Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning

This is the final version. Available on open access from MDPI via the DOI in this recordThis article presents a novel deep learning method for semi-automated detection of historic mining pits using aerial LiDAR data. The recent emergence of national scale remotely sensed datasets has created the potential to greatly increase the rate of analysis and recording of cultural heritage sites. However, the time and resources required to process these datasets in traditional desktop surveys presents a near insurmountable challenge. The use of artificial intelligence to carry out preliminary processing of vast areas could enable experts to prioritize their prospection focus; however, success so far has been hindered by the lack of large training datasets in this field. This study develops an innovative transfer learning approach, utilizing a deep convolutional neural network initially trained on Lunar LiDAR datasets and reapplied here in an archaeological context. Recall rates of 80% and 83% were obtained on the 0.5 m and 0.25 m resolution datasets respectively, with false positive rates maintained below 20%. These results are state of the art and demonstrate that this model is an efficient, effective tool for semi-automated object detection for this type of archaeological objects. Further tests indicated strong potential for detection of other types of archaeological objects when trained accordingly

A machine learning approach for the detection of supporting rock bolts from laser scan data in an underground mine

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordRock bolts are a crucial part of underground infrastructure support; however, current methods to locate and record their positions are manual, time consuming and generally incomplete. This paper describes an effective method to automatically locate supporting rock bolts from a 3D laser scanned point cloud. The proposed method utilises a machine learning classifier combined with point descriptors based on neighbourhood properties to classify all data points as either ‘bolt’ or ‘not-bolt’ before using the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to divide the results into candidate bolt objects. The centroids of these objects are then computed and output as simple georeferenced 3D coordinates to be used by surveyors, mine managers and automated machines. Two classifiers were tested, a random forest and a shallow neural network, with the neural network providing the more accurate results. Alongside the different classifiers, different input feature types were also examined, including the eigenvalue based geometric features popular in the remote sensing community and the point histogram based features more common in the mobile robotics community. It was found that a combination of both feature sets provided the strongest results. The obtained precision and recall scores were 0.59 and 0.70 for the individual laser points and 0.93 and 0.86 for the bolt objects. This demonstrates that the model is robust to noise and misclassifications, as the bolt is still detected even if edge points are misclassified, provided that there are enough correct points to form a cluster. In some cases, the model can detect bolts which are not visible to the human interpreter.University of Exete

Genital warts and cervical neoplasia: an epidemiological study.

Cervical carcinoma and cervical intra-epithelial neoplasia (CIN) are likely to be associated with all sexually transmitted diseases (STDs). To help discover which (if any) of the recognised STDs might actually cause these conditions, a key question is whether one particular such association is much stronger than the others. The present study is therefore only of women newly attending an STD clinic, and compares the prevalences of cytological abnormalities of the cervix among 415 women attending with genital warts, 135 with genital herpes, and 458 with trichomoniasis or gonorrhoea. Significantly more genital wart patients (8.1%) than trichomoniasis or gonorrhoea patients (1.9%) showed dyskaryotic changes (adjusted relative risk (RR) = 5.8 with 95% limits 2.5-13.5) at, or a few months before, first attendance, while no excess whatever was seen in women with genital herpes. Moreover, half the women had a subsequent smear (at an average of 3-4 years after first attendance) and, although the diagnosis at first attendance was not related to the onset rate of dyskaryotic changes observed in these subsequent smears, it was related to the onset rate of grade III cervical intra-epithelial neoplasia (CIN III), which was found in 7 previous genital wart patients, in 2 previous trichomonas patients, but in 0 previous genital herpes patients. Thus, our findings suggest that herpes is not directly relevant to dyskaryotic change, but that one or more of the human papilloma viruses that cause genital warts may be

A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordArtisanal Small-scale Mining (ASM) is a critical source of livelihoods for large areas of the Global South but it can bring with it many problems, including deforestation, water pollution and low worker safety. Timely and comprehensive management of ASM is crucial to ensure that it can take place safely and cleanly, supporting sustainable development. The informal nature of the sector presents challenges related to documenting the locations of ASM. Remote sensing methods have been used to detect ASM, although difficulties with accuracy, resolution and persistent cloud cover have been encountered. This paper proposes a method of ASM detection using a deep convolutional neural network model applied to open source Sentinel-2 multispectral satellite imagery. Firstly, the model is evaluated against both existing ASM detection methods and visual inspection of randomly sampled points. Secondly, the model is used to map mining and urban land use changes over a dataset spanning four years and 6 million hectares of southern Ghana, demonstrating the ability of this method to process very large areas. The omission and commission errors of less than 8% from the sampled points indicate that this model has achieved unprecedented levels of accuracy for the task of detecting ASM from satellite imagery. When applied to the case study area, the data on ASM trends over time demonstrate a correlation between the Ghanaian government's 2017 clampdown and ASM activities. The ASM land use category decreased by 6000 ha in 2017, despite a net increase of 15000 ha over the period 2015–2019. Additionally, the model was applied to quantify the extent of illegal mining related deforestation within Ghana's protected forests, measured at over 3500 ha, with 2400 of these lost since 2015. The results demonstrate that this methodology can detect ASM in Ghana with a high degree of accuracy at a minimal cost in terms of financial and human resources. The model shows strong generalisation abilities, offering exciting potential for using this methodology to further monitor and analyse ASM related land use changes worldwide

