Super-Resolution Analysis for Landfill Waste Classification

TL;DR

This paper investigates how super-resolution techniques can improve landfill waste classification accuracy using aerial imagery, addressing challenges of domain adaptation and image resolution differences in environmental monitoring.

Contribution

It introduces a cross-domain classification framework that incorporates super-resolution to enhance low-resolution images for better waste detection.

Findings

Super-resolution improves waste classification accuracy.

Model sensitivity varies with image quality, requiring threshold adjustments.

Cross-domain adaptation benefits from super-resolution enhancement.

Abstract

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.