Spatially Constrained Bayesian Network (SCB-Net): An Approach to Obtaining Field Data-Constrained Predictive Maps with Uncertainty Assessment. In this project, we have developed an innovative approach to ensure that predicted lithologies accurately reflect the samples collected in the field. Moreover, our model leverages multisource remote sensing data to make predictions in areas where no direct samples are available. Our study focuses on the Churchill Province, located in Quebec, Canada.
Link to remotely sensed data, probability masks, and weights of the models.
Authors: Victor S. Santos (INRS & NRCan), Erwan Gloaguen (INRS), and Shiva Tirdad (NRCan).
INRS: Institut National de la Recherche Scientifique
NRCan: Natural Resources Canada
This project is licensed under the Creative Commons Attribution 4.0 International License - see the LICENSE file for details.