Just a random thought to myself, about how huge and varied the Earth is, and the structures from mud puddle ripples to mountain ranges, and the many ways we record this by making graphics, from cracks in a crystal to plate boundaries. For me, at least, I need some sort of bounds, and notion of the resolution involved to even get started, I have DEMs from point clouds at centimeter intervals and others at tens of meters, different by five orders of magnitude.

I am rather very new into application of machine learning and deep learning based prediction and visualization of geomorphological maps, i would appreciate Muhammad Ali if you could keep me in loop, share some material specially how you started and where you are right now, I do have a research idea also. Looking forward to your valuable reply.

Regards.

Muhammad Ali

Here are some possible solutions to the diffusion problem in U-Net architecture for geomorphological maps:

• Use a multi-channel U-Net architecture. A multi-channel U-Net architecture can help to improve the accuracy of the model by allowing it to learn more features from the input data.
• Increase the number of training samples. A larger training set can help the model to learn more about the distribution of the data, which can improve the accuracy of the predictions.
• Use a different loss function. The loss function is used to measure the error between the predicted and ground truth labels. A different loss function may be able to better capture the diffusion problem and improve the accuracy of the predictions.
• Use a different regularization technique. Regularization techniques can help to prevent the model from overfitting the training data. A different regularization technique may be able to improve the accuracy of the predictions.
• Use a different optimizer. The optimizer is used to update the parameters of the model during training. A different optimizer may be able to improve the convergence of the model and improve the accuracy of the predictions.

It is important to note that there is no one-size-fits-all solution to the diffusion problem in U-Net architecture for geomorphological maps. The best solution will vary depending on the specific data set and the desired accuracy of the predictions.

Here are some additional tips for improving the accuracy of U-Net models for geomorphological maps:

• Use high-quality data. The quality of the data will have a direct impact on the accuracy of the model. Make sure that the data is well-labeled and that it is representative of the area that you are mapping.
• Use a robust training procedure. The training procedure should be designed to avoid overfitting the model to the training data.
• Use a validation set. The validation set should be used to evaluate the performance of the model on unseen data. This can help you to identify when the model is overfitting and to adjust the training procedure accordingly.
• Use a test set. The test set should be used to evaluate the final performance of the model. This is the data that the model will be used on in production, so it is important to make sure that the model performs well on this data.

Please recommend my reply if you find it useful .Thanks