Research

18 pages, 5694 KiB

Open AccessArticle

Assessment of a Rock Pillar Failure by Using Change Detection Analysis and FEM Modelling

by Claudio Vanneschi, Giovanni Mastrorocco and Riccardo Salvini

ISPRS Int. J. Geo-Inf. 2021, 10(11), 774; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10110774 - 13 Nov 2021

Cited by 5 | Viewed by 1702

In this paper, various methods have been used to control and evaluate engineering difficulties in mining accurately. Different unstable scenarios occurring at the surfaces of underground mine walls, have been identified by comparing 3D terrestrial laser scanning surveys and subsequent point cloud 3D [...] Read more.

In this paper, various methods have been used to control and evaluate engineering difficulties in mining accurately. Different unstable scenarios occurring at the surfaces of underground mine walls, have been identified by comparing 3D terrestrial laser scanning surveys and subsequent point cloud 3D analysis. These techniques, combined with a change detection analysis approach and the integration of rock mechanics’ modelling, represent an asset for the assessment and management of the risk in mining. The change detection analysis can be used as control of mining and industrial processes as well as to identify valid model scenarios for establishing possible failure causes. A pillar spalling failure has been identified in an Italian underground marble quarry and this topic represents the basis of the present paper. A Finite-Element Method was used to verify the occurrence of relatively high-stress concentrations in the pillar. The FEM modelling revealed that stresses in the proximity of the pillar may have sufficient magnitude to induce cracks growth and spalling failure. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing Derived Point Cloud Processing)

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26 pages, 20305 KiB

Open AccessArticle

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

by Zhizhong Xing, Shuanfeng Zhao, Wei Guo, Xiaojun Guo and Yuan Wang

ISPRS Int. J. Geo-Inf. 2021, 10(7), 482; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070482 - 13 Jul 2021

Cited by 20 | Viewed by 3377

Abstract

Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of [...] Read more.

Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing Derived Point Cloud Processing)

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22 pages, 5541 KiB

Open AccessArticle

Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network

by Jianfeng Zhu, Lichun Sui, Yufu Zang, He Zheng, Wei Jiang, Mianqing Zhong and Fei Ma

ISPRS Int. J. Geo-Inf. 2021, 10(7), 444; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070444 - 29 Jun 2021

Cited by 6 | Viewed by 2481

Abstract

In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image [...] Read more.

In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing Derived Point Cloud Processing)

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15 pages, 2802 KiB

Open AccessArticle

A Closed-Form Solution to Planar Feature-Based Registration of LiDAR Point Clouds

by Yongbo Wang, Nanshan Zheng and Zhengfu Bian

ISPRS Int. J. Geo-Inf. 2021, 10(7), 435; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070435 - 25 Jun 2021

Cited by 6 | Viewed by 1743

Abstract

Since pairwise registration is a necessary step for the seamless fusion of point clouds from neighboring stations, a closed-form solution to planar feature-based registration of LiDAR (Light Detection and Ranging) point clouds is proposed in this paper. Based on the Plücker coordinate-based representation [...] Read more.

Since pairwise registration is a necessary step for the seamless fusion of point clouds from neighboring stations, a closed-form solution to planar feature-based registration of LiDAR (Light Detection and Ranging) point clouds is proposed in this paper. Based on the Plücker coordinate-based representation of linear features in three-dimensional space, a quad tuple-based representation of planar features is introduced, which makes it possible to directly determine the difference between any two planar features. Dual quaternions are employed to represent spatial transformation and operations between dual quaternions and the quad tuple-based representation of planar features are given, with which an error norm is constructed. Based on L2-norm-minimization, detailed derivations of the proposed solution are explained step by step. Two experiments were designed in which simulated data and real data were both used to verify the correctness and the feasibility of the proposed solution. With the simulated data, the calculated registration results were consistent with the pre-established parameters, which verifies the correctness of the presented solution. With the real data, the calculated registration results were consistent with the results calculated by iterative methods. Conclusions can be drawn from the two experiments: (1) The proposed solution does not require any initial estimates of the unknown parameters in advance, which assures the stability and robustness of the solution; (2) Using dual quaternions to represent spatial transformation greatly reduces the additional constraints in the estimation process. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing Derived Point Cloud Processing)

