High accuracy interpolation of DEM using general adversarial network

This GitHub repository implements and evaluates a general adversarial method for Digital Elevation Model(DEM) interpolation with high resolution, which is an adaptation to the context of Digital Elevation Models (DEMs) from the method DeepFill v2 described in [1]. Pre-trained models are provided, as well as the DEMs used for the evaluation of the method. [1] J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, “Free-Form Image Inpainting with Gated Convolution,” 2018.

Run

0. Requirements:
   • Install python3,PIL, opencv-python.
   • Install tensorflow (tested on Release 1.3.0, 1.4.0, 1.5.0, 1.6.0, 1.7.0).
   • Install tensorflow toolkit neuralgym (run pip install git+https://github.com/JiahuiYu/neuralgym), then substitute data_from_fnames.py for neuralgym/neuralgym/data/data_from_fnames.py
1. Training:
   • Prepare training images filelist and shuffle it (example).
   • Modify inpain_dem.yml to set DATA_FLIST, LOG_DIR, IMG_SHAPES and other parameters.
   • Run python train.py.
2. Resume training:
   • Modify MODEL_RESTORE flag in inpaint_dem.yml. E.g., MODEL_RESTORE: 20180115220926508503_places2_model.
   • Run python train.py.
3. Testing:
   • Run python batch_test.py --flist your_flist --checkpoint_dir your_model_dir --outlist your_output.
4. Still have questions?
   • If you still have questions (e.g.: How filelist looks like? How to use multi-gpus? How to do batch testing?), please first search over closed issues at https://github.com/JiahuiYu/generative_inpainting

Pretrained models

run:

python batch_test.py --flist your_flist --checkpoint_dir ./pretrained_model/vallina_2-4-8-8-4-2dilated --outlist your_output

TensorBoard

Visualization on TensorBoard for training and validation is supported. Run tensorboard --logdir model_logs to view training progress.

License

CC 4.0 Attribution-NonCommercial International

The software is for educaitonal and academic research purpose only.

Citing