Using UAV for automatic lithological classification of open pit mining front

Beretta, Filipe Schmitz; Rodrigues, Áttila Leães; Peroni, Rodrigo de Lemos; Costa, João Felipe Coimbra Leite

doi:10.1590/0370-44672018720122

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…As an open source framework, Tensorflow integrates several models including convolutional neural network, RNN network, and LSTM model [25]. The emergence of Tensorflow framework makes the use of deep learning model simpler and convenient and reduces the difficulty of applying deep learning model [26]. The specific experimental environment is shown in Table 1.…”

Section: Text Word Vector Representation the Corpus Is Trained Withmentioning

confidence: 99%

Question Text Classification Method of Tourism Based on Deep Learning Model

Luo

Zhang²

2022

Wireless Communications and Mobile Computing

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The Internet of Things applications are diverse in nature, and a key aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). Compared with the Internet of Things, it generates a large amount of text data with different characteristics and requirements. Aiming at the problems that machine learning and single structure deep learning model cannot effectively grasp the text emotional information in text processing, resulting in poor classification effect, this paper proposes a text classification method of tourism questions based on deep learning model. First, the corpus is trained with word2vec tool based on continuous word bag model to obtain the text word vector representation. Then, the attention mechanism is introduced into the long-short term network (LSTM), and the attention-based LSTM model is constructed for text feature extraction, which highlights the impact of different words in the input text on the text emotion category. Finally, the text features are input into the Softmax classifier to obtain the probability distribution of text categories, and the model is trained combined with the cross entropy loss function. The experimental results show that the average accuracy, recall, and F value are 0.943, 0.867, and 0.903, respectively, which has better classification effect than other methods.

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Section: Text Word Vector Representation the Corpus Is Trained Withmentioning

confidence: 99%

Question Text Classification Method of Tourism Based on Deep Learning Model

Luo

Zhang²

2022

Wireless Communications and Mobile Computing

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“…Beretta et al [76] classified rocks on the slopes of open-pit mines using UAV photogrammetry and ML. They first reconstructed the terrain through aerial photogrammetry using UAV.…”

Section: Analysis Of Rock Slopementioning

confidence: 99%

“…This result shows the influence of the variability on any type of visual identification on lithological materials. The regular sampling issue in 3-D where samples cannot be directly read in accumulated views has been solved with pseudo-regular samples, with a spacing between the floating points that is approximately equal to the average distance of the original dense cloud [76].…”

Section: Analysis Of Rock Slopementioning

confidence: 99%

Applications of Unmanned Aerial Vehicles in Mining from Exploration to Reclamation: A Review

2020

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Over the past decade, unmanned aerial vehicles (UAVs) have been used in the mining industry for various applications from mineral exploration to mine reclamation. This study aims to review academic papers on the applications of UAVs in mining by classifying the mining process into three phases: exploration, exploitation, and reclamation. Systematic reviews were performed to summarize the results of 65 articles (June 2010 to May 2020) and outline the research trend for applying UAVs in mining. This study found that UAVs are used at mining sites for geological and structural analysis via remote sensing, aerial geophysical survey, topographic surveying, rock slope analysis, working environment analysis, underground surveying, and monitoring of soil, water, ecological restoration, and ground subsidence. This study contributes to the classification of current UAV applications during the mining process as well as the identification of prevalent UAV types, data acquired by sensors, scales of targeted areas, and styles of flying control for the applications of UAVs in mining.

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“…UAVs are also used for oblique photogrammetry, which involves obtaining 3D information of the target object by planning multiple routes to obtain oblique photographs at a 45° pitch angle from four directions or acquiring images from five directions-front, back, left, right, and down-through a single route in a five-shot manner [32][33][34]. It has enjoyed increasing popularity in recent years for applications in survey and mapping, specifically in the 3D modeling of buildings (structures), acquisition of DSM/DEM of open-pit mining slopes, 3D mapping of steep coastal cliffs, establishment of large-scale slope surface models, and identification of emergent strata [35][36][37][38]. However, oblique photogrammetry is disadvantageous because its flight altitude is suitable for a high-elevation surface, and the accuracy of the acquired data is limited by distance.…”

Section: Introductionmentioning

confidence: 99%

Investigation of rock mass deformation through nap-of-the-object photogrammetry: a case study of Hongyun Golden Peak in Fanjing Mountain, China

Dang,

Fan,

Wang

et al. 2023

Preprint

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Traditional field investigation methods are inapplicable in some landslide cases where numerous dangerous rocks are distributed. Therefore, nap-of-the-object photogrammetry (NOOP) has been developed for such applications while meeting the demand for fine measurements. The new technology involves using UAVs to fly close to the object surface to obtain subcentimeter high-resolution images. Afterward, software is used to achieve a fine surface model of the target object. In this paper, a case study of the deformation characteristics of the high-steep rock mass of Hongyun Golden Peak of Fanjing Mountain in Guizhou Province, China, is conducted. Through preliminary oblique photogrammetry, which involves data processing, NOOP route planning, and fine NOOP, a realistic 3D surface model of the Golden Peak of Fanjing Mountain is obtained. The resolution of the model could reach the millimeter level (2 mm). Detailed characteristics of the 3 groups of joints and 9 deep-large cracks are obtained from the rock structure investigation and statistical analysis of fissure deformation. NOOP can be used to acquire the elevation of the target object, thus compensating for the deficiencies of oblique photogrammetry. Additionally, it accurately reproduces the terrain and fine object structures. The results of this study can provide a reference for geological investigations involving monitoring high-steepness dangerous rocks.

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Using UAV for automatic lithological classification of open pit mining front

Cited by 7 publications

References 18 publications

Question Text Classification Method of Tourism Based on Deep Learning Model

Question Text Classification Method of Tourism Based on Deep Learning Model

Applications of Unmanned Aerial Vehicles in Mining from Exploration to Reclamation: A Review

Investigation of rock mass deformation through nap-of-the-object photogrammetry: a case study of Hongyun Golden Peak in Fanjing Mountain, China

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