Identifying illegal waste dumps scenes using deep learning on aerial and satellite images

Abstract

Illegal waste dumping is a great threat to the environment and the health of people worldwide. Through the application of a deep learning concept, this study introduces an innovative discovery of identifying scenes of illegal waste dump (IWD) utilizing aerial and satellite imagery (ASI). The study combines an advanced feature pyramid network (FPN) and the robust ResNet101 architecture to optimize the model to manage the complicated, variable data that is characteristic of remote sensing (RS) data. Significantly, this model attained a remarkable recall of 0.95 and AUC of 0.95, showing the model's superiority in recognising actual waste scenes, which is of great essence in the effort to conserve the environment. Although the accuracy was relatively low (0.39), the high recall level will make sure that the model will have minimum false alarms in illegal dumping areas, which is needed to effectively manage waste and sustain the environment. Such findings not only encourage the technological opportunities in waste management but also provide significant knowledge regarding the future enhancement and implementation of the sources in environmental observation systems