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Abstract The quantification of urban impervious area has important implications for the design and management of urban water and environmental infrastructure systems. This study proposes a deep learning model to classify 15‐cm aerial imagery of urban landscapes, coupled with a vector‐oriented post‐classification processing algorithm for automatically retrieving canopy‐covered impervious surfaces. In a case study in Corpus Christi, TX, deep learning classification covered an area of approximately 312 km²(or 14.86 billion 0.15‐m pixels), and the post‐classification effort led to the retrieval of over 4 km²(or 0.18 billion pixels) of additional impervious area. The results also suggest the underestimation of urban impervious area by existing methods that cannot consider the canopy‐covered impervious surfaces. By improving the identification and quantification of various impervious surfaces at the city scale, this study could directly benefit a variety of environmental and infrastructure management practices and enhance the reliability and accuracy of processed‐based models for urban hydrology and water infrastructure. 

Abstract Background and AimsA comprehensive standardized evaluation tool was needed to assess community awareness and preparedness when the pandemic hit the United States. This study aimed to develop and validate a new Coronavirus Awareness and Preparedness Scale (CAPS) through psychometric testing. MethodsThis study unfolded in two phases. Phase 1 (conducted in March and April 2020) focused on the development of the scale. Phase 2 (conducted in June and July 2020) measured the reliability and validity of the scale. Psychometric testing, including exploratory factor analysis and reliability testing, was performed with a convenience sample of 1237 faculty, staff, and students at a southern university in the United States. ResultsThe final CAPS model consists of four factors with 26 items: threat (seven items), confidence (11 items), individual precautions (three items), and public precautions (five items). The scale demonstrated satisfactory internal consistency (Cronbach'sα = 0.75). Strong and statistically significant item correlations were observed within the subscales through item analysis. ConclusionThe CAPS is a reliable and valid comprehensive evaluation instrument designed to gauge community awareness and preparedness during the early stages of the COVID‐19 pandemic. Its adaptability makes it suitable for measuring readiness and preparedness concerning any novel airborne disease or future airborne pandemic within a community.