  This study combines photo metadata and computer vision to quantify where
uncollected litter is present. Images from the Trash Annotations in Context
(TACO) dataset were used to teach an algorithm to detect 10 categories of
garbage. Although it worked well with smartphone photos, it struggled when
trying to process images from vehicle mounted cameras. However, increasing the
variety of perspectives and backgrounds in the dataset will help it improve in
unfamiliar situations. These data are plotted onto a map which, as accuracy
improves, could be used for measuring waste management strategies and
quantifying trends.