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17 pages, 4059 KiB

Open AccessArticle

Methodological Proposal for Automated Detection of the Wildland–Urban Interface: Application to the Metropolitan Regions of Madrid and Barcelona

by Andrea Zambrano-Ballesteros, Sabina Florina Nanu, José Tomás Navarro-Carrión and Alfredo Ramón-Morte

ISPRS Int. J. Geo-Inf. 2021, 10(6), 381; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060381 - 03 Jun 2021

Cited by 2 | Viewed by 2818

Abstract

Official information on Land Use Land Cover is essential for mapping wildland–urban interface (WUI) zones. However, these resources do not always provide the geometrical or thematic accuracy required to delimit buildings that are easily exposed to risk of wildfire at the appropriate scale. [...] Read more.

Official information on Land Use Land Cover is essential for mapping wildland–urban interface (WUI) zones. However, these resources do not always provide the geometrical or thematic accuracy required to delimit buildings that are easily exposed to risk of wildfire at the appropriate scale. This research shows that the integration of active remote sensing and official Land Use Land Cover (LULC) databases, such as the Spanish Land Use Land Cover information system (SIOSE), creates the synergy capable of achieving this. An automated method was developed to detect WUI zones by the massive geoprocessing of data from official and open repositories of the Spanish national plan for territory observation (PNOT) of the Spanish national geographic institute (IGN), and it was tested in the most important metropolitan zones in Spain: Barcelona and Madrid. The processing of trillions of LiDAR data and their integration with thousands of SIOSE polygons were managed in a Linux environment, with libraries for geographic processing and a PostgreSQL database server. All this allowed the buildings that are exposed to wildfire risk with a high level of accuracy to be obtained with a methodology that can be applied anywhere in the Spanish territory. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing Derived Point Cloud Processing)

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18 pages, 78481 KiB

Open AccessArticle

Exploiting a Semi-Automatic Point Cloud Segmentation Method to Improve the Quality of Rock-Mass Characterization. The Cima Grappa Conservative Restoration Case Study

by Francesco Mugnai, Paolo Farina and Grazia Tucci

ISPRS Int. J. Geo-Inf. 2021, 10(5), 276; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050276 - 28 Apr 2021

Cited by 5 | Viewed by 2399

Abstract

This paper presents results from applying semi-automatic point cloud segmentation methods in the underground tunnels within the Military Shrine’s conservative restoration project in Cima Grappa (Italy). The studied area, which has a predominant underground development distributed in a network of tunnels, is characterized [...] Read more.

This paper presents results from applying semi-automatic point cloud segmentation methods in the underground tunnels within the Military Shrine’s conservative restoration project in Cima Grappa (Italy). The studied area, which has a predominant underground development distributed in a network of tunnels, is characterized by diffuse rock collapsing. In such a context, carrying out surveys and other technical operations are dangerous activities. Considering safety restrictions and unreachable impervious tunnels, having approached the study area with the scan-line survey technique resulted in only partial rock mass characterization. Hence, the geo-mechanical dataset was integrated, applying a semi-automatic segmentation method to the point clouds acquired through terrestrial laser scanning (TLS). The combined approach allowed for remote performance of detailed rock mass characterization, even remotely, in a short time and with a limited operators presence on site. Moreover, it permitted extending assessing tunnels’ stability and state of conservation to the inaccessible areas. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing Derived Point Cloud Processing)

