Robust and fast reconstruction of complex roofs with active sampling from 3D point clouds

This article proposes a novel method for the 3D reconstruction of LoD2 buildings from LiDAR data. We propose an active sampling strategy which applies a cascade of filters focusing on promising samples at an early stage, thus avoiding the pitfalls of RANSAC‐based approaches. Filters are based on prior knowledge represented by (nonparametric) density distributions. In our approach samples are pairs of surflets—3D points together with normal vectors derived from a plane approximation of their neighborhood. Surflet pairs provide parameters for model candidates such as azimuth, inclination and ridge height, as well as parameters estimating internal precision and consistency. This provides a ranking of roof model candidates and leads to a small number of promising hypotheses. Building footprints are derived in a preprocessing step using machine learning methods, in particular support vector machines.     

俯冲板片中微量元素的活动性:西天山榴辉岩相运输脉的制约

西天山的基性高压变质岩显示互相连接的榴辉岩相脉体网络来源于蓝片岩的进变质脱水作用,通过这些网脉可以洞察在俯冲带高压条件下远程流体流动中的流体-岩石相互作用和元素负载。岩相学证据表明外来流体的渗透作用产生运输脉,而流体持续的沟道式流动导致主岩蓝片岩的淋滤。脉和蓝片岩蚀变带中锂(Li)的含量几乎是蓝片岩主岩中锂的两倍,它支持流体是外部来源的设想。这些流体触发了蓝片岩主岩的榴辉岩化作用而形成蓝片岩蚀变带。由于成脉流体中的微量元素含量低,所以导致与流体发生化学反应的主岩中所有微量元素的强烈淋滤。在此过程中有53%~81%的微量元素被活化,与大离子亲石元素和轻稀土元素的损失相符合,它们的损失量几乎是重稀土元素和高场强元素损失量的两倍。