近年來許多研究透過深度學習建構都市量化指標，作為後續相關議題結合應用。基於臺灣廣告招牌密度高、樣式多元，本研究旨在應用常見深度學習（Deep Learning）之語義分割（Semantic Segmentation）以及物件偵測（Object Detection）方式，量化街景影像中廣告招牌街道空間聚集狀態，並探討研究區域空間分布型態。成果顯示，Deeplab v3+模型訓練平均交併比（Mean Intersection over Union, MIoU）值可達83%；YOLOv7模型精確率（Precision）與召回率（Recall）分別可達91.7%與87.1%，顯示有一定辨識成效，亦可與實際分布情形相符合。本研究可為後續廣告招牌進一步應用與探勘，以及相關領域結合應用之契機。

In recent years, deep learning has been used to construct quantitative indicators relevant to urban areas. Given the diverse array of dense billboards in Taiwan, this study aims to utilize deep learning techniques, including semantic segmentation and object detection, in conjunction with street view imagery to quantify the spatial distribution of signboards. Moreover, this study examines the spatial distribution patterns within the research area. The results demonstrate that the MIoU value of Deeplab v3+ model achieves 83%, while the Precision and Recall of YOLOv7 model achieves 91.7% and 87.1%. The analysis of spatial distribution patterns results align well with the actual distribution of billboards. This study can serve as a foundation for further exploration and application of billboards, as well as for integration with other related fields.