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http://2019.itf-oecd.org/

UAS Roadmap

The aim of North Carolina Department of Transportation's (NCDOT’s) Wetland Predictive Modeling (WPM) tool is to help identify the location and extent of jurisdictional wetlands along potential road corridors in order to minimize field campaigns and mitigation efforts. To do so, WPM uses a variety of geospatial data including light detecting and ranging (LiDAR)-derived terrain and digital soil maps. These input data can be deficient in a variety of ways that reduce confidence in WPM output. For instance, WPM inputs can be out-of-date, lack the required spatial resolution, or simply be unavailable. Unmanned aerial systems (UAS) are emerging as a potential solution to these problems: they are relatively low-cost, can be operated “on-demand”, and offer a variety of data streams at unprecedented spatial resolutions. It is likely that UAS-based remote sensing can be integrated into WPM workflows in a way that improves model accuracy, thus reducing the cost and improving the outcome of road planning projects. Nevertheless, substantial challenges exist to implementing a UAS remote sensing program for WPM activities. First, there is a large and rapidly growing selection of aircraft, avionics and software packages, and sensors available on the market. Which combinations are most likely to be effective for WPM? Likewise, the science of turning raw sensor feeds from UAS (e.g. LiDAR point clouds or multispectral images) into usable data is emerging and there are no standardized methods. How best to turn UAS observations into WPM-relevant data streams? Finally, the legal/regulatory environment is moving nearly as quickly as the marketplace, and it is challenging to understand how to fly legally and safely. What are safe and effective flight operations for gathering WPM-relevant data?