Street view imagery, aided by advancements in image quality and accessibility, has emerged as a valuable resource for urban analytics research. Recent studies have explored its potential for estimating lowest floor elevation (LFE), offering a scalable alternative to traditional on-site measurements, crucial for assessing properties' flood risk and damage extent. While existing methods rely on object detection, the introduction of image segmentation has broadened street view images' utility for LFE estimation, although challenges still remain in segmentation quality and capability to distinguish front doors from other doors. To address these challenges in LFE estimation, this study integrates the Segment Anything model, a segmentation foundation model, with vision language models to conduct text-prompt image segmentation on street view images for LFE estimation. By evaluating various vision language models, integration methods, and text prompts, we identify the most suitable model for street view image analytics and LFE estimation tasks, thereby improving the availability of the current LFE estimation model based on image segmentation from 33% to 56% of properties. Remarkably, our proposed method significantly enhances the availability of LFE estimation to almost all properties in which the front door is visible in the street view image. Also the findings present the first baseline and comparison of various vision models of street view image-based LFE estimation. The model and findings not only contribute to advancing street view image segmentation for urban analytics but also provide a novel approach for image segmentation tasks for other civil engineering and infrastructure analytics tasks.

该研究通过将Segment Anything模型与视觉语言模型相结合，对街景图像进行文本提示图像分割，从而改进了街景图像的分割质量及其在低洼地区的可见性估计模型，从33%提高到56%。这个方法不仅推进了城市分析中的街景图像分割，还为其他土木工程和基础设施分析任务的图像分割提供了新的方法。

ELEV-VISION-SAM: 综合视觉语言和基础模型用于建筑物最低地板高程的自动估计