156 agencies are easy ways to ensure both interests are served. Roadside Infrastructure Design for Driverless Vehicles Driverless vehicles likely will soon be a common fea- ture of urban transport (Compostella et al. 2020). Driverless vehicles are highly connected and require active sensory and communication inputs to move safely (Ha et al. 2020). Near-ground urban vegetation that improves and cools urban environments but grows into driverless vehicle communication lines of sight has the potential to conflict with transport com- munication and risk safety; thus, near-ground urban vegetation will likely need to be more actively, coop- eratively, and effectively managed in the future. Roadside designs will likely change to accommodate driverless deliveries and human transport (Freemark et al. 2019), but it is not known at this time whether tree cover will be reduced overall with these design changes (Chapin et al. 2017). This scenario anticipates driverless network devel- opment along the current trajectory at the time of this writing. As of this writing, few firm physical urban plans have been published to guide design develop- ment, so it is difficult to anticipate specific opportuni- ties for design intervention or improvement for this scenario, although general statements can be made. Maintaining connectivity with vehicles and networks will be an important factor in driverless transport (Association of Metropolitan Planning Organizations 2019). Vegetation attenuating or disrupting connected vehicle communication will be viewed unfavorably, but urban greenery will be necessary for city design for human comfort, including ameliorating future urban heat, so connected networks and vegetation must coexist (Rouse et al. 2018). Today, it is difficult to develop an autonomous transport vegetation management system using exist- ing hardware and software technology, which does not exist at a scale that can perform the needed tasks. In the future, both aerial and near-ground fixed and mobile spectral sensors will be utilized to assess veg- etation health, because near-ground vegetation may be obscured by trees, infrastructure, and buildings, thus near-ground sensors likely will be a necessary com- ponent for monitoring and management. There likely will be a future need for a different sensor array design than what exists today for RS ©2022 International Society of Arboriculture Staley: Modern Urban Forestry for Modern Cities data collection. To better view near-ground urban vegetation, instead of the current method of always looking down from above through layers of vegetation and urban obstructions, Sideways-Aimed Spectral Sensors (SASS) for driverless transport networks will be mounted on both fixed infrastructure and selected vehicles. SASS devices will produce output in a ver- tical orientation rather than the typical horizontal image. Software analytics must be developed to ana- lyze vertical imagery at scale—and at extremely high resolution—as data will be imaged at a distance of a few meters from target vegetation instead of hun- dreds or thousands of meters above the surface. Com- bined with data from standard LiDAR and visual sensors for driverless vehicle guidance, SASS will result in a rich, high-resolution, information-dense environment near ground level that very accurately assesses urban vegetation near transport corridors. Collecting additional data from security sensors, stra- tegically mounted environmental monitoring sensors, and handheld devices will result in a potentially immense amount of information. Lastly, querying, analyzing, and managing vast amounts of varied types of data collected in near- ground environments will necessitate specially con- figured, secure, and purpose-built ML algorithms. These ML algorithms will sift through terabytes of data to analyze, monitor, and notify of any changes in near-ground plant environments. These changes will not only include plant growth, but sudden changes in plant orientation such as damage occurring from events such as accidents, vandalism, and weather events that can risk safety or signal disruption. Other Brief Scenarios for Applied Technology Development Other likely future scenarios for urban forests using the CACI model described above include: projects requiring the creation of very high-resolution 3D spectral and visual modeling of historic trees for pres- ervation or construction mitigation; calculating annual changes in biomass across scales for measurement of carbon sequestration by using LiDAR payloads mounted on an RPA or an autonomous delivery vehi- cle; performing construction site monitoring in real or near time for permit violations or vegetation stress by using a tasked RPA or data from a small satellite tasked nearby or during a defined period; collecting RS data on tree species, health, pest presence, and
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