Spatial perception is essential for understanding residents’ subjective experiences and well-being. However, effective methods for tracking changes in spatial perception over time and space remain limited. This study proposes a novel approach that leverages historical street view imagery to monitor the evolution of urban spatial perception. Using the central urban area of Shanghai as a case study, we applied machine learning techniques to analyze 67,252 street view images from 2013 and 2019, aiming to quantify the spatiotemporal dynamics of urban perception. The results reveal the following: temporally, the average perception scores in 2019 increased by 4.85% compared to 2013; spatially, for every 1.5 km increase in distance from the city center, perception scores increased by an average of 0.0241; among all sampling points, 65.79% experienced an increase in perception, while 34.21% showed a decrease; and in terms of visual elements, natural features such as trees, vegetation, and roads were positively correlated with perception scores, whereas artificial elements like buildings, the sky, sidewalks, walls, and fences were negatively correlated. The analytical framework developed in this study offers a scalable method for measuring and interpreting changes in urban perception and can be extended to other cities. The findings provide valuable time-sensitive insights for urban planners and policymakers, supporting the development of more livable, efficient, and equitable urban environments.

Urban disaster resilience has become central to risk governance as climate change, compound hazards, and interdependent urban systems intensify pressures on cities. Publication-based reviews describe visible research outputs but reveal less about how research priorities are formulated and funded. This study examines 26 National Natural Science Foundation of China projects initiated during 2014–2024 and retrieved using three deliberately focused keywords: “urban disaster,” “urban resilience,” and “disaster resilience.” Descriptive statistics, text-assisted coding, temporal analysis, knowledge mapping, and a paradigm matrix were applied across six dimensions: hazard types, urban systems, resilience capacities, research methods, spatial scales, and paradigm orientations. Within this selected corpus, funding activity became concentrated after 2019, while research attention broadened from disaster prevention, risk identification, and vulnerability assessment toward resilience assessment, adaptive governance, community capacity, infrastructure networks, and coupled urban systems. Nevertheless, indicator frameworks, spatial evaluation, and model-based measurement remained dominant. Recovery trajectories, compound hazard interactions, social equity, and cross-system governance received comparatively limited attention. These findings indicate an agenda-level reorientation within the selected NSFC-funded corpus, rather than a universal transformation of Chinese urban disaster research. A more mature resilience agenda requires stronger integration of compound risks, recovery processes, equity, and coupled system governance.