Detecting and Monitoring Plastic Waste Aggregations in Sentinel-2 Imagery

Explore the Data. Read an overview of the project. Read a paper detailing the work

Project Structure

There are three core domains in this repo: Model Training, Site Detection, and Metadata Generation and Site Monitoring. Reference the GPW Pipeline Diagram for information on how pipeline components are related. These scripts are run through a series of notebooks.

Setup

The code is known to run on Python >= 3.7

$ python -m venv env
$ source env/bin/activate
$ pip install -r requirements.txt

Imports are given relative to the repo base directory, which therefore must be on PYTHONPATH. Either in your .bash_profile or at the end of the file env/bin/activate add: export PYTHONPATH=/path/to/plastics:$PYTHONPATH

The bulk-processing pipeline runs on Descartes Labs. Authorization on your local is handled via the command line helper function: $ descarteslabs auth login Follow the link to enter user email and password. If needed, see further instructions from Descartes.

Model Training

1. Create pixel and patch Dataset. create_spectrogram_dataset.ipynb
2. Train pixel classifier. train_spectrogram_classifier.ipynb
3. Train weakly-labeled patch classifier. train patch classifier.ipynb

The outputs of model training are a temporal pixel and a patch classifier. These are stored in the models directory.

Site Detection

1. Generate population-limited area. generate_populated_dltiles.ipynb
2. Deploy pixel + patch classifier inference on Descartes. descartes_spectrogram_run_withpop.ipynb
3. Detect candidates on Descartes. descartes_candidate_detect.ipynb
4. Filter pixel candidates by patch classifier intersection. query_patch_classifier.ipynb

The end product of this site detection flow is a set of intersection candidate points stored in the candidate_sites/{model_version} directory. These sites are then manually validated, and saved in the sampling_locations directory.

Metadata and Monitoring

1. Generate metadata for confirmed sites. generate_metadata.ipynb
2. Generate contours for confirmed sites on Descartes. descartes_contour_run.ipynb

These outputs are pushed directly to the API and are not stored locally.