基于CLIP嵌入和SVM的阿联酋住宅建筑文化遗产风格分类研究

通过降维和聚类，再使用SVM分类器进行自动分类，在八个风格簇上达到了98%的分类准确率，优于现有研究。

Multimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVM

The analysis and classification of cultural heritage architectural styles remain challenging due to the complexity of visual images of buildings, which are highly relied on in traditional CNN-based classification approaches in comparison to textual descriptions, and the relative lack of non-western region-specific datasets. This paper addresses this gap by proposing a multimodal machine learning framework to analyze and classify Emirati residential architecture using OpenAI's CLIP model. We integrate visual features from images and textual features from expert descriptions into a unified 512-dimensional embedding, followed by dimensionality reduction with UMAP for visualization and unsupervised clustering using K-Means. Cluster labels, which are derived from manual analysis of the K-Means clusters, are used to train an SVM classifier for automated architectural style classification. Our approach achieves a classification accuracy of 98% across eight identified style clusters, higher than every other study in the literature, demonstrating the effectiveness of combining visual and textual modalities. Overall, this paper highlights the potential of using multimodal AI to support architectural heritage analysis, offering scalable and interpretable tools for exploring regional architectural identities.

本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：CLIP、SVM、多模态、文化遗产，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始信息发布自一手权威信源「arXiv: OpenAI」（Tier-1 一手发布），具备高确定性参考价值。截至目前，全网已有 11 家独立信源对该事件进行交叉印证与跟进报道（含 Gary Marcus、techcrunch、Latent Space (swyx) 等），属于近期高关注度行业信号。