  Illegal landfills are a critical issue due to their environmental, economic,
and public health impacts. This study leverages aerial imagery for
environmental crime monitoring. While advances in artificial intelligence and
computer vision hold promise, the challenge lies in training models with
high-resolution literature datasets and adapting them to open-access
low-resolution images. Considering the substantial quality differences and
limited annotation, this research explores the adaptability of models across
these domains. Motivated by the necessity for a comprehensive evaluation of
waste detection algorithms, it advocates cross-domain classification and
super-resolution enhancement to analyze the impact of different image
resolutions on waste classification as an evaluation to combat the
proliferation of illegal landfills. We observed performance improvements by
enhancing image quality but noted an influence on model sensitivity,
necessitating careful threshold fine-tuning.
