The field of environmental monitoring and waste management is rapidly advancing with the application of deep learning techniques. Recent research has focused on developing innovative methods for monitoring marine litter, detecting illegal landfills, and classifying soil types using convolutional neural networks (CNNs) and other machine learning algorithms. The use of unmanned aerial vehicles (UAVs) and satellite images has improved the accuracy and efficiency of these methods. Additionally, the development of new datasets such as the AerialWaste Dataset and BuzzSet has enabled the training of more accurate models for waste detection and pollinator monitoring. Noteworthy papers include: Automated Landfill Detection Using Deep Learning, which achieved 92.33% accuracy in detecting illegal landfills using lightweight deep learning models. FusionSort, which introduced an enhanced neural architecture for waste sorting and achieved significant performance gains. BuzzSet, which provided a new large-scale dataset for pollinator detection and achieved high F1-scores for honeybee and bumblebee classes.