Abstract
Abstract. Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor’s biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour’s class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.
Highlights
Point clouds constitute exhaustive datasets on which efficient data mining techniques can build themselves to extract essential information for cultural heritage applications
We tested the method on different samples from different zones of the mosaic to identify the influence of the segmentation and the classification in different scenarios
Our paper in the context of a complex archaeological mosaic emphasised the role of knowledge in registration, segmentation and classification of point clouds
Summary
Point clouds constitute exhaustive datasets on which efficient data mining techniques can build themselves to extract essential information for cultural heritage applications. Generated using an ever increasing variety of remote sensing platforms and sensors (Toth and Jóźków, 2015), their heterogeneity, complexity and massiveness rises This creates a need to move toward hybrid automatic processing and direct information extraction to avoid data saturation (Guest, 2006) and unlock efficient decision making processes close to real-time. While used since decades (Llinas and Hall, 1998), new advancements, methodologies and cost-effective solutions has stirred many industry gravitating around spatial data to develop new effective systems (Labayrade et al, 2005) These leverage the complementarity of signals and features by merging important contextual information sources following data fusion principles. We propose a knowledge-based approach to automatically pre-process, segment, classify and attach semantics onto a complex point cloud of a mosaic to enable archaeological information extraction (tesserae-wise). We present the results and discuss our future work in the area
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