人本都市環境需要具備以行人為本之公共空間與完善之步行設施。在印尼，此原則受到《Indonesian Traffic and Road Transportation Act Number 22, 2009》之支持，其規定所有供公眾通行之道路皆須設置行人設施。依據印尼公共工程與住宅部所頒布之《Technical Planning of Pedestrian Facilities No. 02/SE/M/2018》，人行道最小寬度須達 1.5 m。然而，都市道路普遍存在三種情況：具備足夠空間卻未設置人行道、已設置人行道但寬度不足 1.5 m，以及完全缺乏設置人行道之空間。現有研究多著重於人行道之偵測或其狀況評估，但鮮少探討在既有土地條件限制下，是否具備符合最低標準之足夠空間。
為填補此研究缺口，本研究提出一套整合無人機影像道路語意分割、緩衝區分析，以及道路權狀向量資料之方法，其中向量資料係由印尼 Ministry of Agrarian Affairs and Spatial Planning/National Land Agency 提供。研究採用五種深度學習模型，包括 U-Net, FPN, DeepLabV3+, MA-Net 與 SegFormer，並以來自 Kendari City, Sleman Regency 與 Tainan City 之無人機影像進行訓練。測試則於 Pematang Siantar City 進行，使用空間解析度分別為 4.42、8.84、13.26 與 17.68 cm/pixel 之正射影像。
透過五種深度學習模型與四種空間解析度之組合，共產生 20 組分割結果。單因子重複量數變異數分析顯示，六組空間解析度比較中有五組達顯著差異，而不同模型間之差異則未達顯著。Fleiss’ kappa 顯示各模型於所有空間解析度下皆達「幾乎完全一致」之結果。後續分析選擇由 SegFormer 於 13.26 cm/pixel 所產生之分割結果，因其在維持較高精度之同時，亦能完整保留道路連通性。
緩衝區與疊圖分析共辨識出 18 個人行道寬度不足 1.5 m 之道路區段，其面積介於 1.001 至 62.834 m²。透過 Google Street View 進一步判釋發現，部分不足區段因鄰近空地或農地，仍可透過土地徵收改善；然而，另一些區段則鄰接永久性建築物，因此不易進行改善。本研究所提出之方法，可在無須大量實地調查之情況下，快速辨識人行空間不足之道路區段。

A humane urban environment requires pedestrian-friendly public spaces and adequate pedestrian facilities. In Indonesia, this principle is supported by the Indonesian Traffic and Road Transportation Act Number 22, 2009, which mandates pedestrian facilities on all public roads. According to the Technical Planning of Pedestrian Facilities No. 02/SE/M/2018, by the Indonesian Ministry of Public Works and Housing, sidewalks must have a minimum width of 1.5 m. However, urban roads commonly exhibit three conditions: sufficient space without sidewalks, sidewalks narrower than 1.5 m, or no available sidewalk space. Existing studies generally detect sidewalks or evaluate their condition, but rarely assess whether sufficient space exists to meet minimum standards under current land constraints.
To address this gap, this study proposes a framework integrating UAV-based road semantic segmentation, buffer analysis, and road certificate vector data obtained from the Indonesian Ministry of Agrarian Affairs and Spatial Planning/National Land Agency. 5 deep learning models, i.e., U-Net, FPN, DeepLabV3+, MA-Net, and SegFormer, were trained using UAV imagery from Kendari City, Sleman Regency, and Tainan City. Testing was conducted in Pematang Siantar City using orthophotos with spatial resolutions of 4.42, 8.84, 13.26, and 17.68 cm/pixel.
Combining 4 kinds of spatial resolution and 5 deep learning models, we have obtained 20 segmentation outputs. One-way repeated measures ANOVA showed significant differences in 5 of 6 comparisons between spatial resolutions, whereas differences between models were insignificant. Fleiss’ kappa indicated “almost perfect” agreement among models. The result selected for further analysis was generated by SegFormer at 13.26 cm/pixel because it preserved complete road connectivity while maintaining high accuracy.
Buffer and overlay analysis identified 18 road segments with sidewalk widths below 1.5 m, ranging from 1.001 to 62.834 m². Interpretation using Google Street View showed that some segments could be improved through land acquisition, whereas others bordered permanent buildings and were impractical to fulfil. The proposed framework enables rapid identification of road segments lacking sufficient sidewalk space without extensive field investigation.

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