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Abstract. We propose a new multi-way spatial join algorithm called M-way R-tree join which synchronously traverses M R-trees. The Mway R-tree join can be considered as a generalization of the 2-way R-tree join. Although a generalization of the 2-way R-tree join has recently been studied, it did not properly take into account the optimization techniques of the original algorithm. Here, we extend these optimization techniques for M-way joins. Since the join ordering was considered to be important in the M-way join literature (e.g., relational join), we especially consider the ordering of the search space restriction and the plane sweep. Additionally, we introduce indirect predicates in the M-way join and propose a further optimization technique to improve the performance of the M-way R-tree join. Through experiments using real data, we show that our optimization techniques significantly improve the performance of the M-way spatial join.

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Terrestrial laser scanning (TLS) provides a rapid remote sensing technique to model 3D objects but can also be used to assess the surface condition of structures. In this study, an effective image processing technique is proposed for crack detection on images extracted from the octree structure of TLS data. To efficiently utilize TLS for the surface condition assessment of large structures, a process was constructed to compress the original scanned data based on the octree structure. The point cloud data obtained by TLS was converted into voxel data, and further converted into an octree data structure, which significantly reduced the data size but minimized the loss of resolution to detect cracks on the surface. The compressed data was then used to detect cracks on the surface using a combination of image processing algorithms. The crack detection procedure involved the following main steps: (1) classification of an image into three categories (i.e., background, structural joints and sediments, and surface) using K-means clustering according to color similarity, (2) deletion of non-crack parts on the surface using improved subtraction combined with median filtering and K-means clustering results, (3) detection of major crack objects on the surface based on Otsu’s binarization method, and (4) highlighting crack objects by morphological operations. The proposed technique was validated on a spillway wall of a concrete dam structure in South Korea. The scanned data was compressed up to 50% of the original scanned data, while showing good performance in detecting cracks with various shapes.

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