scholarly journals Spatial data integration for classification of 3D point clouds from digital photogrammetry

Applied GIS ◽  
2005 ◽  
Vol 1 (3) ◽  
pp. 26.1-26.15 ◽  
Author(s):  
Joshphar Kunapo
Keyword(s):  
Data Integration ◽  
Spatial Data ◽  
Point Clouds ◽  
Digital Photogrammetry ◽  
3D Point Clouds ◽  
Spatial Data Integration

scholarly journals An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

2021 ◽  
Vol 13 (15) ◽  
pp. 3021
Author(s):  
Bufan Zhao ◽  
Xianghong Hua ◽  
Kegen Yu ◽  
Xiaoxing He ◽  
Weixing Xue ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Point Clouds ◽  
Intelligent Vehicles ◽  
Critical Data ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Cluster Optimization ◽  
Urban Scene ◽  
Processing Steps

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

scholarly journals GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 541-548 ◽  
Author(s):  
E. Grilli ◽  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Heritage ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Clouds ◽  
3D Data ◽  
3D Documentation ◽  
The Impact

<p><strong>Abstract.</strong> In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.</p>

Spatial Data Integration

2019 ◽  
pp. 1539-1546 ◽  
Author(s):  
Booma Sowkarthiga Balasubramani ◽  
Isabel F. Cruz
Keyword(s):  
Data Integration ◽  
Spatial Data ◽  
Spatial Data Integration

Optimized Spatial Query for Data Integration Based on Semantics

2012 ◽  
Vol 229-231 ◽  
pp. 1895-1899
Author(s):  
Shen Yi Qian ◽  
Hao Dong Zhu
Keyword(s):  
Data Integration ◽  
Query Optimization ◽  
Spatial Data ◽  
Hybrid Approach ◽  
Detailed Examination ◽  
Spatial Knowledge ◽  
Spatial Query ◽  
Spatial Queries ◽  
Spatial Data Integration ◽  
Spatial Semantics

Data integration of geospatial data in distributed, heterogeneous environment involves the use of semantic ontologies. In this kind of integration system, semantic technologies play an important role in improving performance and effectiveness of spatial queries. This paper focuses on methods of query optimization based on spatial semantics at the top level of semantic layer in central data integration systems. After analyzing the hybrid approach for spatial data integration, two categories of query optimization strategies are proposed based on detailed examination of special characteristics of spatial data. With spatial knowledge explicitly specified in ontologies and associated rules, spatial queries can be optimized intelligently.

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