Deep learning selection of analogues for Mars landing sites in the Qaidam Basin, Qinghai-Tibet Plateau
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909457007509504 |
|---|---|
| author | Meng, Fanwei Wang, Xiaopeng Antunes, André Zhao, Jie Zhou, Guoliang Wu, Biqiong Hao, Tianqi |
| author_facet | Meng, Fanwei Wang, Xiaopeng Antunes, André Zhao, Jie Zhou, Guoliang Wu, Biqiong Hao, Tianqi |
| contents | Remote sensing observations and Mars rover missions have recorded the presence of beaches, salt lakes, and wind erosion landforms in Martian sediments. All these observations indicate that Mars was hydrated in its early history. There used to be oceans on Mars, but they have now dried up. Therefore, signs of previous life on Mars could be preserved in the evaporites formed during this process. The study of evaporite regions has thus become a priority area for Mars' life exploration. This study proposes a method for training similarity metrics from surface land image data of Earth and Mars, which can be used for recognition or validation applications. The method will be applied in simulating tasks to select Mars landing sites using a selecting small-scale area of the Mars analaogue the evaporite region of Qaidam Basin, Qinghai-Tibet Plateau. This learning process minimizes discriminative loss function, which makes the similarity measure smaller for images from the same location and larger for images from different locations. This study selected a Convolutional Neural Networks (CNN) based model, which has been trained to explain various changes in image appearance and identify different landforms in Mars. By identifying different landforms, priority landing sites on Mars can be selected. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08584 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep learning selection of analogues for Mars landing sites in the Qaidam Basin, Qinghai-Tibet Plateau Meng, Fanwei Wang, Xiaopeng Antunes, André Zhao, Jie Zhou, Guoliang Wu, Biqiong Hao, Tianqi Geophysics Space Physics Remote sensing observations and Mars rover missions have recorded the presence of beaches, salt lakes, and wind erosion landforms in Martian sediments. All these observations indicate that Mars was hydrated in its early history. There used to be oceans on Mars, but they have now dried up. Therefore, signs of previous life on Mars could be preserved in the evaporites formed during this process. The study of evaporite regions has thus become a priority area for Mars' life exploration. This study proposes a method for training similarity metrics from surface land image data of Earth and Mars, which can be used for recognition or validation applications. The method will be applied in simulating tasks to select Mars landing sites using a selecting small-scale area of the Mars analaogue the evaporite region of Qaidam Basin, Qinghai-Tibet Plateau. This learning process minimizes discriminative loss function, which makes the similarity measure smaller for images from the same location and larger for images from different locations. This study selected a Convolutional Neural Networks (CNN) based model, which has been trained to explain various changes in image appearance and identify different landforms in Mars. By identifying different landforms, priority landing sites on Mars can be selected. |
| title | Deep learning selection of analogues for Mars landing sites in the Qaidam Basin, Qinghai-Tibet Plateau |
| topic | Geophysics Space Physics |
| url | https://arxiv.org/abs/2501.08584 |