This paper argues that AI-enabled analysis of street-view imagery, complemented by performance-gated machine-learning imputation, provides a viable pathway for generating building-specific elevation data at regional scale for flood risk assessment. We develop and apply a three-stage pipeline across 18 areas of interest (AOIs) in Texas that (1) extracts LFE and the height difference between street grade and the lowest floor (HDSL) from Google Street View imagery using the Elev-Vision framework, (2) imputes missing HDSL values with Random Forest and Gradient Boosting models trained on 16 terrain, hydrologic, geographic, and flood-exposure features, and (3) integrates the resulting elevation dataset with Fathom 1-in-100 year inundation surfaces and USACE depth-damage functions to estimate property-specific interior flood depth and expected loss. Across 12,241 residential structures, street-view imagery was available for 73.4% of parcels and direct LFE/HDSL extraction was successful for 49.0% (5,992 structures). Imputation was retained for 13 AOIs where cross-validated performance was defensible, with selected models achieving R suqre values from 0.159 to 0.974; five AOIs were explicitly excluded from prediction because performance was insufficient. The results show that street-view-based elevation mapping is not universally available for every property, but it is sufficiently scalable to materially improve regional flood-risk characterization by moving beyond hazard exposure to structure-level estimates of interior inundation and expected damage. Scientifically, the study advances LFE estimation from a pilot-scale proof of concept to a regional, end-to-end workflow. Practically, it offers a replicable framework for jurisdictions that lack comprehensive Elevation Certificates but need parcel-level information to support mitigation, planning, and flood-risk management.

翻译：本文论证了由人工智能驱动的街景图像分析，辅以性能门控的机器学习插值，为在区域尺度上生成建筑特定的高程数据以进行洪水风险评估提供了一条可行路径。我们开发并应用了一个三阶段流程，覆盖德克萨斯州的18个感兴趣区域，该流程:(1) 使用 Elev-Vision 框架从谷歌街景图像中提取最低楼层高程及街道标高与最低楼层之间的高差，(2) 利用基于16个地形、水文、地理和洪水暴露特征训练的随机森林和梯度提升模型，对缺失的高差值进行插补，(3) 将生成的高程数据集与 Fathom 百年一遇淹没面以及美国陆军工程兵团水深-损失函数相结合，以估算物业特定的室内洪水深度和预期损失。在12,241栋住宅建筑中，73.4%的地块可获得街景图像，其中49.0%（5,992栋建筑）成功直接提取了最低楼层高程/高差值。在13个插补结果可接受的感兴趣区域中，保留了交叉验证性能合理的插补结果，所选模型的 R 平方值介于0.159至0.974之间；另有五个感兴趣区域因插补性能不足而被明确排除在预测之外。结果表明，基于街景的高程映射并非对每处房产都普遍可用，但其可扩展性足以通过超越危害暴露、提供结构级的内涝深度和预期损失估算，从而显著改善区域洪水风险表征。从科学角度看，本研究将最低楼层高程估算从试点规模的概念验证推进到区域性的端到端工作流程。从实践角度看，它为缺乏全面高程证书但需要地块级信息以支持减灾、规划和洪水风险管理的管辖区提供了一个可复制的框架。