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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.

 

Abstract The impact of climate extremes upon human settlements is expected to accelerate. There are distinct global trends for a continued rise in urban dwellers and associated infrastructure. This growth is occurring amidst the increasing risk of extreme heat, rainfall, and flooding. Therefore, it is critical that the urban development and architectural communities recognize climate impacts are expected to be experienced globally, but the cities and urban regions they help create are far more vulnerable to these extremes than nonurban regions. Designing resilient human settlements responding to climate change needs an integrated framework. The critical elements at play are climate extremes, economic growth, human mobility, and livability. Heightened public awareness of extreme weather crises and demands for a more moral climate landscape has promoted the discussion of urban climate change ethics. With the growing urgency for considering environmental justice, we need to consider a transparent, data-driven geospatial design approach that strives to balance environmental justice, climate, and economic development needs. Communities can greatly manage their vulnerabilities under climate extremes and enhance their resilience through appropriate design and planning towards long-term stability. A holistic picture of urban climate science is thus needed to be adopted by urban designers and planners as a principle to guide urban development strategy and environmental regulation in the context of a growingly interdependent world. 

Urban digital twins (UDTs) have been identified as a potential technology to achieve digital transformative positive urban change through landscape architecture and urban planning. However, how this new technology will influence community resilience and adaptation planning is currently unclear. This article: (1) offers a scoping review of existing studies constructing UDTs, (2) identifies challenges and opportunities of UDT technologies for community adaptation planning, and (3) develops a conceptual framework of UDTs for community infrastructure resilience. This article highlights the need for integrating multi-agent interactions, artificial intelligence, and coupled natural–physical–social systems into a human-centered UDTs framework to improve community infrastructure resilience.

 

Urban greenway is an emerging form of urban landscape offering multifaceted benefits to public health, economy, and ecology. However, the usage and user experiences of greenways are often challenging to measure because it is costly to survey such large areas. Based on the online postings from Instagram in 2017, this paper used Computer Vision (CV) technology to analyze and compare how the general public uses two typical greenway parks, The High Line in New York City and the Atlanta Beltline in Atlanta. Face and object detection analysis were conducted to infer user composition, activities, and key experiences. We presented the temporal patterns of Instagram postings as well as the group gatherings, smiling, and representative objects detected from photos. Our results have shown high user engagement levels for both parks while teens are significantly underrepresented. The High Line had more group activities and was more active during weekdays than the Atlanta Beltline. Stronger sense of escape and physical activities can be found in Atlanta Beltline. In summary, social media images like Instagram can provide strong empirical evidence for urban greenway usage when combined with artificial intelligence technologies, which can support the future practice of landscape architecture and urban design.