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Urban building extraction is an important research direction for high-resolution optical remote sensing image understanding and target recognition. Realizing accurate automatic building extraction has important application value and practical significance for the acquisition and update of basic urban geographic information. Due to the complexity of urban scenes and the diversity of building forms, it is difficult to fully express the characteristics of urban buildings and the generalization ability of samples is insufficient, which becomes a bottleneck problem for automatic extraction of urban buildings. In this study, a multi-modal morphological sequence feature synergy method is proposed to fully utilize the advantages of each morphological sequence feature from different modes and jointly mine the high-dimensional spatial information of urban buildings. On this basis, we introduce multi-source a priori information and develop an adaptive segmentation model method based on multi-source a priori information to achieve automatic recognition of urban buildings, thus avoiding the limitations such as errors and low efficiency brought by manual threshold selection. The process of urban building extraction method proposed in this paper is mainly divided into four steps. Firstly, the differential morphological structure sequence features and differential morphological attribute sequence features of remote sensing images are calculated based on the high-resolution remote sensing images respectively. Secondly, the feature selection model is constructed to optimize the differential morphological structure sequence features and differential morphological attribute sequence features respectively. Then, the adaptive segmentation model is constructed based on the multi-source a priori information products and the adaptive segmentation of the preferred features is performed to obtain the initial information of urban buildings. Finally, the voting method is used to fuse the initial information of urban buildings at decision level to obtain the final urban building extraction results. The performance of the proposed method shows that the average extraction accuracy and kappa coefficient of the research method in this paper are 91.3% and 0.87, which are 7.8%, 5.5% and 0.1 and 0.07 higher than 85.7%, 83.5% and 0.81, 0.78 of DMPs and DAPs extraction methods, respectively, thus demonstrating the effectiveness of the method in automatic extraction of urban buildings in this paper. The method in this paper achieves rapid, automated and high-precision urban building information acquisition and update, and provides a method reference template for rapid building detection and update in more cities. In the subsequent research, further quantitative evaluation of each type of a priori information products is needed to further clarify the role of different information products in automatic building extraction, so as to further improve the accuracy and automation of building extraction.