SchemaGAN: Dataset and Pre-trained Model for Subsoil Schematization
SchemaGAN: Dataset and Pre-trained Model for Subsoil Schematization
This Zenodo repository provides the data and pre-trained model associated with SchemaGAN, a Generative Adversarial Network (GAN) adapted from the Pix2Pix architecture to generate subsoil schematizations conditioned on Cone Penetration Test (CPT) data. The files include: Dataset (data.zip): Contains 2D CSV files with dimensions (512 × 32) representing subsoil schematizations, used for training, validation, and testing. Pre-trained Model (schemaGAN.h5): The trained weights ready to use with the SchemaGAN code in the GitHub repository For a complete overview of the methodology, installation instructions, and usage examples, please refer to the GitHub repository and the accompanying thesis.
- Delft University of Technology Netherlands
- Deltares Netherlands
Artificial intelligence, Cone Penetration Test, Cross-section, Schematisation, Generative AI, Machine learning, Deep learning, Subsurface, Geotechnical Engineering, Generative Adversarial Network
Artificial intelligence, Cone Penetration Test, Cross-section, Schematisation, Generative AI, Machine learning, Deep learning, Subsurface, Geotechnical Engineering, Generative Adversarial Network
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