BuildingSense: a new multimodal building function classification dataset

Abstract. Building function is a description of building usage. The accessibility of its information is essential for urban research, including urban morphology, urban environment, and human activity patterns. Existing building function classification methodologies face two major bottlenecks: (1) poor model interpretability and (2) inadequate multimodal feature fusion. Although large models with strong interpretability and efficient multimodal data fusion capabilities offer promising potential for addressing the bottlenecks, they remain limited in processing multimodal spatial datasets. Their performance in building function classification is therefore also unknown. To the best of our knowledge, there is a lack of multimodal building function classification datasets, which results in the challenge of effectively performing their performance evaluation. Meanwhile, prevailing building function categorization schemes remain coarse, which hinders their ability to support finer-grained urban research in the future. To bridge the gap, we constructed a novel multimodal and fine-grained dataset—BuildingSense—for building function classification. Based on BuildingSense, we evaluated the performance of four state-of-the-art large models from the perspective of classification outcomes and reasoning processes. The results demonstrate that large models can effectively comprehend multimodal spatial data, challenging the conventional concept. Based on that, three directions for future research can be key: (1) build a categorized inference example database, (2) develop cost-effective classification models, and (3) quantify the confidence of model outputs. Our findings not only provide insights into the development of subsequent large model-based classification methods but also contribute to the advancement of multimodal fusion-based classification methods. The dataset and code of this paper can be accessed through https://doi.org/10.6084/m9.figshare.30645776.v2 (Su et al., 2025a).