城市土地利用信息在城市管理、政府政策制定及人口活动监测中具有重要作用。然而，由于城市系统的复杂性，准确划分城市功能区仍具挑战性。许多研究聚焦于仅基于高空间分辨率（HSR）遥感影像或社交媒体数据提取特征来进行城市土地利用分类，但因缺乏可用模型，极少有研究同时考虑两类特征。本文提出一种新型场景分类框架，通过融合概率主题模型与支持向量机，识别交通分析区层级下的主导城市土地利用类型。该框架内构建了土地利用词汇表，融合了来自HSR影像的自然-物理特征以及多源社交媒体数据的社会经济语义特征。除与人工解译数据对比外，我们设计了多项实验，测试不同语义特征组合下所提模型的土地利用分类精度。分类结果（总体精度=0.865，Kappa=0.828）表明，将多源地理空间数据提取的特征作为语义特征用于训练分类模型具有显著有效性。该方法可为城市规划者分析精细城市结构及监测土地利用变化提供支持，未来将进一步融合多源数据以完善该框架。

Urban land use information plays an important role in urban management, government policy-making, and population activity monitoring. However, the accurate classification of urban functional zones is challenging due to the complexity of urban systems. Many studies have focused on urban land use classification by considering features that are extracted from either high spatial resolution (HSR) remote sensing images or social media data, but few studies consider both features due to the lack of available models. In our study, we propose a novel scene classification framework to identify dominant urban land use type at the level of traffic analysis zone by integrating probabilistic topic models and support vector machine. A land use word dictionary inside the framework was built by fusing natural–physical features from HSR images and socioeconomic semantic features from multisource social media data. In addition to comparing with manual interpretation data, we designed several experiments to test the land use classification accuracy of our proposed model with different combinations of previously acquired semantic features. The classification results (overall accuracy = 0.865, Kappa = 0.828) demonstrate the effectiveness of our strategy that blends features extracted from multisource geospatial data as semantic features to train the classification model. This method can be applied to help urban planners analyze fine urban structures and monitor urban land use changes, and additional data from multiple sources will be blended into this proposed framework in the future.