Can we use deep learning models to identify the functionality of plastics from space?

Abstract

The function of plastics is an important issue, especially since it determines whether or not they can be recycled. This study presents a two-stage workflow to identify the functions of plastic materials on land surfaces using a deep learning model trained with Sentinel-2 satellite images. First, a classification map identifying 10 distinct plastic types was obtained by evaluating spaceborne hyperspectral PRISMA data. Then, different deep learning algorithms were used to assign functions to the initially classified plastic targets based on the RGB information extracted from Sentinel-2 satellite images. A total of 1,645 plastic polygons were manually labeled on RGB images of Sentinel-2, and the following five main function types were identified: plastic cover sheeting for construction areas, greenhouse structures, photovoltaic panels (PVs), roof materials, and sport field floorings. By comparing three state-of-the-art deep learning models, including GoogLeNet, VGGNet, and ResNet, an overall accuracy of 78% was achieved on the test dataset using the VGG-13 network. The model performed well in identifying PVs, greenhouses, and construction sites, with F1 scores of 0.85, 0.77, and 0.71 respectively. The performance of the model in identifying roofs and sport field floorings was lower, with respective F1 scores of 0.57 and 0.59. Overall, the results show that the proposed workflow using deep learning algorithms trained on Sentinel-2 images has a great potential to identify the function of plastic materials on land surfaces.

Keywords

• Deep learning
• Environmental management
• Plastic detection
• Plastic functionality
• Sentinel-2