  This study aims to compare the effectiveness of a robust ensemble model with
the state-of-the-art ONE-PEACE Large Language Model (LLM) for accurate
detection of sidewalks. Accurate sidewalk detection is crucial in improving
road safety and urban planning. The study evaluated the model's performance on
Cityscapes, Ade20k, and the Boston Dataset. The results showed that the
ensemble model performed better than the individual models, achieving mean
Intersection Over Union (mIOU) scores of 93.1\%, 90.3\%, and 90.6\% on these
datasets under ideal conditions. Additionally, the ensemble model maintained a
consistent level of performance even in challenging conditions such as
Salt-and-Pepper and Speckle noise, with only a gradual decrease in efficiency
observed. On the other hand, the ONE-PEACE LLM performed slightly better than
the ensemble model in ideal scenarios but experienced a significant decline in
performance under noisy conditions. These findings demonstrate the robustness
and reliability of the ensemble model, making it a valuable asset for improving
urban infrastructure related to road safety and curb space management. This
study contributes positively to the broader context of urban health and
mobility.
