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Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.

Street view imagery databases such as Google Street View, Mapillary, and Karta View provide great spatial and temporal coverage for many cities globally. Those data, when coupled with appropriate computer vision algorithms, can provide an effective means to analyse aspects of the urban environment at scale. As an effort to enhance current practices in urban flood risk assessment, this project investigates a potential use of street view imagery data to identify building features that indicate buildings' vulnerability to flooding (e.g., basements and semi-basements). In particular, this paper discusses (1) building features indicating the presence of basement structures, (2) available imagery data sources capturing those features, and (3) computer vision algorithms capable of automatically detecting the features of interest. The paper also reviews existing methods for reconstructing geometry representations of the extracted features from images and potential approaches to account for data quality issues. Preliminary experiments were conducted, which confirmed the usability of the freely available Mapillary images for detecting basement railings as an example type of basement features, as well as geolocating the features., (© The Author(s) 2023.)

New York City (NYC) was deeply impacted by COVID-19 in spring 2020, with thousands of new cases daily. However, the pandemic's effects were not evenly distributed across the city, and the specific contributors have not yet been systematically considered. To help investigate that topic, this study analyzed the interaction of people with neighborhood businesses and other points of interest (POIs) in parts of three NYC neighborhoods in the spring of 2020 during the peak of the first COVID-19 wave through anonymized cellphone data and direct the observation of 1313 individuals leaving healthcare facilities. This study considered social vulnerability index (SVI) levels, population density, and POI visit behaviors from both cellphone data and firsthand observations of behavior around select NYC health facilities in different boroughs as various proxies. By considering equivalent businesses or groups of businesses by neighborhood, POI visits better aligned with COVID-19 infection levels than SVI. If tracking POI visit levels proves a reliable direct or relative proxy for disease transmission when checked against larger datasets, this method could be critical in both predictions of future outbreaks and the setting of customer density limits.

Background: Epidemiological studies have long been used for infection transmission prevention, but exact patterns of touch behaviours and transportation choices [contributors to community spread of coronavirus disease 2019 (COVID-19)] were previously unknown., Aim: To investigate individual risk behaviour levels with respect to local COVID-19 infection levels., Methods: A longitudinal field study recorded behaviours of individuals leaving medical facilities following the New York State's PAUSE order. A subset of those data was analysed herein (4793 records, 16 facilities, 23 rd March-17 th May 2020). Touched objects and transportation choices were compared over time using Chi-squared tests ( P <0.05 significance threshold)., Findings: In Week 1, 64.1% of subjects touched at least one environmental object [such as a building door handle (21.8%); traffic light, railing or parking meter (5.6%)]; shared object [such as a vehicle door handle (19.7%)]; personal object [such as a cell phone (4.2%)]; or themselves (0.4%). By Week 8, <35% of subjects touched at least one object, where the greatest reduction was in touching environmental objects. The frequency of touching increased slightly during the observation period for some personal objects such as cell phones. The use of public transportation remained steady (approximately 20%) throughout the study period; for-hire vehicle usage increased from 0% in Week 1 to 7% in Week 8, mirroring a 7% decrease in the use of personal vehicles (from 34% to 27%). Touching and transportation patterns varied significantly by facility., Conclusions: While this study observed a decline in touch patterns and use of shared modes of transportation, the persistence of many risk-related behaviours suggests that more effective public health policies, including cleaning regimens for public environmental objects and the removal or relocation of frequently touched objects, could help limit the spread of COVID-19., (© 2021 The Authors.)