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Maintaining an up-to-date repository of horizontal curve attributes is extremely important due to the role curves play in the safe operation of highways. Such attributes are typically collected using traditional surveying techniques which have been shown to be time-consuming, traffic disruptive, and potentially unsafe methods. This burden is further aggravated when the data collection is required on a large highway network across North America. To overcome this burden, this paper proposes a framework for network-level detection and extraction of horizontal curve elements from Light Detection and Ranging (LiDAR) data in a fully automated manner. The proposed technique was validated and then tested on LiDAR data collected on 242 km of highways in Alberta, Canada. The algorithm was successful in detecting all curves on the test highways and estimated their attributes with accuracies ranging from 96% to 100% demonstrating the robustness of the extraction method and the feasibility of performing the extraction on such a large scale. The proposed method is an alternative approach that could help transportation agencies maintain an updated inventory of horizontal alignment information on a large-scale. [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]