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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]

Satellite data from the Advanced Very High Resolution Radiometer (AVHRR) have been widely employed for fire monitoring around the world by virtue of the thermal emission in the middle-infrared (mid-IR) channel at 3.7 μm. This channel, however, receives both thermal emission and solar reflection. As far as fire detection is concerned, the solar reflection contaminates the fire emission signal, which can cause significant errors, especially over non-forest biomes. This study presents a method to detect and eliminate the significant contribution of solar reflection to the AVHRR mid-IR band so that the fire detection accuracy is improved. AVHRR data from April to November 2004 were analysed. Twenty-seven percent of commission errors, mainly located in the southwestern part of North America, were found to be caused by the strong solar reflection from the surface. We also found that the calculated solar reflection itself is an effective indicator of false detections for the AVHRR. Introducing a new test to take into account this effect leads to a considerable reduction in commission errors. The new filter can eliminate most commission errors at the expense of minor increases in omission errors. The total number of true fires is missed by 0.3%, and the total number of false fire detections is reduced by 27.1%. [ABSTRACT FROM AUTHOR]