rock exposures could not be represented or taken into laboratories. Besides that, VR (Virtual Reality) is growing in many academic areas as an important technology to represent 3D objects, bringing immersion to the most simple tasks. Following that trend, MOSIS (Multi Outcrop Sharing and Interpretation System) was created to help earth scientists and other users to visualize and study VFEs (Virtual Field Environments) from all over the world in immersive virtual reality.

One of the main difficulties in the inspection of Bridges/Viaducts by observation is inaccessibility or lack of access throughout the structure. Mapping using remote sensors on Unmanned Aerial Vehicles (UAVs) or by means of laser scanning can be an interesting alternative to the engineer as it can enable more detailed analysis and diagnostics. Such mapping techniques also allow the generation of realistic 3D models that can be integrated in Virtual Reality (VR) environments. In this sense, we present the ImSpector, a system that uses realistic 3D models generated by remote sensors embedded in UAVs that implements a virtual and immersive environment for inspections. As a result, the system provides the engineer a tool to carry out field tests directly at the office, ensuring agility, accuracy and safety in bridge and viaduct inspections.

Computer vision systems allow digital reconstruction of targets by capturing information through remote sensors such as video cameras and scanners. In this context, the objective of this work was to evaluate the capacity and quality of three-dimensional reconstruction of static targets using the ZED stereoscopic camera. For this goal, we took images of several environments and objects with different surfaces, textures, lighting, distances and acquisition speeds. The results were compared with high-density and high precision point clouds obtained from the targets using a Leica Viva TS15 total station. The data were processed in the CloudCompare software to calculate the displacement between the models generated by the camera and the total station. Under certain circumstances, this technology is able to reconstruct three-dimensional objects and environments with an error of a few centimeters.

This study aimed the analysis of the positional and geometric accuracy of objects in orthomosaics obtained through different unmanned aerial vehicle (UAV) data processing software covering an area located within Universidade do Vale do Rio dos Sinos - UNISINOS in São Leopoldo, RS. A total of nine ground control points (GCP) and twenty checkpoints were surveyed in order register and classify the processed orthomosaics according to the cartographic accuracy standard - Padrão de Exatidão Cartográfica (PEC). Four software was employed to process the UAV data: Pix4D mapper, Agisoft PhotoScan, Menci APS and Bentley Context Capture. The results obtained from each software were compared and identified the smallest distortions when processing with and without ground control points. The flight was executed at a height of 90m with 60% sidelap and 80% overlap using an ST800 UAV equipped with a Sony NEX-7 small format non-metric camera with 24 megapixels resolution. The software GeoPEC was used to classify the orthomosaics according to PEC. For data processed with ground control points all orthomosaics were classified “Class A” in 1/500 scale, however, only Menci APS did not present a trend line via t-student test. On the other hand, Menci APS presented the worst results without the ground control points. In processing with GCP, all orthomosaics obtained optimum results with an approximated error of 2,5 m2, about 0.03% of the area.

Laser scanning and photogrammetry techniques have been broadly adopted by Oil&Gas industry for modeling petroleum reservoir analogues. Beyond the benefits of digital data itself, computer systems employed by geoscientists for interpretation and modeling tasks provide high quality rendering, point clouds surface meshes and photo-realistic textured models. But these systems, commonly, have used 2-D display, the 3-D models and information are projected on the screen, providing a limited visualization and restrictive toolset for interpretation. This work proposes to break this paradigm by developing a fully immersive system capable to virtually teleport the geoscientists to the fieldwork and provide a complete toolset for the outcrop's interpretation. Besides, the system has been evaluated and validated by geologists with different skills and it has emerged as an useful and attractive toolset for Oil&Gas industry.

The main goal of this paper was to evaluate the use of a low cost immersive driving simulator to improve the teaching learning process of the Transport Infrastructure undergraduate course. The driving simulator that was developed in a virtual reality environment to assist both the teaching of engineering and the research on road safety. An experiment was conducted in Transport Infrastructure 1 course for Civil Engineering students in a Brazilian university. The students developed a geometric design of a road that was posteriorly modeled in 3D and provided in simulator. Students piloted a vehicle in the immersive simulator in the same road that they designed. Subsequently the usability of the system was assessed by the SUS metric (System Usability Scale). We performed an evaluation with 52 users and the SUS metric that we found was of 73% assuring a degree of usability above average and demonstrating that the immersive system is good to be used as a complementary tool in the learning of transport infrastructure.

In this study we present a mobile application for geoscience. It refers to a digital field book for automating data collection and outcrop/core description, and optimizing the final data processing. Sensors were developed for semi-automatic data collection, real time calculations, measurements of dip angles and dip directions, geographic location, among others. Field tests were performed comparing the traditional method with the proposed digital method. The preliminary results show a good acceptance of the mobile application by geoscientists and an improvement in the time required to perform data collection. Field tests are still going on and the complete results will be used to improve the development of this important tool in geoscience field work.

In this study, we propose a method to calibrate the laser pulse return intensity of a Terrestrial Laser Scanner (TLS) based on Artificial Neural Networks. The laser pulse return intensity has an important rule on rocks types' classification when using Digital Outcrops Models (DOM) and has been the focus of much research by the geological community as it helps the geological interpretation in outcrops. In our experiment, we used a TLS Ilris 3D model with a wavelength of 1,535 nm. Our method has shown good efficiency for the calibration of the laser pulse return intensity, demonstrating a strong applicability for classification studies of rock types on Digital Outcrops Models.

