Benchmarking illegal waste dumping detection: A public video dataset and a reference baseline

Illegal waste dumping represents one of the most pervasive threats to environmental health and the well-being of terrestrial ecosystems. As widely documented in the literature, this phenomenon is deeply rooted in cultural factors and civic behavior, making early detection a crucial component in effective prevention strategies. Recent advances in artificial intelligence offer promising avenues for automatically identifying illicit dumping actions; however, progress in this direction is severely hindered by the scarcity of publicly available video datasets tailored to the Illegal Waste Dumping Detection (IWDD) task. To address this critical gap, we introduce Mivia-IWDD-500, a novel, fully balanced dataset comprising 500 videos: 250 positive samples and 250 negative samples. The positive class is further evenly divided into 125 videos depicting static disposal events, intentional and spatially localized acts such as depositing garbage bags or bulky items, and 125 videos capturing dynamic disposal behaviors, which involve brief, spontaneous, and spatially dispersed actions such as discarding waste while walking or from a moving vehicle. Alongside the dataset, we present a baseline model designed to serve as a reference point for future research. The proposed baseline model achieves an F1[jls-end-space/]-score of 0.79, demonstrating the viability of the dataset and establishing a solid foundation for subsequent advancements.