kd-SNN: A Metric Data Structure Seconding the Clustering of Spatial Data

Faustino, Bruno; Pires, João Moura; Santos, Maribel Yasmina; Moreira, Guilherme

doi:10.1007/978-3-319-09144-0_22

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Polygon is one of the most basic geometrical shapes, and therefore polygons are used in many aspects of spatial query processing [11,12]. There many kinds of polygons.…”

Section: Creating Polygonsmentioning

confidence: 99%

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A taxonomy for region queries in spatial databases

Taniar

Rahayu

2015

Journal of Computer and System Sciences

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In spatial databases, there are two basic types of queries, namely nearest neighbour queries (kNN) and range queries. Spatial range queries are not only finding objects of interest within a certain range or radius, but feature a wide spectrum: from finding objects of interest to forming the range (or region). Therefore, in this paper, we coin a term "Region Queries" to indicate a broad category of spatial range queries. It is imperative to understand the full capabilities of region queries, before starting to work on processing and optimising such queries. The aim of this paper is to show a complete picture of region queries. In this study, we present taxonomy of region queries, comprising of three categories: (i) finding objects of interest, (ii) forming regions, and (iii) determining centroids. These three query types form a comprehensive view of what region queries are about.

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“…Polygon is one of the most basic geometrical shapes, and therefore polygons are used in many aspects of spatial query processing [11,12]. There many kinds of polygons.…”

Section: Creating Polygonsmentioning

confidence: 99%

“…15. There are known algorithms to create a concave (or non-convex) polygon, such as using shared nearest neighbour (SNN) clustering algorithm [12].…”

Section: Creating Polygonsmentioning

confidence: 99%

A taxonomy for region queries in spatial databases

Taniar

Rahayu

2015

Journal of Computer and System Sciences

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Scalable Parallel Algorithms for Shared Nearest Neighbor Clustering

Kumari

Saurabh²,

Goyal

et al. 2016

2016 IEEE 23rd International Conference on High Performance Computing (HiPC)

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Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis

et al. 2021

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Efficient building instance segmentation is necessary for many applications such as parallel reconstruction, management and analysis. However, most of the existing instance segmentation methods still suffer from low completeness, low correctness and low quality for building instance segmentation, which are especially obvious for complex building scenes. This paper proposes a novel unsupervised building instance segmentation (UBIS) method of airborne Light Detection and Ranging (LiDAR) point clouds for parallel reconstruction analysis, which combines a clustering algorithm and a novel model consistency evaluation method. The proposed method first divides building point clouds into building instances by the improved kd tree 2D shared nearest neighbor clustering algorithm (Ikd-2DSNN). Then, the geometric feature of the building instance is obtained using the model consistency evaluation method, which is used to determine whether the building instance is a single building instance or a multi-building instance. Finally, for multiple building instances, the improved kd tree 3D shared nearest neighbor clustering algorithm (Ikd-3DSNN) is used to divide multi-building instances again to improve the accuracy of building instance segmentation. Our experimental results demonstrate that the proposed UBIS method obtained good performances for various buildings in different scenes such as high-rise building, podium buildings and a residential area with detached houses. A comparative analysis confirms that the proposed UBIS method performed better than state-of-the-art methods.

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kd-SNN: A Metric Data Structure Seconding the Clustering of Spatial Data

Cited by 3 publications

References 12 publications

A taxonomy for region queries in spatial databases

A taxonomy for region queries in spatial databases

Scalable Parallel Algorithms for Shared Nearest Neighbor Clustering

Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis

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