ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W1, 119-127, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
05 Oct 2016
F. Poux, P. Hallot, R. Neuville, and R. Billen ULG, Department of Geography, Geomatics Unit, University of Liège, 4000 Liège, Belgium
Keywords: Point cloud data structure, classification, feature extraction, segmentation, data mining, machine learning, multi-dimensional indexing, point cloud database 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.
Conference paper (PDF, 1062 KB)

Citation: Poux, F., Hallot, P., Neuville, R., and Billen, R.: SMART POINT CLOUD: DEFINITION AND REMAINING CHALLENGES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W1, 119-127,, 2016.

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