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Abstract In high seismic risk regions, it is important for city managers and decision makers to create programs to mitigate the risk for buildings. For large cities and regions, a mitigation program relies on accurate information of building stocks, that is, a database of all buildings in the area and their potential structural defects, making them vulnerable to strong ground shaking. Structural defects and vulnerabilities could manifest via the building's appearance. One such example is the soft‐story building—its vertical irregularity is often observable from the facade. This structural type can lead to severe damage or even collapse during moderate or severe earthquakes. Therefore, it is critical to screen large building stock to find these buildings and retrofit them. However, it is usually time‐consuming to screen soft‐story structures by conventional methods. To tackle this issue, we used full image classification to screen them out from street view images in our previous study. However, full image classification has difficulties locating buildings in an image, which leads to unreliable predictions. In this paper, we developed an automated pipeline in which we segment street view images to identify soft‐story buildings. However, annotated data for this purpose is scarce. To tackle this issue, we compiled a dataset of street view images and present a strategy for annotating these images in a semi‐automatic way. The annotated dataset is then used to train an instance segmentation model that can be used to detect all soft‐story buildings from unseen images.