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First Floor Elevation (FFE) is a crucial indicator used in the United States for assessing the vulnerability of buildings to flood events. However, most buildings within floodplains lack accurate FFE data. On-site FFE data collection could be costly and time-consuming. To facilitate efficient FFE data collection, this paper explores the extent to which FFEs for an entire community’s building stock can be estimated using limited FFE data records. More specifically, this study leverages geostatistical imputation techniques to fill in missing FFE data. The proposed approach first stratifies buildings based on attributes such as foundation type and then applies Kriging to estimate missing FFEs by leveraging spatial relationships and distances between known data points. To demonstrate the applicability and validity, the approach is tested in three New Jersey townships: the inland township Manville and the two coastal townships Longport and Ventnor City. By transforming data into First Floor Height and stratifying by building type, the methodology achieved a Root Mean Square Error (RMSE) as low as 0.9893 ft in Manville, with variations in coastal towns due to unique structural and geographic factors. The findings highlight the potential of combining geostatistical modeling with building-specific attributes to enhance flood vulnerability assessments. This approach not only addresses critical data deficiencies but also supports informed decision-making in resilience planning and natural hazard mitigation.
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Exploring flood mitigation governance by estimating first-floor elevation via deep learning and google street view in coastal Texas
Flood mitigation governance is critical for coastal regions where flooding has caused considerable damage. Raising the First-Floor Elevation (FFE) above the base flood elevation (BFE) is an effective mitigation measure for buildings with a high risk of flooding. In the U.S., measuring FFE is necessary to obtain an Elevation Certificate (E.C.) for the National Flood Insurance Program (NFIP) and has traditionally required labor-consuming field surveys. However, the advances in computer vision technology have facilitated the handling of large image datasets, leading to new FFE measurement approaches. Taking Galveston Island (including the cities of Galveston and Jamaica Beach) in Coastal Texas as a case study, we explore how these new approaches may inform flood risk management and governance, including how FFE estimates may be combined with BFE estimates from flood inundation probability mapping to model the predicted cost of raising buildings’ FFE above their BFE. After establishing the FFE model’s accuracy by comparing its results with previously validated FFE estimates in three districts of Galveston, we generalize the workflow to building footprints across Galveston Island. By combining the FFE data derived from our workflow with multidimensional building information, we further analyze the future flood control and post-disaster maintenance strategies. Our findings present valuable data collection paradigms and methodological concepts that inform flood governance for Galveston Island. The proposed workflow can be extended to flood management and research for other vulnerable coastal communities.
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- Award ID(s):
- 2122054
- PAR ID:
- 10448737
- Date Published:
- Journal Name:
- Environment and Planning B: Urban Analytics and City Science
- ISSN:
- 2399-8083
- Page Range / eLocation ID:
- 239980832311756
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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First Floor Elevation (FFE) of a house is crucial information for flood management and for accurately assessing the flood exposure risk of a property. However, the lack of reliable FFE data on a large geographic scale significantly limits efforts to mitigate flood risk, such as decision on elevating a property. The traditional method of collecting elevation data of a house relies on time-consuming and labor-intensive on-site inspections conducted by licensed surveyors or engineers. In this paper, we propose an automated and scalable method for extracting FFE from mobile LiDAR point cloud data. The fine-tuned yolov5 model is employed to detect doors, windows, and garage doors on the intensity-based projection of the point cloud, achieving an mAP@0.5:0.95 of 0.689. Subsequently, FFE is estimated using detected objects. We evaluated the Median Absolute Error (MAE) metric for the estimated FFE in Manville, Ventnor, and Longport, which resulted in values of 0.2 ft, 0.27 ft, and 0.24 ft, respectively. The availability of FFE data has the potential to provide valuable guidance for setting flood insurance premiums and facilitating benefit-cost analyses of buyout programs targeting residential buildings with a high flood risk.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1177/23998083231175681
