This repository contains the code for training generative adversarial networks (GANs) to generate aerial flood prediction imagery. The GANs are input with a pre-flooding satellite image in addition to informative factors such as a digital elevation model, flow accumulation, distance to rivers, and OSM map. The GANs output a photorealistic post-flooding image prediction.
This repository also contains code for training a flood segmentation model, which when input a post-flooding satellite image, outputs a binary mask indicating the locations of floodwaters. The segmentation model can hence be used to evaluate the predictions of the GANs, by comparing the flood masks of a pair of predicted and ground truth images.
The dataset and associated metadata are available on Zenodo (https://zenodo.org/doi/10.5281/zenodo.13366121) under the Creative Commons Attribution Non-Commercial Share-Alike 4.0 International licence.
To train a GAN model for flood image generation:
python train.py --model=PairedAttention --dataset_subset=usa --dataset_dem=same --data_path=path/to/data --num_epochs=200 --topography=all --resize=512 --crop=4 --save_model_interval=50 --save_images_interval=25 --verbose
To train a segmentation model:
python segment.py --train --dataset_subset=usa --data_path=path/to/data --num_epochs=100 --save_model_interval=25 --save_images_interval=10 --verbose
A model can be evaluated by calculating metrics, plotting the losses over the epochs, plotting a random sample of generated images, or generating a specific named image:
python evaluate.py --model=PairedAttention --dataset_subset=usa --dataset_dem=same --use_test_data --data_path=path/to/data --resize=512 --crop=4 --topography=all --pretrained_model_path=path/to/model --plot_losses --plot_sample_images --num_images=10 --calculate_metrics --segmentation_model_path=path/to/segmentation --plot_single_image=output --image_name=hurricane-harvey_00000257 --crop_index=3
Multiple models can be compared by their calculated metrics or generated images:
python compare.py --compare=models --dataset_subset=usa --dataset_dem=same --use_test_data --data_path=path/to/data --resize=512 --crop=4 --topography=all --segmentation_model_path=path/to/segmentation --pix2pix_path=pix2pix/path --cyclegan_path=cyclegan/path --attentiongan_path=attentiongan/path --pairedattention_path=pairedattention/path --calculate_metrics --image_names hurricane-harvey_00000257_3 hurricane-harvey_00000268_1
Sample generated images from different model architectures:
Sample generated images from different combinations of input factors:
