Accurate waste classification is vital for achieving sustainable waste management and reducing the environmental footprint of urbanization. Misclassification of recyclable materials contributes to landfill accumulation, inefficient recycling, and increased greenhouse gas emissions. To address these issues, this study introduces HybridSOMSpikeNet, a hybrid deep learning framework that integrates convolutional feature extraction, differentiable self-organization, and spiking-inspired temporal processing to enable intelligent and energy-efficient waste classification. The proposed model employs a pre-trained ResNet-152 backbone to extract deep spatial representations, followed by a Differentiable Soft Self-Organizing Map (Soft-SOM) that enhances topological clustering and interpretability. A spiking neural head accumulates temporal activations over discrete time steps, improving robustness and generalization. Trained on a ten-class waste dataset, HybridSOMSpikeNet achieved a test accuracy of 97.39%, outperforming several state-of-the-art architectures while maintaining a lightweight computational profile suitable for real-world deployment. Beyond its technical innovations, the framework provides tangible environmental benefits. By enabling precise and automated waste segregation, it supports higher recycling efficiency, reduces contamination in recyclable streams, and minimizes the ecological and operational costs of waste processing. The approach aligns with global sustainability priorities, particularly the United Nations Sustainable Development Goals (SDG 11 and SDG 12), by contributing to cleaner cities, circular economy initiatives, and intelligent environmental management systems.

翻译：准确的垃圾分类对于实现可持续废物管理和减少城市化的环境足迹至关重要。可回收材料的错误分类会导致垃圾填埋场堆积、回收效率低下以及温室气体排放增加。为解决这些问题，本研究提出了HybridSOMSpikeNet，一种混合深度学习框架，它集成了卷积特征提取、可微分自组织和受脉冲启发的时序处理，以实现智能且高能效的垃圾分类。所提出的模型采用预训练的ResNet-152主干网络提取深度空间表征，随后连接一个可微分软自组织映射，以增强拓扑聚类和可解释性。一个脉冲神经头部在离散时间步上累积时序激活，提高了模型的鲁棒性和泛化能力。在一个十类垃圾数据集上进行训练后，HybridSOMSpikeNet取得了97.39%的测试准确率，优于多种先进架构，同时保持了适合实际部署的轻量级计算特性。除了其技术创新，该框架还带来了切实的环境效益。通过实现精确、自动化的废物分拣，它支持更高的回收效率，减少可回收物流中的污染，并最大限度地降低废物处理的生态和运营成本。该方法通过助力建设更清洁的城市、循环经济倡议和智能环境管理系统，与全球可持续发展优先事项，特别是联合国可持续发展目标（SDG 11和SDG 12）保持一致。