landcover-orinoquia/finetuning

Finetuning the land cover model interactively

Files in this folder are configurations and implementations of required classes for finetuning the model interactively in an instance of the Land Cover Mapping tool (this repo will be referred to as the landcover repo below).

Setup

Because our implementation of ModelSession relies on the experiment configuration file (a .py file) that produced the model, this repo needs to be on the PYTHONPATH when running the Land Cover Mapping tool's server, in additional to the AI for Earth utilities repo:

export PYTHONPATH="${PYTHONPATH}:/home/boto/wcs/pycharm:/home/boto/lib/ai4eutils"

Note that directory names in this repo should not clash with ones in the landcover repo.

Basemap needs to be in the landcover repo's root directory for the server to serve the data. We can create a symbolic link to the files stored in a blob storage container:

ln -s /home/boto/wcs/mnt/wcs-orinoquia/images_sr_median/2013_2014_dem/wcs_orinoquia_sr_median_2013_2014_dem.vrt wcs_orinoquia_sr_median_2013_2014_dem.vrt

The configuration files for the dataset and model in this folder and ModelSessionOrinoquia.py should be copied to the landcover repo's web_tool directory.

We also need to make the following changes:

• Modify worker.py to add a case for the model_type pytorch_landsat.

• Modify web_tool/DataLoader.py, changing

  resolution=(x_res, y_res)
  

  to

  resolution=(30, 30)  # hardcode Landsat 8 resolution
  

• In web_tool/js/components.js, modify these values in addInferenceWindowSizeSlider to:

  min: 7680,
  max: 23040,
  

• In web_tool/js/globals.js, set the following variables:

  var INFERENCE_WINDOW_SIZE = 300;
  var INFERENCE_WINDOW_SIZE = 7680;
  

Operation