The proper maintenance and efficient management of built infrastructure networks are critical for the wellbeing and economic development of society. In the past, the combination of remote sensing and advanced computing has been investigated for the analysis and modeling of buildings and facilities. However, similar research efforts in support of infrastructure networks are scarce. This article focuses on the automated classification of powerline infrastructure components and assessment of vegetation pruning efforts with morphology reasoning and geometric inference. The method includes the classification and subtraction of ground points, and the extraction and classification of above-the-ground objects belonging to power distribution and transmission infrastructure. Algorithmic reasoning based on the catenary curve function enabled the inference of occluded (e.g., by vegetation) sections of electrical conductors. Parameters of the catenary curve were investigated so that the curve fits the actual sag of the conductor. In addition, a curved cylindrical buffer along the electrical conductor was mathematically modeled to automatically and unequivocally determine clearance volumes of nearby vegetation or trees, e.g., scope of pruning work. Preliminary results of this study are being leveraged in support of the operation and maintenance of power infrastructure.