Based on Blockchain and Artificial Intelligence Technology: Building Crater Identification from Planetary Imagery

Year 2024, Volume: 9 Issue: 2, 19 - 32, 30.10.2024

Zeren Wu , Shitova Margarita

https://doi.org/10.28978/nesciences.1567736

Abstract

Blockchain and Artificial Intelligence (AI) technology are a core force for industrial upgrading and change. Crater counting commenced with a manual enumeration of dozens, hundreds, or thousands of craters to ascertain the lifespan of geological units on planets within the solar system. Automatic crater identification methods have sought to expedite this procedure. Prior studies have utilized computer vision methodologies using manually designed features, including light and shadow trends, circle identification, and detection of edges. The study persists, with academics now employing approaches such as AI that allow the method to generate distinct characteristics autonomously. The burgeoning discipline of AI, characterized by a rapid increase in publications and methodologies, can enhance crater counting applications, mainly through collaborative multidisciplinary initiatives. The results show that integrating blockchain and AI technology can effectively promote the construction of crater detection from planetary imagery.

Keywords

Blockchain, artificial intelligence, crater detection, intelligence

References

• Aburaed, N., Alsaad, M., Mansoori, S. A., & Al-Ahmad, H. (2022). A study on the autonomous detection of impact craters. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition, 181-194.
• Ali, R., Qadri, Y. A., Zikria, Y. B., Al-Turjman, F., Kim, B. S., & Kim, S. W. (2020). A blockchain model for trustworthiness in the internet of things (IoT)-based smart-cities. Trends in cloud-based IoT, 1-19.
• Chi, J., Wang, M., Chen, J., Hu, L., Chen, Z., Backman, L. J., & Zhang, W. (2022). Topographic orientation of scaffolds for tissue regeneration: recent advances in biomaterial design and applications. Biomimetics, 7(3), 131. https://doi.org/10.3390/biomimetics7030131
• Cunha, P. R. D., Soja, P., & Themistocleous, M. (2021). Blockchain for development: a guiding framework. Information Technology for Development, 27(3), 417-438.
• Farshidi, S., Jansen, S., España, S., & Verkleij, J. (2020). Decision support for blockchain platform selection: Three industry case studies. IEEE Transactions on Engineering Management, 67(4), 1109-1128.
• Fernandez-Martinez, M., & Sánchez-Lozano, J. M. (2021). Assessment of near‐earth asteroid deflection techniques via spherical fuzzy sets. Advances in Astronomy, 2021(1), 6678056. https://doi.org/10.1155/2021/6678056
• Ikhsan, M. I., & Arifyanto, M. I. (2022). Exploring multi-planet system wasp-148 using n-body simulation and deep learning. In Journal of Physics: Conference Series, IOP Publishing, 2243(1). https://doi.org/10.1088/1742-6596/2243/1/012010
• Jones, R. H. (2024). Meteorites and planet formation. Reviews in Mineralogy and Geochemistry, 90(1), 113-140.
• Kaur, S., Chaturvedi, S., Sharma, A., & Kar, J. (2021). A research survey on applications of consensus protocols in blockchain. Security and Communication Networks, 2021(1), 6693731. https://doi.org/10.1155/2021/6693731
• Khan, D., Jung, L. T., & Hashmani, M. A. (2021). Systematic literature review of challenges in blockchain scalability. Applied Sciences, 11(20), 9372. https://doi.org/10.3390/app11209372
• Lee, C., & Hogan, J. (2021). Automated crater detection with human level performance. Computers & Geosciences, 147, 104645. https://doi.org/10.1016/j.cageo.2020.104645
• Nassar, M., Salah, K., ur Rehman, M. H., & Svetinovic, D. (2020). Blockchain for explainable and trustworthy artificial intelligence. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(1), e1340. https://doi.org/10.1002/widm.1340
• Pansara, R. (2023). Cultivating Data Quality to Strategies, Challenges, and Impact on Decision-Making. International Journal of Management Education for Sustainable Development, 6(6), 24-33.
• Pinti, D. L. (2023). Absolute and Relative Ages. In Encyclopedia of Astrobiology, 38-40.
• Shao, Z., Yuan, S., Wang, Y., & Xu, J. (2022). Evolutions and trends of artificial intelligence (AI): research, output, influence and competition. Library Hi Tech, 40(3), 704-724.
• Sigman, M. E., & Williams, M. R. (2020). Chemometric applications in fire debris analysis. Wiley Interdisciplinary Reviews: Forensic Science, 2(5), e1368. https://doi.org/10.1002/wfs2.1368
• Silvestrini, S., Piccinin, M., Zanotti, G., Brandonisio, A., Bloise, I., Feruglio, L., & Varile, M. (2022). Optical navigation for Lunar landing based on Convolutional Neural Network crater detector. Aerospace Science and Technology, 123, 107503. https://doi.org/10.1016/j.ast.2022.107503
• Tao, Y., Conway, S. J., Muller, J. P., Putri, A. R., Thomas, N., & Cremonese, G. (2021). Single image super-resolution restoration of TGO CaSSIS colour images: Demonstration with perseverance rover landing site and Mars science targets. Remote Sensing, 13(9), 1777. https://doi.org/10.3390/rs13091777
• Yu, X., & Wang, P. (2021). Economic effects analysis of environmental regulation policy in the process of industrial structure upgrading: Evidence from Chinese provincial panel data. Science of the Total Environment, 753, 142004. https://doi.org/10.1016/j.scitotenv.2020.142004