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：本文认为，基于人工智能的街景图像分析，辅以性能门控的机器学习插补，为在区域尺度上生成用于洪灾风险评估的建筑特定高程数据提供了可行途径。我们开发并应用了一套三阶段流程，覆盖得克萨斯州18个感兴趣区域（AOI）：(1) 利用Elev-Vision框架从谷歌街景图像中提取最低楼层高程（LFE）及街道标高与最低楼层的高差（HDSL）；(2) 使用基于16个地形、水文、地理和洪灾暴露特征训练的随机森林和梯度提升模型，对缺失的HDSL值进行插补；(3) 将所得高程数据集与Fathom百年一遇淹没面及美国陆军工程兵团（USACE）水深-损失函数集成，以估算特定财产的室内洪水深度和预期损失。在12,241栋住宅结构中，73.4%的地块有可用街景图像，其中49.0%（5,992栋结构）成功实现了LFE/HDSL的直接提取。插补保留在13个交叉验证性能可接受的AOI中，所选模型的决定系数（R²）在0.159至0.974之间；五个AOI因性能不足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适用于每处房产，但其可扩展性足以通过从洪灾暴露分析转向基于结构的室内淹没问题深度和预期损失估算，显著改善区域洪灾风险表征。科学上，本研究将LFE估算从概念验证试点推进至区域端到端工作流程。实践上，它为缺乏全面高程证书但需要宗地级信息以支持减灾、规划和洪灾风险管理的司法管辖区提供了可复制的框架。