This paper proposes a technique named Printgrammetry, a structured workflow that allows the extraction of 3D models from Google Earth platform through the combination of image captures from the screen monitor with Structure from Motion algorithms. This technique was develop to help geologists and other geoscientists in acquiring 3D photo-realistic models of outcrops and natural landscapes of big proportions without the need of field mapping and expensive equipment. The methodology is detailed aiming to permit easy reproducibility and focused on achieving the highest resolution possible by working with the best images that the platform can provide. The results have shown that it is possible to obtain visually high quality models from natural landscapes from Google Earth by acquiring images at high Level of Detail regions of the software, using a 4K monitor, multi-directional screenshots and by marking homogeneously spaced targets for georeferencing and scaling. The geometric quality assessment performed using Light Detection and Ranging ground truth data as comparison shows that the Printgrammetry dense point clouds have reached 98.1\% of the total covered area under 5 meters of distance for the Half Dome case study and 96.7\% for the Raplee Ridge case study. The generated 3D models were then visualized and interacted through an immersive virtual reality software that allowed geologists to manipulate this virtual field environment in different scales. This technique is considered by the authors to have a promising potential for research, industrial and educational projects that doesn't requires highly precision models.

The study of outcrops in geosciences is being significantly improved by the enhancement of technologies that aims to build digital outcrop models (DOMs). Usually, the virtual environment is built by a collection of partially overlapped photographs taken from diverse perspectives, frequently using unmanned aerial vehicles (UAV). However, in situations including very steep features or even sub-vertical patterns, incomplete coverage of objects is expected. This work proposes an integration framework that uses terrestrial spherical panoramic images (SPI), acquired by omnidirectional fusion camera, and a UAV survey to overcome gaps left by traditional mapping in complex natural structures, such as outcrops. The omnidirectional fusion camera produces wider field of view images from different perspectives, which are able to considerably improve the representation of the DOM, mainly where the UAV has geometric view restrictions. We designed controlled experiments to guarantee the equivalent performance of SPI compared with UAV. The adaptive integration is accomplished through an optimized selective strategy based on an octree framework. The quality of the 3D model generated using this approach was assessed by quantitative and qualitative indicators. The results show the potential of generating a more reliable 3D model using SPI allied with UAV image data while reducing field survey time and complexity.

In Petroleum Geology, to assess the hydrocarbon generation potential in source rocks involves the determination of the kerogen type by some destructive method. The usage of such methods is a bottleneck in the process because it is time-consuming, requires specialized tools and personnel, and ends up destroying the rock sample, so it is not possible to do any posterior analysis. This study presents an alternative method for determination of the kerogen type that is fast and non-destructive using hyperspectral data and machine learning techniques. The method is validated using five distinct supervised learning algorithms that were applied to spectral data collected in rock samples from Taubaté Basin, Brazil, of an outcrop whose rocks have a wide range of hydrocarbon generation potential. Cores and samples were collected from the outcrop and had their kerogen type determined by geochemical analyses performed in the laboratory. The robustness of the method is evaluated in two distinct experiments. In the first one, the hyperspectral dataset was collected using a non-imaging spectroradiometer; in the second one, the method uses non-imaging hyperspectral data as training and is tested in hyperspectral images collected. In both experiments, the method was able to establish a relationship between selected spectral features and the kerogen type of the source rocks sampled. The results obtained in this paper are prospective for non-destructive classification of kerogen type (and, consequently, the hydrocarbon generation potential), since most of the models generated achieved accuracy above 0.8 in the validation step and 0.75 in the test step.

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).

Accurate quantification of mineralogical composition in carbonate rocks is essential for reservoir characterization in the oil industry, directly influencing petrophysical properties such as porosity and permeability. However, traditional methods such as X-ray diffraction (XRD) are destructive and provide limited spatial sampling. The aim of this study was to develop and validate a workflow for the continuous quantification of calcite and dolomite in drill cores from the Brazilian pre-salt oil province by integrating short-wave infrared (SWIR) hyperspectral imaging (HSI) and Machine-Learning algorithms. A total of 80 m of cores were evaluated using 170 XRD-validated samples to calibrate linear, nonlinear, and ensemble models. The results showed that the combination of Multiplicative Scatter Correction (MSC) preprocessing with Multilayer Perceptron (MLP) and Support Vector Regression (SVR) achieved the best performance, reaching an $R^2$ of 0.84. Explainable Artificial Intelligence (SHAP) confirmed the relevance of diagnostic bands between 2330 and 2360 nm, improving geological interpretability of the predictions. The proposed methodology provides a non-destructive and high-resolution alternative for mineralogical profiling, supporting the evaluation of complex reservoirs and decision-making in the oil and gas industry. Although the workflow was validated using a specific pre-salt dataset, future studies should assess its transferability to other carbonate reservoirs and broader geological settings.