The use of LiDAR and multiples digital images jointly with 3-D reconstruction techniques for creating 3-D models of natural outcrops and surfaces studies have increased dramatically in the last few years. These techniques have provided an enormous amount of data for interpretation by geoscientists. However, these researchers have no available software capable of offering a user experience comparable to the fieldwork. The majority of solutions have considered desktop systems, which presents inherent limitations due to the 2-D characteristics of displays and loss of immersion into the 3-D model, or up until expensive and complex stereoscopic based approaches to improve the 3-D user experience do not offer well suitable solutions. To address these limitations, this paper presents a low-cost completely disruptive solution for processing, visualizing, sharing and directly handling Digital Outcrop Models with the support of a full interpretation toolset, the MOSIS System. The proposed system provides a fully immersive computational environment, capable of teleporting virtually geoscientists to the fieldwork, giving an awareness of being there physically with an extensible toolset for the DOM's interpretation. Besides, desktop, web and mobile versions of MOSIS have been under development and fulfill the lack of tools for digital outcrop modeling.

Airborne and terrestrial laser scanning techniques have been largely used for the reconstruction of high-resolution 3-D topography in the field of geosciences. In recent years, laser scanning has been also exploited on rock properties, biomass classification and carbon storage estimation. However, when laser spot collides partially against the target or even against undesirable background objects, part of emitted beam is lost and does not return to the laser station. So, it can introduce fewer discontinuities or even artifacts in the point cloud borders, comprising the results. Assuming an interest in minimizing this border effect, we have proposed a computational postprocessing algorithm which identifies anomalies and discrpancies and minimize it by recovering the expected intensity of returned laser pulse. The proposed technique operates on the basis of the collected point cloud intensity of return pulse, laser scanner's position and signals divergence, without requiring any kind of previous setup or additional accessory to the laser scanner.

We investigate here the potential of the spectroscopy in the identification and quantification of mixtures of kaolinite and goethite from the Continuum Removal (CR) of the spectra in the short-wave infrared. For this purpose, spectral measurements of the kaolinite, goethite, and controlled mixtures of these minerals were systematically performed. The continuum of the results were removed, the depth of the kaolinite diagnostic absorption was calculated and compared with a spectral library. It was possible to identify the kaolinite with high determination coefficient (R2>0.95) when its proportion reaches at least 60% in the mixture. For quantification purposes, it was possible to quantify kaolinite using the diagnostic absorption feature depth in the CR with a coefficient of determination of 0.99.

Academia and industry are increasingly concerned with producing general-purpose model composition techniques to support many software engineering activities, e.g., evolving UML design models or reconciling conflicting models.However, the current techniques fail to provide flexible and reusable architectures, a comprehensive understanding of the critical composition activities, and guidelines about how developers can use and extend them.These limitations are one of the main reasons why state-of-the-art techniques are often unable to aid the development of new composition tools.To overcome these shortcomings, this paper, therefore, proposes a flexible, component-based architecture for aiding the development of composition techniques.Moreover, an intelligible composition workflow is proposed to help developers to improve the understanding of crucial composition activities and their relationships.Our preliminary evaluation indicated that the proposed architecture could support composition tools for UML class, sequence, and component diagrams.

Context: Model comparison plays a central role in many software engineering activities.However, a comprehensive understanding about the state-of-art is still required.Goal: This paper, therefore, aims at classifying, identifying publication fora, and performing thematic analysis of the current literature in model comparison for creating an extensive and detailed understanding about this area, thereby determining gaps by graphing and pinpointing in which research areas and for which study types a shortage of publications still exits.Method: We have conducted a systematic mapping study to scrutinize those contributions produced over time, which research topics have most investigated, and which research methods that have been applied.For this, we have followed well-established empirical guidelines to define and apply a systematic mapping study.Results: The results are: (1) majority of studies (14 out of 40) provide generic model comparison techniques, rather than comparison techniques for UML diagrams; (2) a categorization and quantification of the current studies in a variety of dimensions; and (3) an overview of current research topics and trends.

Context: Model comparison plays a central role in many software engineering activities. However, a comprehensive understanding about the state-of-the-art is still required. Goal: This paper aims at classifying and performing a thematic analysis of the current literature. Method: For this, we have followed well-established empirical guidelines to define and perform a systematic mapping study. Results: Some studies (14 out of 40) provide generic model comparison techniques, rather than specific ones for UML diagrams. Conclusion: Fine-grained techniques are still required to support ever-present and complex model comparison tasks during the evolution of design models.

Increases in the frequency of extreme events, such as the occurrence of high temperatures, are prone to produce severe effects on summer crop yields especially soybeans and maize. Under a climate change scenario, the physical parameters of the Earth's surface, such as temperature, water availability and evapotranspiration, are expected to change over the next decades. We investigated the variability of soybean yields associated with crop canopy temperatures during key development that are sensitive to the occurrence of high temperatures in Mato Grosso State, Brazil. In the present paper, we propose that the temperature fluctuations around the optimum level in the crop canopy can cause favorable effects on soybean yields in MT State/Brazil. In order to evaluate the above mentioned hypothesis, we investigated the effects of canopy temperature on soybean yield during flowering to the grain filling periods using Aqua and Terra/MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data, between 2003 and 2010. Comparison of spatially interpolated maps show that yield variations are positively related to canopy-LST during of flowering period, with R2=0.60 and RMSD=6.2%. Overall results show that increases in canopy-LST temperature in Mato Grosso State, during flowering/grain filling periods, are related to higher soybean yield averages.