Exploring flood mitigation governance by estimating first-floor elevation <i>via</i> deep learning and google street view in coastal Texas

Gao, Ge; Ye, Xinyue; Li, S. C.; Huang, Xiao; Ning, Huan; Retchless, David; Li, Zhenlong

doi:10.1177/23998083231175681

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…Multi-rotor drones and vehicle-based light detection and ranging (LiDAR) are especially scalable and adaptable to urban environments [24][25][26][27]. FFEs and other structural elevations accurate to traditional measures have been derived or imputed utilizing a variety of remote sensing and machine learning methodologies [28][29][30][31][32]. In this study, a comparative multinational case study analysis of residential communities is presented using a drone-based methodology to derive FFEs and assess parcel-level flood vulnerability.…”

Section: Introductionmentioning

confidence: 99%

Mapping the Flood Vulnerability of Residential Structures: Cases from The Netherlands, Puerto Rico, and the United States

Diaz,

Lee,

Kothuis

et al. 2024

Geosciences

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Floods are consistently ranked as the most financially devastating natural disasters worldwide. Recent flood events in the Netherlands, Caribbean, and US have drawn attention to flood risks resulting from pluvial and fluvial sources. Despite shared experiences with flooding, these regions employ distinct approaches and flood management strategies due to differences in governance and scale—offering a three-site case study comparison. A key, yet often lacking, factor for flood risk and damage assessments at the parcel level is building elevation compared to flood elevation. First-floor elevations (FFEs) are a critical element in the vulnerability of a building flooding. US-based flood insurance policies require FFEs; however, data availability limitations exist. Drone-based FFEs were measured in all locations to assess the flood vulnerabilities of structures. Flood vulnerability profiles revealed 64% of buildings were vulnerable to a form of inundation, with 40% belonging to “moderate” or “major” inundation, and inundation elevation means (IEMs) of −0.55 m, 0.19 m, and 0.71 m within the US, Netherlands, and Puerto Rico sites, respectively. Spatial statistics revealed FFEs were more responsible for flood vulnerabilities in the US site while topography was more responsible in the Netherlands and Puerto Rico sites. Additional findings in the Puerto Rico site reveal FFEs and next highest floor elevations (NHFEs) vulnerable to future sea level rise (SLR) flood elevations. The findings within the Netherlands provide support for developing novel multi-layered flood risk reduction strategies that include building elevation. We discuss future work recommendations and how the different sites could benefit significantly from strengthening FFE requirements.

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Section: Introductionmentioning

confidence: 99%

Mapping the Flood Vulnerability of Residential Structures: Cases from The Netherlands, Puerto Rico, and the United States

Diaz,

Lee,

Kothuis

et al. 2024

Geosciences

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Simulating urban energy use under climate change scenarios and retrofit plans in coastal Texas

Zhu,

Ye,

et al. 2024

Urban Info

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Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures.

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Detection of Pine Wilt Disease Using AAV Remote Sensing With an Improved YOLO Model

Wang,

Cai,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Exploring flood mitigation governance by estimating first-floor elevation via deep learning and google street view in coastal Texas

Cited by 7 publications

References 25 publications

Mapping the Flood Vulnerability of Residential Structures: Cases from The Netherlands, Puerto Rico, and the United States

Mapping the Flood Vulnerability of Residential Structures: Cases from The Netherlands, Puerto Rico, and the United States

Simulating urban energy use under climate change scenarios and retrofit plans in coastal Texas

Detection of Pine Wilt Disease Using AAV Remote Sensing With an Improved YOLO Model

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