KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computational Transport Phenomena Lab
Computational Transport Phenomena Laboratory, Division of Physical Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Earth Science and Engineering
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Structural and Functional Bioinformatics Group
Online Publication Date2021-05-19
Print Publication Date2021-08
Embargo End Date2023-05-19
Permanent link to this recordhttp://hdl.handle.net/10754/669320
MetadataShow full item record
AbstractBecause of the impending energy crisis and the environmental Impact of fossil fuels, researchers are actively looking for alternatives, such as Helium-3 on the Moon. Although it remains challenging to explore energies on the Moon due to the long physical distance, the lunar features, such as craters and rilles, can be the hotspots for such energy sources, according to recent studies. Thus, identifying lunar features, such as craters and rilles, can facilitate the discovery of Helium-3 on the Moon, which is enriched in such hotspots. However, previously, no computational method was developed to recognize the lunar features automatically for facilitating space energy discovery. In our research, we aim at developing the first deep learning method to identify multiple lunar features simultaneously for potential energy source discovery. Based on the state-of-the-art deep learning model, High Resolution Net, our model can efficiently extract semantic information and high-resolution spatial information from the input images, which ensures the performance for recognizing the lunar features. With a novel framework, our method can recognize multiple lunar features, such as craters and rilles, at the same time. We also used transfer learning to handle the data deficiency issue. With comprehensive experiments on three datasets, we show the effectiveness of the proposed method. All the datasets and codes are available online.
CitationChen, S., Li, Y., Zhang, T., Zhu, X., Sun, S., & Gao, X. (2021). Lunar features detection for energy discovery via deep learning. Applied Energy, 296, 117085. doi:10.1016/j.apenergy.2021.117085
SponsorsThis work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants: BAS/1/1351-01, URF/1/4074-01, URF/1/3769-01, URF/1/4077-01-01, and REI/1/0018-01-01.