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20 pages, 5365 KiB

Open AccessArticle

Maximizing Impacts of Remote Sensing Surveys in Slope Stability—A Novel Method to Incorporate Discontinuities into Machine Learning Landslide Prediction

by Lingfeng He, John Coggan, Mirko Francioni and Matthew Eyre

ISPRS Int. J. Geo-Inf. 2021, 10(4), 232; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040232 - 06 Apr 2021

Cited by 10 | Viewed by 2729

Abstract

This paper proposes a novel method to incorporate unfavorable orientations of discontinuities into machine learning (ML) landslide prediction by using GIS-based kinematic analysis. Discontinuities, detected from photogrammetric and aerial LiDAR surveys, were included in the assessment of potential rock slope instability through GIS-based [...] Read more.

This paper proposes a novel method to incorporate unfavorable orientations of discontinuities into machine learning (ML) landslide prediction by using GIS-based kinematic analysis. Discontinuities, detected from photogrammetric and aerial LiDAR surveys, were included in the assessment of potential rock slope instability through GIS-based kinematic analysis. Results from the kinematic analysis, coupled with several commonly used landslide influencing factors, were adopted as input variables in ML models to predict landslides. In this paper, various ML models, such as random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and deep learning neural network (DLNN) models were evaluated. Results of two validation methods (confusion matrix and ROC curve) show that the involvement of discontinuity-related variables significantly improved the landslide predictive capability of these four models. Their addition demonstrated a minimum of 6% and 4% increase in the overall prediction accuracy and the area under curve (AUC), respectively. In addition, frequency ratio (FR) analysis showed good consistency between landslide probability that was characterized by FR values and discontinuity-related variables, indicating a high correlation. Both results of model validation and FR analysis highlight that inclusion of discontinuities into ML models can improve landslide prediction accuracy. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing Derived Point Cloud Processing)

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16 pages, 12476 KiB

Open AccessArticle

Data Gap Classification for Terrestrial Laser Scanning-Derived Digital Elevation Models

by Matthew S. O’Banion, Michael J. Olsen, Jeff P. Hollenbeck and William C. Wright

ISPRS Int. J. Geo-Inf. 2020, 9(12), 749; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120749 - 15 Dec 2020

Cited by 6 | Viewed by 2156

Abstract

Extensive gaps in terrestrial laser scanning (TLS) point cloud data can primarily be classified into two categories: occlusions and dropouts. These gaps adversely affect derived products such as 3D surface models and digital elevation models (DEMs), requiring interpolation to produce a spatially continuous [...] Read more.

Extensive gaps in terrestrial laser scanning (TLS) point cloud data can primarily be classified into two categories: occlusions and dropouts. These gaps adversely affect derived products such as 3D surface models and digital elevation models (DEMs), requiring interpolation to produce a spatially continuous surface for many types of analyses. Ultimately, the relative proportion of occlusions in a TLS survey is an indicator of the survey quality. Recognizing that regions of a scanned scene occluded from one scan position are likely visible from another point of view, a prevalence of occlusions can indicate an insufficient number of scans and/or poor scanner placement. Conversely, a prevalence of dropouts is ordinarily not indicative of survey quality, as a scanner operator cannot usually control the presence of specular reflective or absorbent surfaces in a scanned scene. To this end, this manuscript presents a novel methodology to determine data completeness by properly classifying and quantifying the proportion of the site that consists of point returns and the two types of data gaps. Knowledge of the data gap origin can not only facilitate the judgement of TLS survey quality, but it can also identify pooled water when water reflections are the main source of dropouts in a scene, which is important for ecological research, such as habitat modeling. The proposed data gap classification methodology was successfully applied to DEMs for two study sites: (1) A controlled test site established by the authors for the proof of concept of classification of occlusions and dropouts and (2) a rocky intertidal environment (Rabbit Rock) presenting immense challenges to develop a topographic model due to significant tidal fluctuations, pooled water bodies, and rugged terrain generating many occlusions. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing Derived Point Cloud Processing)

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