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.
Mature use case
Aquatic Litter Drones
Aquatic Litter monitoring system using drones
Aim
Development actions during iMagine
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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
Development timeline
The development timeline begins with integrating the existing AI model with the iMagine platform and configuring the API for basic inference. Data storage capabilities within the platform will be leveraged. The next step involves developing the service to enable authentication and retraining. More data will be added, and a wider range of models will be made available. The model output will be enhanced with additional metadata. Documentation and step-by-step guides will be created to assist users and promote the service. Once the updated service is accessible to customers, such as through the EOSC Marketplace, user feedback will be monitored and incorporated into future improvements.
