##Table of Contents

• Background
• Dependencies
• Data
• Usage
• Contact

to be continued...

This code is implemented with the anaconda environment:

• cudatoolkit 11.2.2
• cudnn 8.1.0.77
• gdal 3.2.3
• numpy 1.23.3
• pandas 1.5.0
• python 3.9.13
• scikit-learn 1.1.2
• tensorflow 2.10.0
• tqdm 4.64.1

The source and experiment data will be opened...

• For the unsupervised pretraining stage, see ./Unsupervised Pretraining/DAS_pretraining.py and pretrain the base model. The parameter would be saved in ./DAS_logs/savedmodel.npz.
• For the scene segmentation and task sampling stage, see ./scene_sampling.py, the result would be output into ./seg_output folder.
• For the the meta learner, see ./meta_learner.py.
• For the model adaption and landslide susceptibility prediction, see ./predict_LSM.py. The intermediate model and adapted models of blocks would be saved in folder ./checkpoint_dir and ./models_of_blocks, respectively.The adapted models will predict the susceptibility for each sample vector in ./src_data/grid_samples_HK.xlsx.
• The ./tmp folder restores some temp records.
• For the figuring in the experiment, see ./figure.py, the figures would be save in folder ./figs.

To ask questions or report issues, please open an issue on the issues tracker.