Mining Goes Digital – Mueller et al. (Eds)
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
661
High-resolution modeling of open-pit slopes using UAV and
photogrammetry
R. Battulwar, J. Valencia, G. Winkelmaier, B. Parvin & J. Sattarvand
University of Nevada, Reno, Nevada, USA
ABSTRACT: Accurate detection and characterization of tension cracks for stability analyses purposes in open-pit mines requires high-resolution terrain models of the mine. This
paper presents several contributions to the field of remote sensing of mines using Unmanned
Aerial Vehicles (UAVs). A multivariate linear regression model for battery power consumption of drone has been derived by conducting an empirical study in various flight scenarios.
The model has also been validated using data from a test flight. A genetic algorithm for
solving the problem of flight planning and optimization have been proposed. The developed
power consumption model has been used as the fitness function in this algorithm. A novel
Android application has been developed for autonomous drone flights to follow mine terrain and capture high-resolution images. These images will be stitched together to be used
for crack detection. A case study has been presented showcasing the ability of the developed
software to achieve high-resolution imaging of an area.
1 INTRODUCTION
The past decade has witnessed an unparalleled augmentation in the field of UAVs. Their
presence can be seen in modern mining operations throughout the world. With the increased
availability of cost-effective drones and skilled personnel, many drone companies are looking
for potential applications of drones in the mining industry. Considering the vast nature of
mines and their hazardous environment, UAV technology is the perfect tool to be applied in
these operations. As a result, many mining giants like Rio Tinto, BHP Billiton, Newmont,
and Barrick have already been using drones for surveying, volumetric analyses, and monitoring purposes. Transforming these tasks into an aerial task gives a big advantage in terms of
time and man power.
Despite advances in slope monitoring, identification of tension cracks remains a laborious
task on the part of geotechnical engineers. Some of the drawbacks of the current slope monitoring practices which are addressed through this research work are as follows. (i) The radarbased scanners essentially monitor the slope movement but cannot identify cracks which are
useful for numerical modeling of slope stability. Further, their deployment is expensive and
unaffordable for small mining operations. The price of an SSR can go as high as 450,000
USD which includes installation as well as training cost (Hannon, 2007). (ii) The process of
installing instrumentation in a moving wall to monitor the expansion of each crack is firstly
unsafe and dangerous. Secondly, this task is slow and time-consuming.
In this study, it is proposed to utilize (i) a UAV for imaging and mapping the open-pit
mines, and (ii) state-of-the-art machine learning techniques for automated identification of
cracks and characterizing their dynamics. The results will be an early-warning system that
alerts the geotechnical engineers about the location of potential failures so that the necessary
safety steps can be executed.
The availability of up-to-date high-resolution terrain models of a mine is a crucial bottleneck for achieving accurate detection of tension cracks or any other useful purposes like
volumetric analysis or designed vs actual survey of ramps and benches. This problem is
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
661
High-resolution modeling of open-pit slopes using UAV and
photogrammetry
R. Battulwar, J. Valencia, G. Winkelmaier, B. Parvin & J. Sattarvand
University of Nevada, Reno, Nevada, USA
ABSTRACT: Accurate detection and characterization of tension cracks for stability analyses purposes in open-pit mines requires high-resolution terrain models of the mine. This
paper presents several contributions to the field of remote sensing of mines using Unmanned
Aerial Vehicles (UAVs). A multivariate linear regression model for battery power consumption of drone has been derived by conducting an empirical study in various flight scenarios.
The model has also been validated using data from a test flight. A genetic algorithm for
solving the problem of flight planning and optimization have been proposed. The developed
power consumption model has been used as the fitness function in this algorithm. A novel
Android application has been developed for autonomous drone flights to follow mine terrain and capture high-resolution images. These images will be stitched together to be used
for crack detection. A case study has been presented showcasing the ability of the developed
software to achieve high-resolution imaging of an area.
1 INTRODUCTION
The past decade has witnessed an unparalleled augmentation in the field of UAVs. Their
presence can be seen in modern mining operations throughout the world. With the increased
availability of cost-effective drones and skilled personnel, many drone companies are looking
for potential applications of drones in the mining industry. Considering the vast nature of
mines and their hazardous environment, UAV technology is the perfect tool to be applied in
these operations. As a result, many mining giants like Rio Tinto, BHP Billiton, Newmont,
and Barrick have already been using drones for surveying, volumetric analyses, and monitoring purposes. Transforming these tasks into an aerial task gives a big advantage in terms of
time and man power.
Despite advances in slope monitoring, identification of tension cracks remains a laborious
task on the part of geotechnical engineers. Some of the drawbacks of the current slope monitoring practices which are addressed through this research work are as follows. (i) The radarbased scanners essentially monitor the slope movement but cannot identify cracks which are
useful for numerical modeling of slope stability. Further, their deployment is expensive and
unaffordable for small mining operations. The price of an SSR can go as high as 450,000
USD which includes installation as well as training cost (Hannon, 2007). (ii) The process of
installing instrumentation in a moving wall to monitor the expansion of each crack is firstly
unsafe and dangerous. Secondly, this task is slow and time-consuming.
In this study, it is proposed to utilize (i) a UAV for imaging and mapping the open-pit
mines, and (ii) state-of-the-art machine learning techniques for automated identification of
cracks and characterizing their dynamics. The results will be an early-warning system that
alerts the geotechnical engineers about the location of potential failures so that the necessary
safety steps can be executed.
The availability of up-to-date high-resolution terrain models of a mine is a crucial bottleneck for achieving accurate detection of tension cracks or any other useful purposes like
volumetric analysis or designed vs actual survey of ramps and benches. This problem is