Digital outcrop models (DOMs) have facilitated quantitative and qualitative studies in digital and virtual environments of source and reservoir rock analogs important to the oil industry. The use of immersive virtual reality (iVR) to extend field experiences has motivated several research groups to develop software integrating immersive virtual reality techniques with tools to interpret and derive geological information from DOMs. This virtual approach can also contribute to the development of geological and spatial thinking skills taught in the classroom and during field trips. The immersive virtual field trips (iVFTs) can provide students access to outcrops and additional data restricted to field learning activities while allowing additional interactions impossible in the field. iVFTs have been developed recently however the structuring of iVFTs for geology classes has not been presented in a way that inexperienced iVR users can make use of such systems. In this scenario, our work proposes a method to structure an iVFT using georeferenced data containers (GDCs) and the virtual reality software Mosis LAB while evaluating users' perceptions during an iVFT study case. The evaluation using technology acceptance model (TAM) questionnaires, showed that users were positively impacted by the observational iVFT experience, effectively supporting e-learning, and class field learning activities and preparations. This approach allows field trip experiences in less accessible study sites, especially in less favorable conditions like the ones during the Coronavirus 2019 (COVID-19) pandemic where many geoscience departments had their field trips hampered.

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.

Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually lack the spatial resolution that suborbital sensors have. Many techniques have been developed to improve spatial resolution through data fusion. However, most of them have serious limitations regarding application and scale. Recently Super-Resolution (SR) convolution neural networks have been tested with encouraging results. However, they require large datasets, more time and computational power for training. To overcome these limitations, this work aims to increase the spatial resolution of multispectral bands from the Landsat satellite database using a modified artificial neural network that uses pixel kernels of a single spatial high-resolution RGB image from Google Earth as input. The methodology was validated with a common dataset of indoor images as well as a specific area of Landsat 8. Different downsized scale inputs were used for training where the validation used the ground truth of the original size images, obtaining comparable results to the recent works. With the method validated, we generated high spatial resolution spectral bands based on RGB images from Google Earth on a carbonated outcrop area, which were then properly classified according to the soil spectral responses making use of the advantage of a higher spatial resolution dataset.

Geologically, the Arctic is one of the least-explored regions of Earth. Obtaining data in the high Arctic is logistically, economically, and environmentally expensive, but the township of Longyearbyen (population of 2617 as of 2024) at 78° N represents a relatively easily accessible gateway to Arctic geology and is home to The University Centre in Svalbard (UNIS). These unique factors provide a foundation from which to teach and explore Arctic geology via the classroom, the laboratory, and the field. UNIS was founded in 1993 as the Norwegian “field university”, offering field-based courses in Arctic geology, geophysics, biology, and technology to students from Norway and abroad. In this contribution, we present one of the educational components of the international collaboration project NOR-R-AM (a Norwegian-Russian-North American collaboration in Arctic research and collaboration, titled Changes at the Top of the World through Volcanism and Plate Tectonics) which ran from 2017 to 2024. One of the key deliverables of NOR-R-AM was a new graduate (Master's and PhD-level) course called Arctic Tectonics and Volcanism that we have established and taught annually at UNIS since 2018 and detail herein. The course's main objective is to teach the complex geological evolution of the Arctic from the Devonian period (∼ 420 million years ago, Ma) to the present day through integrating multi-scale datasets and a broad range of geoscientific disciplines. We outline the course itself before presenting student perspectives based on both an anonymous questionnaire (n=27) and in-depth perceptions of four selected students. The course, with an annual intake of up to 20 MSc and PhD students, is held over a 6-week period, typically in spring or autumn. The course comprises modules on field and polar safety, Svalbard/Barents Sea geology, wider Arctic geology, plate tectonics, mantle dynamics, geo- and thermochronology, and geochemistry of igneous systems. A field component, which in some years included an overnight expedition, provides an opportunity to appreciate Arctic geology and gather field observations and data. Digital outcrop models, photospheres, and tectonic plate reconstructions provide complementary state-of-the-art data visualization tools in the classroom and facilitate efficient fieldwork through pre-fieldwork preparation and post-fieldwork quantitative analyses. The course assessment is centred around an individual research project that is presented orally and in a short and impactful Geology journal-style article. Considering the complex subject and the diversity of students' backgrounds and level of geological knowledge before the course, the student experiences during this course demonstrate that the multi-disciplinary, multi-lecturer field-and-classroom teaching is efficient and increases their motivation to explore Arctic science.

Field-based education is a cornerstone of geoscience learning, offering students the opportunity to connect theoretical knowledge with real-world geological contexts. Yet, access to such experiences remains limited due to logistical, financial, environmental, and social barriers, especially in remote or extreme environments like the High Arctic. In response to these challenges, Virtual Field Experiences (VFEs) are emerging as promising tools to complement traditional fieldwork. In this study we investigate how VFEs contribute to experiential learning among students at the University Centre in Svalbard (UNIS) in Longyearbyen, Svalbard. Using a mixed-methods approach grounded in experiential learning theory and the Technology Acceptance Model, we surveyed 66 students who used VFEs as part of geoscience courses at UNIS before and after physical field excursions. Results reveal that students found VFEs particularly valuable for pre-field orientation, spatial awareness, and post-field reflection. Students reported improved readiness, deeper conceptual understanding, and enhanced engagement. The virtual representations enabled them to revisit complex sites, identify overlooked features, and consolidate observations made during fieldwork. Importantly, most participants did not see VFEs as a replacement for traditional fieldwork but recognized their role in making field learning more inclusive, repeatable, and accessible. We highlight how VFEs can reduce cognitive overload by familiarizing students with unfamiliar environments, thus helping to manage the “novelty space” often experienced during initial field exposure. Students expressed strong interest in broader applications of VFEs across courses and disciplines, particularly when enriched with interactivity and guided tasks. These findings suggest that digital field representations hold significant promise for expanding access to geoscience education and enhancing learning outcomes. Future research should explore co-design practices with students and educators to optimize VFEs for equity, sustainability, and pedagogical impact across diverse learning environments.