Digital geological mapping has progressed significantly with the advent of commercial GIS, GPS technologies, and portable devices over the past three decades. However, many software and app tools that enhance field data collection remain proprietary and specific to mapping projects or organisations, which limits their integration and sharing within the geoscientific community. This presentation will introduce GEOL-QMAPS, an open-source, QGIS-based solution designed for flexible and harmonised digital geological mapping, developed as part of the West African eXploration Initiative 4. GEOL-QMAPS includes a QGIS field data entry template and a custom QGIS plugin, both available on open-access online repositories. It supports fieldwork on tablets or mobile devices via QField app, integrates with desktop QGIS, and facilitates the creation of new and legacy field databases according to user-defined guidelines. The presentation will cover the general workflow for implementing GEOL-QMAPS and provide examples demonstrating its effectiveness both in the field and in office settings.

How to cite: Perret, J., Jessell, M. W., and Bétend, E.: An open-source, QGIS-based solution for digital geological mapping: GEOL-QMAPS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-74, https://doi.org/10.5194/egusphere-egu25-74, 2025.

How to cite: Youssefi, D., Bellet, V., Constantin, A., Lallement, D., Dubois, E., and Tanguy, Y.: Open-source 3D tools developed for the CO3D mission and beyond, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1311, https://doi.org/10.5194/egusphere-egu25-1311, 2025.

The continuous increase in resolution and complexity of Earth System Models (ESMs), aimed at improving the accuracy of simulations, significantly increases data handling demands. For this reason, scalable and efficient I/O solutions are critical to ensure that data storage, processing, and transfer do not become bottlenecks that hinder overall simulation throughput. Optimising this workflow is essential not only for improving performance but also for reducing the energy footprint of large-scale Earth system simulations.

Many ESMs, including the IFS-NEMO coupled model used in our case study within the Destination Earth Climate Digital Twin project, adopt a client-server I/O architecture to address these challenges. In this scheme, the model sends generated data to a server that handles complex post-processing tasks, such as interpolation, encoding, and data writing, while the model continues simulating the next time steps. However, this approach also requires continuous optimisation to ensure the overall efficiency of the output pipeline.

To achieve this, detailed I/O profiling was conducted, with a focus on the MultIO library, which manages the output pipeline, including ocean server creation and ocean data transport. On the client side, frequent and costly access to metadata was identified as a major source of I/O overhead, while on the server side, high interpolation times were observed, prompting further analysis and targeted optimisations that achieved a sixfold speedup in ocean interpolation. Additional pipeline actions were reviewed and optimised, contributing to a more efficient and scalable output workflow. Combined with tests to optimise server resource allocation, these efforts resulted in an overall efficiency improvement of up to 6.7% in simulation performance.

How to cite: Peña, C., Aguridan, R., Yepes, X., and Acosta, M.: I/O Profiling and Optimisation to Improve Energy Efficiency: Insights from the Climate Digital Twin project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9616, https://doi.org/10.5194/egusphere-egu25-9616, 2025.

This study develops a Cellular Automaton (CA) model to predict forest fire spread in the Sunshine Coast region, utilizing diverse meteorological and environmental datasets. Key variables such as temperature, wind speed, rainfall, soil moisture, solar radiation, vegetation type, slope, elevation, and proximity to roads and streams are integrated to simulate fire dynamics with high spatial resolution. Historical fire occurrence data are used for model calibration and validation, ensuring accuracy in replicating fire spread patterns. The CA model operates through iterative cell-based transitions, governed by rules reflecting the complex interplay of environmental and meteorological factors. Results highlight the significant influence of wind, vegetation type, and topography on fire behaviour, with simulations effectively capturing spatial variability and spread dynamics. The findings underscore the model's potential as a robust, scalable tool for wildfire management, enabling data-driven planning for prescribed burns and risk mitigation. This research offers valuable insights into forest fire behaviour, contributing to sustainable ecosystem management and resilience planning in subtropical regions such as the southeast part of Queensland Australia.

How to cite: Singh, H., Ang, L., and Srivastava, S. K.: Modelling Forest Fire Spread in the SEQ Region Using Meteorological and Environmental Datasets: A Cellular Automaton Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15828, https://doi.org/10.5194/egusphere-egu25-15828, 2025.

Libvips is an open source image processing library originally created for museum imaging projects through a range of EU-funded projects from 1989 onwards. The challenges of processing images which were much larger than available RAM as well as coping with multi-band multi-format pixels led to an extremely efficient software design. It also makes automatic use of multi-core CPUs. Today libvips is used by many websites due to its speed and low memory use (from Wikipedia to shopify and booking.com). It is OSS Fuzz tested by Google as it is classed as essential Internet software. It has been used in many museum imaging projects to stitch X-ray images and process massive scans (e.g. The battle of Murten 1.6 TerraPixel scan). Tiled pyramidal tiff images made for multi-resolution web-browsing are easily made and handled by libvips and its viewer vipsdisp. A spreadsheet-like GUI called nip is also useful for experimenting with image processing. These features make it a useful tool for processing images in the earth sciences, especially when sizes are larger than 32 GiB when most desktop or laptop computers struggle with typical software. Python can use the library (pyvips) which makes it easy to use with other tools but can also be used from C, C++, Ruby and Javascript.

