通过融合多模态数据可以更准确地绘制城市土地利用格局。然而，许多研究仅考虑地块内的社会经济与物理属性，忽略了多模态数据带来的空间交互作用及不确定性。为解决上述问题，我们构建了一种多模态数据融合模型（MDFNet），从多模态数据中提取自然物理、社会经济及空间连通性辅助信息。同时，建立基于广义加性模型与可学习权重模块的不确定性分析框架，以解释数据驱动的不确定性。选取深圳作为示范区域，实验结果表明该方法具有有效性，测试准确率达到0.882，Kappa系数为0.858。不确定性分析显示，遥感数据、社会感知数据和出租车轨迹数据对整体任务的贡献率分别为0.361、0.308和0.232。研究还揭示了多模态数据在不同土地利用类别中的协同机制，为城市分布格局的精准、可解释制图提供了有效方法。

Urban land use patterns can be more accurately mapped by fusing multimodal data. However, many studies only consider socioeconomic and physical attributes within land parcels, neglecting spatial interaction and uncertainty caused by multimodal data. To address these issues, we constructed a multimodal data fusion model (MDFNet) to extract natural physical, socioeconomic, and spatial connectivity ancillary information from multimodal data. We also established an uncertainty analysis framework based on a generalized additive model and learnable weight module to explain data-driven uncertainty. Shenzhen was chosen as the demonstration area. The results demonstrated the effectiveness of the proposed method, with a test accuracy of 0.882 and a Kappa of 0.858. Uncertainty analysis indicated the contributions in overall task of 0.361, 0.308, and 0.232 for remote sensing, social sensing, and taxi trajectory data, respectively. The study also illuminates the collaborative mechanism of multimodal data in various land use categories, offering an accurate and interpretable method for mapping urban distribution patterns.