The quality of the data plays a significant role in the success of a ML model. A well-curated dataset with a diverse range of examples will lead to better performance compared to a dataset with a limited number of examples. It is important to gather a large and representative dataset to ensure the model can generalize well to new unseen data. The goal of this challenge is to improve the performance of object detection in aerial images by leveraging the power of multi-source data and advanced techniques, and to demonstrate the potential of these approaches for improving waste management efforts. Main outcome of this challenge is the preparation of a pipeline for visualization of provided trained waste detector.