Climate vulnerability is higher in coastal regions. Communities can largely reduce their hazard vulnerabilities and increase their social resilience through design and planning, which could put cities on a trajectory for long-term stability. However, the silos within the design and planning communities and the gap between research and practice have made it difficult to achieve the goal for a flood resilient environment. Therefore, this paper suggests an AI (Artificial Intelligence)-driven platform to facilitate the flood resilience design and planning. This platform, with the active engagement of local residents, experts, policy makers, and practitioners, will break the aforementioned silos and close the knowledge gaps, which ultimately increases public awareness, improves collaboration effectiveness, and achieves the best design and planning outcomes. We suggest a holistic and integrated approach, bringing multiple disciplines (architectural design, landscape architecture, urban planning, geography, and computer science), and examining the pressing resilient issues at the macro, meso, and micro scales.

Urban public spaces are a key component to the well-being and prosperity of modern society. It has been increasingly important to improve the qualities and maximize the usages of urban public spaces. There is a lack of studies that investigate how people use and perceive urban parks using quantitative analysis of location-based social media reviews. This study tackles this gap by introducing a case study that uses social media reviews (Tripadivisor.com) to understand the perceived site quality and experiences of Bryant Park in New York City. A large dataset including 11,419 Tripadvisor reviews from 10,615 users was collected. LDA (Latent Dirichlet Allocation), a natural language processing and machine learning technique, was used to perform topic modeling analysis that could reveal hidden themes in large amounts of text. The results include five semantic topics and their associated topic terms. A comprehensive overview of the user experiences in Bryant Park were provided along with their weekly and monthly dynamics. The findings provide insights for future public space designers and managers by revealing how users describe the designs and operations of Bryant Park.

In modern cities, urban public spaces, such as parks, gardens, plazas, and streets, play a big role in people’s social activities, physical activities, mental health, and overall well-being. However, the traditional post-occupancy evaluation (POE) process for public spaces such as large urban parks is extremely difficult, especially for long-term user experiences through observations, surveys, and interviews. On the other hand, social media has emerged as a major media outlet recording millions of user experiences to the public, which provides opportunities to inform how public space is used and perceived by users. Furthermore, unlike previous research that primarily presented descriptive characters of park programs, our study employs a network model to elucidate the interactive relationships and intensities among reported park elements, human activities, and experiences. This approach enables us to track the sources within the space that impact people’s perceptions, such as weather conditions, food options, and notable landmarks. The utilization of this network model opens avenues for future research to comprehensively investigate the factors shaping people’s perceptions in public open spaces. This study uses Bryant Park as an example and presents a new analytical framework, POSE (post-occupancy social media evaluation), to support long-term POE studies for large public spaces. Methods such as data automation, descriptive statistics, and social network analysis were used. The identification and quantification of meaningful park activities, scenes, and sentiments as well as their relationships will help optimize the design and management of park programs.

Due to the recent excessive pursuit of rapid economic development in China, the cultural heritage resources have been gradually destroyed. This paper proposes cultural recovery and ecological remediation patterns, and adopts virtual reality (VR) technology to evaluate the visual aesthetic effect of the restored landscape. The results show that: (1) the average vegetation coverage increased, providing data support for remediation design evaluation; and (2) the fixation counts and average saccade counts of the subjects increased after the remediation design, indicating that the restored cultural landscape reduced visual fatigue and provided a better visual aesthetic experience. Furthermore, the comparative analysis of the quality of the water environment shows that the remediation design project improved the ecological environment quality of the relics area. The results of this study will contribute to rural revitalization in minority areas in southwest China.