用街景图和机器学习，精准估算德州每栋房的洪水内涝深度和损失风险。
Property-Level Flood Risk Assessment Using AI-Enabled Street-View Lowest Floor Elevation Extraction and ML Imputation Across Texas
- 通过街景图像提取房屋最低点高程与路面高差，结合多模型智能补全缺失数据。
- 覆盖1.2万栋住宅，49%可直接提取高程，补全后模型精度最高达R²=0.974。
- 适合缺高程证书但需精细化防洪管理的地区，可复用于其他区域。
本文提出一种基于人工智能的街景图像分析方法，结合性能筛选的机器学习插补技术，实现大范围区域建筑级高程数据生成，用于洪水风险评估。研究在德克萨斯州18个重点区域构建三阶段流程：（1）利用Elev-Vision框架从谷歌街景图中提取最低楼面高程（LFE）及路面与最低楼层高差（HDSL）；（2）采用随机森林与梯度提升模型，基于16类地形、水文、地理与洪水暴露特征对缺失的HDSL值进行插补；（3）将生成的高程数据与Fathom 1-in-100年淹没面及美国陆军工程兵团深度-损毁函数融合，估算各房产内部洪水深度与预期损失。在12,241栋住宅中，街景可用率为73.4%，成功提取LFE/HDSL的为49.0%（5,992栋）。13个区域保留插补结果，交叉验证显示模型R²达0.159至0.974；5个区域因性能不足被排除。结果表明，尽管街景法非全覆盖，但其可规模化应用，显著提升区域洪水风险刻画能力，从暴露范围推进至结构级内涝与损毁估计。本研究将低层高程估计从试点验证推向区域化端到端流程，兼具科学意义与实践价值，为缺乏完整高程证书的地区提供可复用的规划与减灾决策工具。
原文摘要 · Abstract (English)
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.
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