Knowledge Distillation for Urban Tree Image Classification Based on the Diffusion Model

Abstract

As urban areas continue to emerge as significant contributors to global carbon emissions, the classification of urban street trees has gained increasing importance in carbon sequestration research. By precisely identifying and categorizing different tree species, the carbon absorption capabilities of urban vegetation can be evaluated more accurately. This understanding is essential for addressing the growing environmental challenges posed by carbon emissions, as urban trees play a crucial role in absorbing carbon dioxide and mitigating the effects of climate change. In this study, a Feature Distillation Based on Diffusion Model (FDBD) framework was proposed, utilizing state-of-the-art image classification technology to enhance the accuracy of urban tree species identification. The framework utilized knowledge distillation, a process where a smaller, more efficient “student” model is trained to mimic the performance of a larger, more complex “teacher” model, significantly reducing computational demands while maintaining high accuracy. The model’s effectiveness has been validated through combination experiments with a selected backbone model, achieving promising results. This approach not only enhances the understanding of urban trees’ carbon sequestration potential but also provides crucial insights for policymakers. By facilitating precise tree classification, it empowers urban planners to implement more informed and targeted strategies for carbon reduction, ultimately promoting more sustainable urban environments.