To establish an operational service at the iMagine platform for ingestion, storage, analysis and processing of drone images, observing litter floating at surface waters in seas, rivers and lakes, and lying at beaches and shores, delivering standardised classified litter data sets, which are fit for purpose of environmental management and indicators.
Aim
Development actions during iMagine
1
1
Formulating and adopting standard protocols for drone observation and for classifying observed litter objects, making use of EU guidelines (TG-ML)
2
2
Refining processing methodology and setting up operational environment at iMagine platform, with AI image analysis service and databases for ingestion of ‘raw’ and annotated drone images, and classified litter data, resulting from the image processing
3
3
Developing guidance and training material for uptake of litter observation by drones and observed image processing data by researchers, environmental agencies, NGOs and other users (including citizen science)
4
4
Reaching out to users for uptake and provide support and training.
Objective and challenge
This use case aims to create a functional service on the iMagine platform that can ingest, store, analyze, and process drone images to identify and classify floating litter in bodies of water and on beaches. The goal is to provide standardized data sets on litter for environmental management purposes. The technology behind this service involves using UAV surveys at different altitudes and employing two CNN deep neural networks to quantify and characterize the observed litter. This approach has been successfully applied in various countries through collaborations with the World Bank Group and NGOs, supporting local stakeholders and clean-up operations. The training subset of the model has been made available on Zenodo.
However, the current service needs more user-friendliness and requires several manual steps. To address these issues, the project aims to incorporate the following features into the service using the iMagine platform:
- Easy storage and access to custom data
- User-friendly API
- Ready-to-use environment (e.g., Docker)
- Information on image processing requirements
- Simplified usage of provided test data for retrained mode
- Documentation and step-by-step guides
Timeline and progress
completion
100%
start date
Sep 2022
end date
Aug 2025
Sample Results
Check the sample input images, their resulting classification plots, and the tabular results of the classification.
Image credit: Naja Bertolt Jensen / Ocean Image Bank