19 skills found
OpenGeoVis / PVGeo🌍 Python package of VTK-based algorithms to analyze geoscientific data and models
cgre-aachen / Open AR Sandboxopen-AR-Sandbox is a project to enable haptic interaction with geoscientific content in AR-Sandboxes.
MiraGeoscience / Geoh5pyPython API for geoh5, an open file format for geoscientific data.
Doodleverse / Segmentation GymA neural gym for training deep learning models to carry out geoscientific image segmentation. Works best with labels generated using https://github.com/Doodleverse/dash_doodler
dengwirda / Jigsaw Geo MatlabMATLAB bindings for JIGSAW(GEO): an unstructured mesh generator for geoscientific modelling.
swisstopo / Swissgeol Viewer Suiteswissgeol.ch gives you insight in geoscientific data - above and below the surface
lesommer / Oocgcmoocgcm is a python library for the analysis of large gridded geophysical dataset.
g-adopt / G AdoptRepository for the development of the Geoscientific ADjoint Optimization PlaTform (G-ADOPT).
NRCan / Geoscience Language ModelsGloVe and BERT language models re-trained using geoscientific text.
dengwirda / Jigsaw Geo PythonPython bindings for JIGSAW(GEO): an unstructured mesh generator for geoscientific modelling.
pygeo / Pycmbspython based geoscientific and climate data analyis and model benchmarking tool
cerea-daml / Co2 Images SegThe official code for "Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants", submitted to " Geoscientific Model Development"
Doodleverse / Segmentation ZooA collection of geoscientific image segmentation models
brunorpinho / Docker Geoscience NotebookJupyter Notebook GeoScientific Python Stack - Dockerfile
Petroles / Regis CollectionRetrieval Evaluation for Geoscientific Information Systems
dengwirda / Jigsaw Geo TutorialA tutorial on mesh generation for geoscientific modelling using JIGSAW.
Foraminarium / MicroCT Image Analysis With ImageJ FijiMicro X-ray Computed Tomography (microCT) image analysis provides information about micro-scale structures and properties critical to the biological and geoscientific fields. Recent advances in microCT have in part been driven by the availability of new 3D image processing techniques. These techniques, while developed for the biological/life sciences, have the potential to support other scientific fields given an appropriate application. For example, microCT analysis can provide information about the density and structure of the microfossil shells of foraminifera, which are commonly used in paleoceanography to reconstruct aspects of the ocean and climate. Information about the density and structure of foraminiferal shells is useful for characterizing changes in ocean acidification and in foraminiferal species classification and assemblage-based sea surface temperature studies. The macros presented here provide a user-friendly semi-automated workflow for applying imageJ/Fiji tools to extract microCT-derived information on many individuals scanned together in a batch. The example datasets provided are of a batch of individual foraminiferal shells. The batch is small (six individuals) for simplicity of the example, but the workflow is applicable for rapidly processing large batches (dozens to hundreds) of individuals.
xiekangwhu / CWSC Deep Residual NetworkSoil moisture storage capacity (SMSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models, but there are currently no available parameter datasets of SMSC on a global scale especially for hydrological models. Here, we produce a dataset of SMSC parameter for global hydrological models. Parameter calibration of three commonly used monthly water balance models provides the labels for the deep residual network. Calibration on the global grids can significantly reduce parameter discontinuities compared to calibration on individual catchments. The global SMSC is reconstructed at 0.5° resolution by integrating 15 types of meteorological, topographic, and runoff data based on a deep residual network. SMSC products are validated with spatial distribution against root zone depth datasets and validated in terms of simulation efficiency on global grids and 20 catchments from different climatic regions, respectively. We provide the global SMSC parameter dataset as a benchmark for geoscientific modelling by users.
Tari91 / Locating Mineral Deposits Using Deep Learning On Geophysical DataMineral Deposits Detection Suite applies deep learning to predict mineral deposits using synthetic geophysical data. It integrates data simulation, CNN-based modeling, and Excel export, delivering a precise and efficient framework for geoscientific analysis and mineral exploration research.