Using deep learning and Hough transformations to infer mineralised veins from LiDAR data over historic mining areas

This is the final version. Available on open access from ISPRS via the DOI in this recordISPRS2020: XXIV ISPRS CongressThis paper presents a novel technique to improve geological understanding in regions of historic mining activity. This is achieved through inferring the orientations of geological structures from the imprints left on the landscape by past mining activities. Open source high resolution LiDAR datasets are used to fine-tune a deep convolutional neural network designed initially for Lunar LiDAR crater identification. By using a transfer learning approach between these two very similar domains, high accuracy predictions of pit locations can be generated in the form of a raster mask of pit location probabilities. Taking the raster of the predicted pit location centres as an input, a Hough transformation is used to fit lines through the centres of the detected pits. The results demonstrate that these lines follow the patterns of known mineralised veins in the area, alongside highlighting veins which are below the scale of the published geological maps

Minor differences in body condition and immune status between avian influenza virus-infected and noninfected mallards: a sign of coevolution?

Wildlife pathogens can alter host fitness. Low pathogenic avian influenza virus (LPAIV) infection is thought to have negligible impacts on wild birds; however, effects of infection in free-living birds are largely unstudied. We investigated the extent to which LPAIV infection and shedding were associated with body condition and immune status in free-living mallards (Anas platyrhynchos), a partially migratory key LPAIV host species. We sampled mallards throughout the species\u27 annual autumn LPAIV infection peak, and we classified individuals according to age, sex, and migratory strategy (based on stable hydrogen isotope analysis) when analyzing data on body mass and five indices of immune status. Body mass was similar for LPAIV-infected and noninfected birds. The degree of virus shedding from the cloaca and oropharynx was not associated with body mass. LPAIV infection and shedding were not associated with natural antibody (NAbs) and complement titers (first lines of defense against infections), concentrations of the acute phase protein haptoglobin (Hp), ratios of heterophils to lymphocytes (H:L ratio), and avian influenza virus (AIV)-specific antibody concentrations. NAbs titers were higher in LPAIV-infected males and local (i.e., short distance) migrants than in infected females and distant (i.e., long distance) migrants. Hp concentrations were higher in LPAIV-infected juveniles and females compared to infected adults and males. NAbs, complement, and Hp levels were lower in LPAIV-infected mallards in early autumn. Our study demonstrates weak associations between infection with and shedding of LPAIV and the body condition and immune status of free-living mallards. These results may support the role of mallards as asymptomatic carriers of LPAIV and raise questions about possible coevolution between virus and host

The roles of migratory and resident birds in local avian influenza infection dynamics

Migratory birds are an increasing focus of interest when it comes to infection dynamics and the spread of avian influenza viruses (AIV ). However, we lack detailed understanding of migratory birds’ contribution to local AIV prevalence levels and their downstream socio‐economic costs and threats. To explain the potential differential roles of migratory and resident birds in local AIV infection dynamics, we used a susceptible‐infectious‐recovered (SIR ) model. We investigated five (mutually non‐ exclusive) mechanisms potentially driving observed prevalence patterns: (1) a pronounced birth pulse (e.g. the synchronised annual influx of immunologically naïve individuals), (2) short‐term immunity, (3) increase in susceptible migrants, (4) differential susceptibility to infection (i.e. transmission rate) for migrants and residents, and (5) replacement of migrants during peak migration. SIR models describing all possible combinations of the five mechanisms were fitted to individual AIV infection data from a detailed longitudinal surveillance study in the partially migratory mallard duck (Anas platyrhynchos ). During autumn and winter, the local resident mallard community also held migratory mallards that exhibited distinct AIV infection dynamics. Replacement of migratory birds during peak migration in autumn was found to be the most important mechanism driving the variation in local AIV infection patterns. This suggests that a constant influx of migratory birds, likely immunological naïve to locally circulating AIV strains, is required to predict the observed temporal prevalence patterns and the distinct differences in prevalence between residents and migrants. Synthesis and applications . Our analysis reveals a key mechanism that could explain the amplifying role of migratory birds in local avian influenza virus infection dynamics; the constant flow and replacement of migratory birds during peak migration. Apart from monitoring efforts, in order to achieve adequate disease management and control in wildlife—with knock‐on effects for livestock and humans,—we conclude that it is crucial, in future surveillance studies, to record host demographical parameters such as population density, timing of birth and turnover of migrants