Street view imagery 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 expanded the utility of street view images 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 (VLMs) to conduct text-prompt image segmentation on street view images for LFE estimation. By evaluating various VLMs, integration methods, and text prompts, the most suitable model was identified for street view image analytics and LFE estimation tasks, thereby improving the coverage of the current LFE estimation model based on image segmentation from 33% to 56% of properties. Remarkably, our proposed method, ELEV-VISION-SAM, significantly enhances the availability of LFE estimation to almost all properties in which the front door is visible in the street view image. In addition, the findings present the first baseline and quantified comparison of various vision models for 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.

中文翻译：

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集成的视觉语言和地基模型，用于自动估算建筑物的最低楼层标高


街景图像已成为城市分析研究的宝贵资源。最近的研究探索了它估算最低楼层标高 （LFE） 的潜力，为传统的现场测量提供了一种可扩展的替代方案，对于评估物业的洪水风险和破坏程度至关重要。虽然现有方法依赖于对象检测，但图像分割的引入扩大了街景图像在 LFE 估计中的实用性，尽管分割质量和区分前门和其他门的能力仍然存在挑战。为了解决 LFE 估计中的这些挑战，本研究将分割基础模型 Segment Anything 模型与视觉语言模型 （VLM） 集成在一起，对街景图像进行文本提示图像分割以进行 LFE 估计。通过评估各种 VLM、集成方法和文本提示，确定了最适合街景图像分析和 LFE 估计任务的模型，从而将当前基于图像分割的 LFE 估计模型的覆盖率从 33% 提高到 56%。值得注意的是，我们提出的方法 ELEV-VISION-SAM 显着提高了 LFE 估计对几乎所有在街景图像中可以看到前门的房产的可用性。此外，这些发现还提出了用于基于街景图像的 LFE 估计的各种视觉模型的第一个基线和量化比较。该模型和发现不仅有助于推进用于城市分析的街景图像分割，还为其他土木工程和基础设施分析任务的图像分割任务提供了一种新颖的方法。