Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy.

An online shopping intermediary is an Internet platform on which consumers and third-party sellers transact. Shopping intermediaries provide a search environment (e.g., search aids) to lower the search costs incurred by consumers when finding and evaluating sellers’ products. We study strategic incentives of an intermediary in the design of its search environment as a means to ease search costs. An important aspect of our analysis is that consumers optimally decide how many sellers to evaluate and how deeply (e.g., number of attributes) to evaluate each of them. We find that the equilibrium search environment embeds sufficiently high search costs to prevent consumers from evaluating too many sellers, but not too high to cause them to evaluate sellers’ products at partial depth. This paper was accepted by J. Miguel Villas-Boas, marketing.

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Consumer citizenship behavior is widely considered to be vital to business success. However, the role of resource uniqueness and service quality in encouraging citizenship behavior in tourism settings has not been well understood. Grounded on a framework integrating the Stimulus-Organism-Response Model and Social Exchange Theory, this study examines whether tourism resource uniqueness and service quality affect tourists’ citizenship behaviors (i.e., word-of-mouth recommendations and providing feedback) through the mediating effect of tourist emotion (i.e., positive and negative emotions). A total of 321 samples collected from three types of scenic spots in China were analyzed using structural equation modeling and Bootstrapping procedures. Results suggest that both tourism resource uniqueness and service quality positively predict positive emotion and negatively influence negative emotion, which is further positively and negatively associated with word-of-mouth recommendation and providing feedback, respectively. Moreover, both positive emotion and negative emotion mediate the effects of tourism resource uniqueness and service quality on tourists’ citizenship behaviors. Findings provide evidence that both resource uniqueness and service quality are critical to understand tourists’ citizenship behavior, and offer important marketing implications for destinations to manage tourist emotional experiences.