Illegal waste disposal sites pose significant ecological and public-health risks yet remain difficult to track with traditional field inspections. We propose a multi-agent detection framework that fuses textural, spectral, and contextual cues from medium-resolution satellite imagery for this work. Three specialized agents - Waste-Pile, Road, and Industry detectors - are implemented as YOLO (You Only Look Once) convolutional models that generate partial hypotheses, which are then hierarchically aggregated through rule weights learned from expert-labelled samples. The system provides an interpretable set of object relations, allowing regulators to trace how individual cues contribute to the final decision. The method was validated on an independent test area near Taromske (Dnipropetrovsk region, Ukraine) and corroborated by ground surveys. Joint aggregation raised the posterior probability of the primary target cluster from 0.27 (single-detector confidence) to 0.91, while maintaining robustness to label noise and heterogeneous sensor characteristics. Compared with conventional CNN baselines, the proposed approach delivers three key advantages: explicit explainability of outputs, transferability to 10 m spatial resolution without extensive retraining, and seamless integration of heterogeneous evidence sources. The proposed framework can serve as a cost-effective backbone for regional and national waste-monitoring systems. Future work will focus on near-real-time processing of Sentinel-2 time series, incorporation of hyperspectral and thermal methane indicators to assess remediation stages, and extension of the array of features to other anthropogenic disturbances such as open-pit mining and construction debris.