Recognition of Building Structural Types Using Multisource Remote Sensing Data and Prior Knowledge

Abstract

Highlights: What are the main findings? Integrating remote sensing imagery with multidimensional prior knowledge constructs a more comprehensive feature space, significantly improving the discrimination of different building structural types. After feature selection and systematic comparison of multiple machine learning algorithms, XGBoost is identified as the optimal classifier, achieving the highest weighted F1 score of 78.62%. What are the implications of the main findings? The proposed framework alleviates the limitations of building structural classification based solely on single-source remote sensing imagery. The findings indicate that integrating multisource remote sensing data with prior knowledge enables a more comprehensive characterization of building structural differences, thereby improving the stability and overall performance of BST classification. Accurate identification of building structural types (BSTs) is essential for seismic vulnerability assessment and disaster risk management. Traditional field survey methods are constrained by high costs, low efficiency, and limited scalability. Although remote sensing-based approaches offer strong potential for large area applications, they are often hindered by limited spatial resolution, spectral confusion, and difficulties in capturing information related to internal building structures. To address these limitations, this study proposes a BST classification approach that integrates remote sensing image features with multisource prior knowledge. In addition to conventional remote sensing features derived from building shape, spectral, and texture, multiple types of prior information are incorporated to compensate for the insufficient structural discriminative capability of remote sensing imagery alone. These include distance to roads, terrain conditions, building height, population, gross domestic product (GDP), and nighttime light intensity. Considering the limited number of labeled samples and the high dimensionality of features, fourteen mainstream machine learning algorithms are systematically evaluated. Through feature selection and model optimization, XGBoost is identified as the most effective classifier, achieving the highest weighted F1 score of 78.62%. The results demonstrate that, under the same machine learning model settings, models trained solely on remote sensing features consistently underperform those integrating multisource features combined with feature selection, confirming the effectiveness of synergistically fusing remote sensing features with prior knowledge for improving overall BST classification performance. Further analyses demonstrate that different groups of remote sensing features and prior knowledge are associated with reductions in misclassification between specific BSTs. Compared with approaches based exclusively on remote sensing imagery, the proposed method exhibits higher and more balanced classification performance across different BSTs, with particularly notable advantages for structure categories that are difficult to distinguish using single-source remote sensing features. This study provides the foundation for subsequent seismic vulnerability analysis and related risk studies.