Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1145/3303714.3303732 |
Increase the Exploitation of Mars Satellite Images Via Deep Learning Techniques | |
AlMarzooqi, Mariam; AlNaqbi, Asayel; AlMheiri, Aysha; Bezawada, Srikanth; Mohamed, Elfadil A.; Zaki, Nazar | |
通讯作者 | Zaki, Nazar |
会议名称 | International Conference on Robotics, Control and Automation Engineering (RCAE) / International Conference on Advanced Mechanical and Electrical Engineering (AMEE) |
会议日期 | DEC 26-28, 2018 |
会议地点 | Beijing, PEOPLES R CHINA |
英文摘要 | Mars is the fourth planet from the sun and the second smallest planet in the solar system after Mercury. Like Earth, Mars has a range of surface features such as valleys, deserts, and polar ice caps. Scientists around the globe have developed a specific interest in the terrain and climate of Mars because it is believed to have the potential to host life. To assist scientists to discover past or present life on Mars, we developed machine learning models (based on deep learning) to analyze the satellite images received from the Red Planet. The models automatically eliminated satellite images that were of a low quality and subsequently classified the high-quality images based on climate/environmental conditions. The models were tested on sample datasets and demonstrated the ability to achieve considerable accuracy. We also integrated additional functionality to convert two-dimensional (2D) satellite images into an informative (3D) format for better analysis and exploration. Furthermore, the solution was integrated into a mobile application that can be used by scientists and members of the public who are interested in space science. |
英文关键词 | Mars space science deep learning NIMA satellite images 3D images |
来源出版物 | PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ROBOTICS, CONTROL AND AUTOMATION ENGINEERING (RAE 2018) AND INTERNATIONAL CONFERENCE ON ADVANCED MECHANICAL AND ELECTRICAL ENGINEERING (AMEE 2018) |
出版年 | 2018 |
页码 | 171-175 |
EISBN | 978-1-4503-6102-6 |
出版者 | ASSOC COMPUTING MACHINERY |
类型 | Proceedings Paper |
语种 | 英语 |
国家 | U Arab Emirates |
收录类别 | CPCI-S |
WOS记录号 | WOS:000473331800034 |
WOS关键词 | QUALITY ASSESSMENT |
WOS类目 | Automation & Control Systems ; Computer Science, Theory & Methods ; Robotics |
WOS研究方向 | Automation & Control Systems ; Computer Science ; Robotics |
资源类型 | 会议论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/308034 |
作者单位 | United Arab Emirates Univ, Coll Informat Technol, Al Ain 15551, U Arab Emirates |
推荐引用方式 GB/T 7714 | AlMarzooqi, Mariam,AlNaqbi, Asayel,AlMheiri, Aysha,et al. Increase the Exploitation of Mars Satellite Images Via Deep Learning Techniques[C]:ASSOC COMPUTING MACHINERY,2018:171-175. |
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