Segmentation Zoo Res-UNet models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.

Doodleverse/Segmentation Zoo models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.

These model data are based on the Coast Train v1 Landsat-8 labeled imagery subset. Models have been fitted to 4 different types of data

1. NDWI (1 band): (g-nir)/(g+nir)

2. MNDWI (1 band): (swir-g)/(swir+g)

3. RGB (3 band): red, green, blue

4. RGB-NIR-SWIR (5 band): red, green, blue, nir, swir

Classes are: {0: water, 1: whitewater, 2:sediment, 3:other}. These classes have been remapped from the original 11 classes
 

These files are used in conjunction with Segmentation Zoo*

For each model, there are 3 files:

1. config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.

2. weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function  `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images

3. model card file: this is a json file containing the following fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata

References

* https://github.com/Doodleverse/segmentation_zoo

** https://github.com/Doodleverse/segmentation_gym