Keep Wales Tidy/Keep Scotland Beautiful - Towards Data-Driven Litter-Free Streets

Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges.

This data study involved the use of litter datasets to establish a focused proof of concept (PoC) for automatic litter detection that can potentially replace the manual component of the local environmental audit and management system (LEAMS) methodology. Specifically, the study has been carried out using several datasets comprising over 16,800 images of various types and having various levels of annotations. The images were broadly categorised into (1) Images with complete annotation, (2) Images with incomplete annotation, (3) Duplicate images and (4) Non-duplicate images. To better evaluate the datasets, an initial systematic data analysis was performed to identify any potential distinctions and/or similarities between the various subsets of the datasets. In particular, the distribution of the annotations and categories, as well as any potential biases were taken into consideration during the initial systematic data analysis. The initial analysis allowed for a more robust preparation of the datasets for the data study.