How to cite: Martinez, K., Cupitt, J., Fuller, L., and Wolthuizen, K. A.: The libvips image processing library, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17728, https://doi.org/10.5194/egusphere-egu25-17728, 2025.

The constant growth in computational demands of scientific applications, combined with energy efficiency requirements, makes GPU acceleration an important factor to consider in high-performance computing environments. Therefore, GPU porting has become essential for efficiently utilizing modern heterogeneous systems that currently provide both multicore CPUs and GPU-accelerated partitions.

While small, relatively new projects might be candidates for complete rewrites in low-level GPU languages like CUDA or HIP, this approach becomes impractical for larger and more complex codebases. Thus, a more convenient way is provided by the directive-based approach, which allows developers to maintain their original C++ or Fortran code while adding extra OpenACC/OpenMP directives to generate energy-efficient GPU code. 

However, this seemingly straightforward method often presents significant challenges. For instance, dealing with code that employs layouts that are not well-suited for GPU architectures, such as deeply nested loop structures or complex memory access patterns that result in suboptimal performance, might lead to the need to reorganize the initial code after all.

In this work, we present a systematic approach to performing the GPU code transition through compiler directives. This several-step incremental process seeks to reach a significant performance and energy consumption improvement while preserving code maintainability, portability, and output accuracy. We demonstrate the effectiveness of our approach through a detailed case study of our ongoing project porting the NEMO ocean model, which represents an interesting example of a complex scientific Fortran code with numerous common computational patterns. Finally, we discuss the experiences, limitations, and trade-offs encountered during this process, providing useful insights for other porting efforts that could face similar GPU migration challenges.

How to cite: Quintanilla, R., Medvedev, A., Yepes-Arbós, X., Aguridan, R., and Acosta, M. C.: A systematic methodology for directive-based GPU porting: NEMO ocean model as a case study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18460, https://doi.org/10.5194/egusphere-egu25-18460, 2025.

GeoSight is an open-source Python package developed for geospatial analysis, processing, and visualization.

GeoSight’s development has been shaped by ongoing collaboration between lecturers, students, and industry professionals. Students have contributed, using and improving the package while learning Python programming, object-oriented design, and addressing real-world geoscience challenges.  

With a focus on accessible, structured and modular programming, the package is user-friendly, flexible, and easily extendable. It is distributed under a public license to encourage collaboration and widespread use.

Key features of GeoSight include 2D and 3D geospatial data visualization, contour and slope analysis, noise filtering, and the integration of satellite imagery into terrain models. Advanced capabilities, such as machine learning for classification and geological computations such as strike and dip measurements, might serve geoscientists, engineers, and urban planners. For an efficient processing, GeoSight supports GPU/CPU architectures.

We here present GeoSight features, benefits, and limitations, discussing its modular design, its support for student learning, and adaptability to changing geospatial standards. We plan in keep expanding its analytical tools, its cross-platform compatibility, and adding further features such as real-time visualization and multiple data-source integration

How to cite: Vaseghnia, H., Fernando Cardozo Diaz, N., and Riccardi, E.: GeoSight: An Open-Source library for Geospatial Analysis and Visualization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20575, https://doi.org/10.5194/egusphere-egu25-20575, 2025.

Recent advances in remote sensing and computer vision have reshaped how geospatial data is captured, visualized, and analyzed. Drones equipped with high-resolution cameras and sensors enable rapid, detailed surveys from multiple angles, providing near-real-time insights into complex environments. While photogrammetry-based workflows yield accurate 3D meshes, they often demand substantial computational power and lengthy processing times.

Emerging techniques such as Gaussian splatting and Neural Radiance Fields (NeRFs) offer compelling alternatives to traditional mesh-based methods. Gaussian splatting represents 3D scenes as point-based “splats” with mathematical distributions, enabling faster, photorealistic rendering. NeRFs employ neural networks to generate volumetric reconstructions from sparse image inputs, capturing intricate lighting and geometry with minimal manual intervention. Together, these methods reduce post-processing complexity and enhance visual fidelity.

In this session, we demonstrate novel applications of Gaussian splats and NeRFs within ArcGIS and discuss how these approaches can integrate with familiar mesh-based workflows. We also explore ways to extend existing 3D streaming standards, such as OGC’s Indexed 3D Scene Layers (I3S), to incorporate these emerging data types. Finally, we showcase real-world examples demonstrating how blending Gaussian splats and conventional meshes enables richly detailed, interactive visualizations at multiple scales. This convergence promises more efficient collaboration, cost-effective workflows, and deeper insights into rapidly evolving built and natural environments.

How to cite: Belayneh, T.: Next-Generation Geospatial Visualization: From Traditional Meshes to Gaussian Splats and NeRFs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21310, https://doi.org/10.5194/egusphere-egu25-21310, 2025.