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

Lone motherhood in England, 1945–1990 : economy, agency and identity

This thesis examines the history of lone motherhood in England between 1945 and 1990. Most studies of lone motherhood after 1945 have focused on unmarried women, but this study looks at all routes into lone motherhood: pre-marital pregnancy, separation, divorce and widowhood. Existing research on post-1945 history has tended to prioritise the role of the state in determining demographic trends in family life and behaviour. This thesis uses oral history evidence to demonstrate how women’s agency shaped routes into lone motherhood as well as their management of female-headed household economies and their sense of identity within the post-war welfare state. A sample of fifty oral history interviews, primarily selected from the Millennium Memory Bank at the National Sound Archive forms the basis of the thesis. Interviewees are predominantly working-class and from urban locations across all regions of England. The sample is divided into five generational cohorts, which span the immediate post-war period, 1950s, 1960s 1970s and 1980s. Childhood, adolescent and marital experiences are analysed within each cohort in order to understand changes and continuities in women’s entrance into lone motherhood. In addition, contemporary sociological sources are discussed alongside the oral histories in order to understand the relationship between the sociological construction of lone motherhood and lone mothers’ developing social identities in the post-war period. Three categories of analysis in relation to the experience of lone motherhood feature: ‘Accommodation and Housing,’ ‘Maternal Economy’ and ‘Social Membership and Identity.’ The study concludes that women’s greater entrance into lone motherhood after 1970 was driven by their rejection of an untenable social and economic division of labour in marriage, which remained consistent across our period. The development of sociological classification in relation to one parent families in the 1960s is demonstrated to have been taken-up by women from the 1970s onwards to legitimize their entitlement to state assistance and housing. This entitlement is also argued to have rested on an inter-generational maternal identity that understood the importance of maternity and the false demarcation between waged and domestic labour, which working-class women, inside and outside of marriage, confronted across the twentieth-century

How do parents experience relocation disputes in the family courts?

Relocation cases are known to be amongst the most difficult decisions for family court judges. This article reports the findings of an empirical study of parents who were involved in relocation disputes, reporting their views on the experience of being involved in one of these difficult cases. We consider the origins of the disputes and parents' perceptions of how their cases were resolved, as well as some initial discussion of the aftermath of the cases as seen in the first few months

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

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 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

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