Abstract
Abstract. Dealing with coloured point cloud acquired from terrestrial laser scanner, this paper identifies remaining challenges for a new data structure: the smart point cloud. This concept arises with the statement that massive and discretized spatial information from active remote sensing technology is often underused due to data mining limitations. The generalisation of point cloud data associated with the heterogeneity and temporality of such datasets is the main issue regarding structure, segmentation, classification, and interaction for an immediate understanding. We propose to use both point cloud properties and human knowledge through machine learning to rapidly extract pertinent information, using user-centered information (smart data) rather than raw data. A review of feature detection, machine learning frameworks and database systems indexed both for mining queries and data visualisation is studied. Based on existing approaches, we propose a new 3-block flexible framework around device expertise, analytic expertise and domain base reflexion. This contribution serves as the first step for the realisation of a comprehensive smart point cloud data structure.
Highlights
The democratization of capturing devices across a growing number of industries has made point clouds a mainstream spatial sensor’s output data
We propose a framework for the development of a new structure: the smart point cloud (SPC)
Multiple classifiers or ensemble learners outperform single classifiers in term of accuracy making them a great tool for multisensory data. (Koppula et al, 2011) propose a 3D point cloud labelling method based on support vector machines (SVM) empirically validated over an indoor scene captured by a RGB-D sensor (Kinect)
Summary
The democratization of capturing devices across a growing number of industries has made point clouds a mainstream spatial sensor’s output data. We need to retain only relevant observations and avoid data saturation This implies to identify internal and external influential sources over sensed data, to classify and structure point clouds through indexing scheme and database management systems (Cura et al, 2015). Segmentation relies on data abstraction into consistent indicators and feature descriptors which can describe the essential information both spatial and semantic This challenge remains highly contextual as to detect relevant objects given a specific context, one must understand which descriptors he should use to recognize an object composed of several points within a scene. We propose a framework for the development of a new structure: the smart point cloud (SPC) This is the first contribution of a PhD research combining laserscanning and big data management. We will discuss requirements for a contextually dependent SPC structure built on a topological level of detail (LoD) retaining both spatial and semantic relationship for intelligent data mining, followed by our future work in the area
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