A Distributed System for Optimal Scale Feature Extraction and Semantic Classification of Large-Scale Airborne LiDAR Point Clouds

2020 ◽  
pp. 280-288
Author(s):  
Satendra Singh ◽  
Jaya Sreevalsan-Nair
Keyword(s):  
Feature Extraction ◽  
Distributed System ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Semantic Classification ◽  
Scale Feature ◽  
Optimal Scale

scholarly journals WEAKLY SUPERVISED SEGMENTATION-AIDED CLASSIFICATION OF URBAN SCENES FROM 3D LIDAR POINT CLOUDS

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 151-157 ◽  
Author(s):  
S. Guinard ◽  
L. Landrieu
Keyword(s):  
Large Scale ◽  
Conditional Random Field ◽  
Level Structure ◽  
Point Clouds ◽  
Semantic Classification ◽  
Urban Scenes ◽  
Weakly Supervised ◽  
High Level ◽  
3D Lidar

We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.

scholarly journals Large-Scale ALS Data Semantic Classification Integrating Location-Context-Semantics Cues by Higher-Order CRF

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1700
Author(s):  
Wei Han ◽  
Ruisheng Wang ◽  
Daqing Huang ◽  
Cheng Xu
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Point Clouds ◽  
Higher Order ◽  
Semantic Classification ◽  
Benchmark Datasets ◽  
Media Lab

We designed a location-context-semantics-based conditional random field (LCS-CRF) framework for the semantic classification of airborne laser scanning (ALS) point clouds. For ALS datasets of high spatial resolution but with severe noise pollutions, more contexture and semantics cues, besides location information, can be exploited to surmount the decrease of discrimination of features for classification. This paper mainly focuses on the semantic classification of ALS data using mixed location-context-semantics cues, which are integrated into a higher-order CRF framework by modeling the probabilistic potentials. The location cues modeled by the unary potentials can provide basic information for discriminating the various classes. The pairwise potentials consider the spatial contextual information by establishing the neighboring interactions between points to favor spatial smoothing. The semantics cues are explicitly encoded in the higher-order potentials. The higher-order potential operates at the clusters level with similar geometric and radiometric properties, guaranteeing the classification accuracy based on semantic rules. To demonstrate the performance of our approach, two standard benchmark datasets were utilized. Experiments show that our method achieves superior classification results with an overall accuracy of 83.1% on the Vaihingen Dataset and an overall accuracy of 94.3% on the Graphics and Media Lab (GML) Dataset A compared with other classification algorithms in the literature.

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar

scholarly journals HOLISTIC PARAMETRIC RECONSTRUCTION OF BUILDING MODELS FROM POINT CLOUDS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 689-695
Author(s):  
Z. Li ◽  
W. Zhang ◽  
J. Shan
Keyword(s):  
Large Scale ◽  
Traditional Approach ◽  
Holistic Approach ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Optimization Strategy ◽  
Building Roof ◽  
Building Model ◽  
Building Models

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.

Distribution of date elements and its relationship to the types of digital libraries: Analysing aggregated metadata of a Polish large-scale distributed system

2017 ◽  
Vol 51 (3) ◽  
pp. 710-720
Author(s):  
Veslava Osinska ◽  
Krystyna K. Matusiak ◽  
Malgorzata Kowalska ◽  
Bozena Bednarek-Michalska ◽  
Piotr Malak
Keyword(s):  
Distributed System ◽  
Digital Libraries ◽  
Large Scale ◽  
Big Data Analysis ◽  
Resource Discovery ◽  
Visual Methods ◽  
Systematic Classification ◽  
Knowledge Growth ◽  
Digital Knowledge

Large-scale distributed digital library systems with aggregated metadata provide platforms for resource discovery and retrieval. For researchers, aggregated metadata offers a potential for big data analysis and exploration of digital knowledge growth. The paper reports the findings of the study that investigated the distribution of the date elements in the metadata aggregated in the Polish Federation of Digital Libraries and related it to the types of libraries. The purpose of this study was to address the gap in research about heterogeneous digital libraries and explore the dynamics of their growth. The authors included timeline characteristics of the development of Polish digital libraries and proposed a new dynamics parameter – resource release interval. They used histograms, which have been grouped according to the organizational and thematic criteria, developed for this study. All charts are characterized by two similar maximum points. Their shapes and ratio have been analysed by both statistical and visual methods. The shape of resource release interval charts revealed characteristic differences for libraries types. The proposed approach, based on time characteristics, is an important step in the development of systematic classification of digital libraries and digitizing institutions. It can be also considered as a new tool in monitoring the dynamics of digital knowledge growth.

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