Data-Driven Color Quantification and Conservation Palette Generation for Vernacular Built Heritage: A Case Study of Central Fujian Tubao

Architectural color contributes to the regional identity, material continuity, and conservation of vernacular built heritage. However, existing studies rarely establish a quantitative workflow that integrates image-color reliability control, regional comparison, and conservation-oriented palette generation. This study develops a data-driven color analysis framework for Central Fujian Tubao, combining field color measurements, image correction, semantic segmentation, color clustering, and multi-indicator evaluation. The framework integrates CIE76-guided image color calibration, DeepLabv3+ building-body segmentation, and K-means clustering (C-D-K workflow), followed by entropy-weighted evaluation using the technique for order preference by similarity to an ideal solution (TOPSIS). The analysis used 36 images from 24 Central Fujian Tubao, 68 calibration pairs, and 216 first-level dominant colors. Post-correction color differences at all 68 calibration points satisfied the predefined operational CIE76 criterion of ΔE*ab ≤ 6. Both CIE76 and CIEDE2000 indicated reduced mean calibration residuals. These reflect calibration agreement; no independent color-validation set was used. The three Tubao groups shared low- to medium-chroma warm grayish-brown, earthy-yellow, and reddish-brown tones, with descriptive variation in palette combinations and proportions. Building-level comparisons across 24 Tubao did not detect statistically significant regional differences in L*w, a*w, or b*w. TOPSIS classified candidate colors into core and auxiliary conservation reference palettes. The workflow reproducibly transforms field-constrained architectural images into quantitative color evidence and preliminary conservation references for material comparison and regional coordination; restoration use requires material, historical, and on-site verification.