Recent progress in deep neural networks has spurred significant development of image-based trash classification literature. These methods predominately use transfer learning to achieve state-of-the-art results. In this contribution, a new methodology is introduced that uses metadata fields such as location and time-based traffic intensity to assist existing image-based classifiers. We curated ISBNet, a dataset which contains 889 images and their associated metadata, distributed over 5 classes (paper, plastic, cans, tetra pak, and landfill). This dataset was used to develop our model, ThanosNet, which is superior to current state-of-the-art, image-based, trash classification models. Although ISBNet is localized to one user community, the general methodology developed here is applicable to a wide array of consumer contexts.