scholarly journals Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3926 ◽  
Author(s):  
Jongwon Kim ◽  
Jeongho Cho
Keyword(s):  
Delaunay Triangulation ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Synthetic Data ◽  
Point Clouds ◽  
Spatial Data Mining ◽  
New Approach ◽  
Clustering Technique ◽  
3D Point Clouds ◽  
Multiple Clusters

In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining.

On the MDBSCAN Algorithm in a Spatial Data Mining Context

2013 ◽  
pp. 263-273
Author(s):  
Gabriella Schoier
Keyword(s):  
Spatial Data ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Synthetic Data ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Data Set ◽  
Spatial Variables ◽  
Spatial Objects ◽  
Spatial Data Sets

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular, spatial clustering algorithms, which group similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this chapter is to present a density based algorithm for the discovery of clusters of units in large spatial data sets (MDBSCAN). This algorithm is a modification of the DBSCAN algorithm (see Ester (1996)). The modifications regard the consideration of spatial and non spatial variables and the use of a Lagrange-Chebychev metrics instead of the usual Euclidean one. The applications concern a synthetic data set and a data set of satellite images

On the MDBSCAN Algorithm in a Spatial Data Mining Context

Data Mining ◽  
2013 ◽  
pp. 435-444
Author(s):  
Gabriella Schoier
Keyword(s):  
Spatial Data ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Synthetic Data ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Data Set ◽  
Spatial Variables ◽  
Spatial Objects ◽  
Spatial Data Sets

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular, spatial clustering algorithms, which group similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this chapter is to present a density based algorithm for the discovery of clusters of units in large spatial data sets (MDBSCAN). This algorithm is a modification of the DBSCAN algorithm (see Ester (1996)). The modifications regard the consideration of spatial and non spatial variables and the use of a Lagrange-Chebychev metrics instead of the usual Euclidean one. The applications concern a synthetic data set and a data set of satellite images

scholarly journals The Spatial Patterns of Service Facilities Based on Internet Big Data: A Case Study on Chengdu

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hao Li ◽  
Jianshu Duan ◽  
Yidan Wu ◽  
Sizhuo Gao ◽  
Ting Li
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Sustainable Urban Development ◽  
Practical Significance ◽  
Ring Road ◽  
Multiple Clusters

In the context of the mid-late development of China’s urbanization, promoting sustainable urban development and giving full play to urban potential have become a social focus, which is of enormous practical significance for the study of urban spatial pattern. Based on such Internet data as a map’s Point of Interest (POI), this paper studies the spatial distribution pattern and clustering characteristics of POIs of four categories of service facilities in Chengdu of Sichuan Province, including catering, shopping, transportation, scientific, educational, and cultural services, by means of spatial data mining technologies such as dimensional autocorrelation analysis and DBSCAN clustering. Global spatial autocorrelation is used to study the correlation between an index of a certain element and itself (univariate) or another index of an adjacent element (bivariate); partial spatial autocorrelation is used to identify characteristics of spatial clustering or spatial anomaly distribution of geographical elements. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is able to detect clusters of any shape without prior knowledge. The final step is to carry out quantitative analysis and reveal the distribution characteristics and coupling effects of spatial patterns. According to the results, (1) the spatial distribution of POIs of all service facilities is significantly polarized, as they are concentrated in the old city, and the trend of suburbanization is indistinctive, showing three characteristics, namely, central driving, traffic accessibility, and dependence on population activity; (2) the spatial distribution of POIs of the four categories of service facilities is featured by the pattern of “one center, multiple clusters,” where “one center” mainly covers the area within the first ring road and partial region between the first ring road and the third ring road, while “multiple clusters” are mainly distributed in the well-developed areas in the second circle of Chengdu, such as Wenjiang District and Shuangliu District; and (3) there is a significant correlation between any two categories of POIs. Highly mixed multifunctional areas are mainly distributed in the urban center, while service industry is less aggregated in urban fringe areas, and most of them are single-functional or dual-functional regions.

scholarly journals A new approach for semi-automatic rock mass joints recognition from 3D point clouds

2014 ◽  
Vol 68 ◽  
pp. 38-52 ◽  
Author(s):  
Adrián J. Riquelme ◽  
A. Abellán ◽  
R. Tomás ◽  
M. Jaboyedoff
Keyword(s):  
Rock Mass ◽  
Point Clouds ◽  
New Approach ◽  
3D Point Clouds

Study on Fuzzy Clustering Algorithm of Spatial Data Mining

2013 ◽  
Vol 416-417 ◽  
pp. 1244-1250
Author(s):  
Ting Ting Zhao
Keyword(s):  
Data Mining ◽  
Fuzzy Clustering ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Rapid Development ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Data Set ◽  
Fuzzy Similarity

With rapid development of space information crawl technology, different types of spatial database and data size of spatial database increases continuously. How to extract valuable information from complicated spatial data has become an urgent issue. Spatial data mining provides a new thought for solving the problem. The paper introduces fuzzy clustering into spatial data clustering field, studies the method that fuzzy set theory is applied to spatial data mining, proposes spatial clustering algorithm based on fuzzy similar matrix, fuzzy similarity clustering algorithm. The algorithm not only can solve the disadvantage that fuzzy clustering cant process large data set, but also can give similarity measurement between objects.

scholarly journals Knowledge Discovery Web Service for Spatial Data Infrastructures

2020 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Morteza Omidipoor ◽  
Ara Toomanian ◽  
Najmeh Neysani Samany ◽  
Ali Mansourian
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Web Service ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Data Mining Techniques ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
High Level

The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge Discovery Web Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.

scholarly journals LiDAR-based Simultaneous Localization and Mapping in an underground mine in Złoty Stok, Poland

2021 ◽  
Vol 942 (1) ◽  
pp. 012035
Author(s):  
P Trybała
Keyword(s):  
Mobile Robot ◽  
Spatial Data ◽  
Laser Scanner ◽  
Simultaneous Localization And Mapping ◽  
Point Clouds ◽  
Working Environment ◽  
Accuracy Evaluation ◽  
Process Data ◽  
3D Point Clouds ◽  
Localization And Mapping

Abstract The mining sector is one of the most promising areas for implementing advanced autonomous robots. The benefits of increased safety, robot actions’ repeatability, and reducing human presence in hazardous locations are especially important in underground mines. One of the core functionalities of such a device is the robot’s ability to localize and navigate itself in the working environment. To achieve this, simultaneous localization and mapping (SLAM) techniques are used. In selected cases, they also allow the acquisition of dense spatial data in the form of 3D point clouds, which can be utilized for various 3D modeling and spatial analysis purposes. In this work, a mobile robot, equipped only with a compact laser scanner, is used to acquire spatial data in the adit of a closed mine in Zloty Stok, Poland. This data is further processed with selected SLAM algorithms to create a homogeneous 3D point cloud. Results are visualized and compared to a model obtained with a survey-grade laser scanner. Accuracy evaluation shows that employing SLAM algorithms to process data collected by a mobile robot can produce a reasonably accurate 3D geometrical model of an underground tunnel, even without incorporating any additional sensors.

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