Multi-level fusion of CNN and Mamba for floatable plastics mapping along high-speed railway: a case study of the Beijing-Shanghai high-speed railway, China

Under strong wind conditions, floatable plastics (i.e., plastic mulching films, plastic greenhouses, plastic dust-proof nets, etc.) are prone to be blown up to hang on the power transmission lines of high-speed railways, leading to the misfunction or even train service suspension. Therefore, timely and accurate mapping of these floatable plastics is of great significance to both railway administrations and the safety of passengers onboard. However, the potential of remote sensing has not been well justified in this study field. To tackle this issue, we take Beijing-Shanghai high-speed railway as an example, which is the busiest railway in China, and propose a novel deep learning based semantic segmentation model to map floatable plastics from very high-resolution optical satellite imagery. Specifically, a well-annotated sample dataset of floatable plastics is prepared, consisting of three typical categories of plastic mulching films, plastic greenhouses and plastic dust-proof nets. Afterwards, a hybrid Convolutional Neural Network-Mamba (CNN-Mamba) network is proposed, which integrates multi-scale convolutions with various local receptive fields and Mamba with global receptive fields into an end-to-end model. Specifically, the Multi-Perspective Fusion Block leverages multi-kernel convolutions to capture multi-scale local features, while the Feature Refinement Module integrates encoder-decoder multi-level features, thereby improving semantic consistency and boundary precision. Experimental results showed that the proposed model has achieved a high performance in floatable plastics mapping with an mIoU of 0.8641 and an average F1-score of 0.9261. Ablation studies have been done to justify the rationality of each module in the proposed hybrid model. Besides, the proposed model also outperformed several CNN-based and Mamba-based networks, not only in floatable plastics mapping but also in two other popular land use land cover datasets. Overall, this study provides an effective pipeline for monitoring the floatable plastics along high-speed railways.