This is an application of large language models (llms) and machine learning to provide summary reports of volunteer observations of marine litter using the OSPAR, JRC or NOAA methods for counting beach litter.
The application provides summary reports of exising data and provides forecasts for a region, city or body of water. The llm acts first as a transcriber of test results and then as an assistant to provide a written report and resource package that can be either used directly or integrated into other documents.
The first report proved that a relatively small team could cover a large territory with the correct technology. This remains the case. That is why this project, in its current itteration, should be specifically attractive to stakeholders with an internal data-science team. Stakeholders with experience in python or R will feel right at home in the development environment and will be the source of many improvements. For those interested in learning the basics, we are here to coach them and help them in their projects.
The challenge in front of us is to transform the observations into accurate and actionable information for two different purposes. There is the administration of the territory and identification of priorities with regard to plastics in the environment. Then, once the priorities have been established these new indicators must be reported on. This project concerns the infrastructure and processes necessary to identify priorities and develop indicators to reduce plastics in the environment from litter density counts.
The inventory data is used to forecast the density of litter based on historical results and topographic features within 1 500 m of each survey location.
National and regional assessemtns
- national shoreline litter survey : https://github.com/hammerdirt-analyst/IQAASL-End-0f-Sampling-2021
- Lake Geneva survey : https://associationsauvegardeleman.github.io/plastock/index.html
Education and research
- EPFL solid waste engineering (annual field excercise with post grad students) : https://hammerdirt-analyst.github.io/solid-waste-team/titlepage.html#
- published article : https://github.com/hammerdirt-analyst/landuse
- implimenting bayes theorem (initial version) : https://hammerdirt-analyst.github.io/finding-one-object/titlepage.html
contact analyst at hammerdirt