8
mine by using a state of are neuromorphic inspired sen-
sors which is known as event camera. The obtained results
show that the proposed model could classify the scene
in underground mine into regions rocks and non-rocks.
This research represents the base for applying deep learn-
ing based semantic segmentation model to digitize mining
industry for increasing miners safety.
REFERENCES
[1] WEF Mining. Metals in a sustainable world 2050. In
World Economic Forum: Geneva, Switzerland, 2015.
[2] Songmei Fan, Jingjing Yan, and Jinghua Sha.
Innovation and economic growth in the mining
industry: Evidence from china’s listed companies.
Resources Policy, 54:25–42, 2017.
[3] Datu Buyung Agusdinata, Hallie Eakin, and
Wenjuan Liu. Critical minerals for electric vehicles:
A telecoupling review. Environmental Research Letters,
17(1):013005, 2022.
[4] Yousef Ghorbani, Glen T Nwaila, Steven E Zhang,
Julie E Bourdeau, Manuel Cánovas, Javier Arzua,
and Nooraddin Nikadat. Moving towards deep
underground mineral resources: Drivers, challenges
and potential solutions. Resources Policy, 80:103222,
2023.
[5] Patrick G. Dempsey, Lydia M. Kocher, Mahiyar
F. Nasarwanji, Jonisha P. Pollard, and Ashley E.
Whitson. Emerging ergonomics issues and opportuni-
ties in mining. International Journal of Environmental
Research and Public Health, 15(11), 2018.
[6] Satar Mahdevari, Kourosh Shahriar, and Akbar
Esfahanipour. Human health and safety risks manage-
ment in underground coal mines using fuzzy topsis.
Science of the Total Environment, 488:85–99, 2014.
[7] Alicja Krzemien, Ana Suárez Sánchez, Pedro Riesgo
Fernández, Karsten´ Zimmermann, and Felipe
González Coto. Towards sustainability in under-
ground coal mine closure contexts: A methodology
proposal for environmental risk management. Journal
of Cleaner Production, 139:1044–1056, 2016.
[8] Cuebong Wong, Erfu Yang, Xiu-Tian Yan, and
Dongbing Gu. An overview of robotics and auton-
omous systems for harsh environments. In 2017
23rd International Conference on Automation and
Computing (ICAC), pages 1–6, 2017.
[9] U.S. Department of Labor, Mine Safety and Health
Administration. Mine safety and health at a glance,
Figure 11. Semantic segmentation of accumulated events images from an event camera. Unseen accumulated images have been
segmented to rock and non-rock regions by the proposed deep learning semantic segmentation model.
mine by using a state of are neuromorphic inspired sen-
sors which is known as event camera. The obtained results
show that the proposed model could classify the scene
in underground mine into regions rocks and non-rocks.
This research represents the base for applying deep learn-
ing based semantic segmentation model to digitize mining
industry for increasing miners safety.
REFERENCES
[1] WEF Mining. Metals in a sustainable world 2050. In
World Economic Forum: Geneva, Switzerland, 2015.
[2] Songmei Fan, Jingjing Yan, and Jinghua Sha.
Innovation and economic growth in the mining
industry: Evidence from china’s listed companies.
Resources Policy, 54:25–42, 2017.
[3] Datu Buyung Agusdinata, Hallie Eakin, and
Wenjuan Liu. Critical minerals for electric vehicles:
A telecoupling review. Environmental Research Letters,
17(1):013005, 2022.
[4] Yousef Ghorbani, Glen T Nwaila, Steven E Zhang,
Julie E Bourdeau, Manuel Cánovas, Javier Arzua,
and Nooraddin Nikadat. Moving towards deep
underground mineral resources: Drivers, challenges
and potential solutions. Resources Policy, 80:103222,
2023.
[5] Patrick G. Dempsey, Lydia M. Kocher, Mahiyar
F. Nasarwanji, Jonisha P. Pollard, and Ashley E.
Whitson. Emerging ergonomics issues and opportuni-
ties in mining. International Journal of Environmental
Research and Public Health, 15(11), 2018.
[6] Satar Mahdevari, Kourosh Shahriar, and Akbar
Esfahanipour. Human health and safety risks manage-
ment in underground coal mines using fuzzy topsis.
Science of the Total Environment, 488:85–99, 2014.
[7] Alicja Krzemien, Ana Suárez Sánchez, Pedro Riesgo
Fernández, Karsten´ Zimmermann, and Felipe
González Coto. Towards sustainability in under-
ground coal mine closure contexts: A methodology
proposal for environmental risk management. Journal
of Cleaner Production, 139:1044–1056, 2016.
[8] Cuebong Wong, Erfu Yang, Xiu-Tian Yan, and
Dongbing Gu. An overview of robotics and auton-
omous systems for harsh environments. In 2017
23rd International Conference on Automation and
Computing (ICAC), pages 1–6, 2017.
[9] U.S. Department of Labor, Mine Safety and Health
Administration. Mine safety and health at a glance,
Figure 11. Semantic segmentation of accumulated events images from an event camera. Unseen accumulated images have been
segmented to rock and non-rock regions by the proposed deep learning semantic segmentation model.