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Dynamic monitoring of dark slope streaks within large-scale Martian scenes using multitemporal high-resolution orbiter imagery.

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Title: Dynamic monitoring of dark slope streaks within large-scale Martian scenes using multitemporal high-resolution orbiter imagery.
Authors: Wan, Bo1 (AUTHOR) wanbo@tongji.edu.cn, Liu, Sicong1,2 (AUTHOR) sicong.liu@tongji.edu.cn, Tong, Xiaohua1,2 (AUTHOR) xhtong@tongji.edu.cn, Xie, Huan1,2 (AUTHOR) huanxie@tongji.edu.cn, Feng, Yongjiu1,2 (AUTHOR) yjfeng@tongji.edu.cn, Jin, Yanmin1,2 (AUTHOR) jinyanmin@tongji.edu.cn, Du, Kecheng1 (AUTHOR) kecheng_du@tongji.edu.cn, Zhang, Jie1 (AUTHOR) zhangjie22@tongji.edu.cn
Source: Advances in Space Research. May2026, Vol. 77 Issue 10, p10925-10943. 19p.
Subjects: Martian surface, Object recognition (Computer vision), Time series analysis, High resolution imaging, Dust, Geomorphology, Topography
Abstract: Dark slope streaks (DSS) represent an intriguing geomorphological landform observed on Martian surface. Studying DSS can provide insights into a range of scientific issues, including climate evolution and geology processes on Mars. However, existing manual identification method is time-consuming and labor-intensive. Automatic methods for extracting DSS in large region have not yet been widely adopted. This study employs Mask R-CNN and manual verification to extract of DSS and to investigate their multi-temporal variations across large-scale Martian regions. Images from the High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter (MRO), taken from 30 images across regions on Mars where DSS distributions are present, were used to construct a DSS dataset for training and validating the deep learning method. Additionally, we conducted a comprehensive analysis of multi-temporal variations in three large experimental regions near the Amazonis Planitia, located in the mid-latitude areas of Mars' northern hemisphere. By examining these extensive regions across multiple time periods, we obtained the change number of DSS. Changes in these regions were assessed by comparing surface temperatures variation, dust deposition, and terrain characteristics. The results reveal that DSS formation is influenced by multiple environmental factors. In particular, the occurrence of new, large-scale DSS events shows no clear monotonic relationship with surface temperature fluctuations but is more closely associated with intensified dust activity during the autumn and winter. This suggests that DSS initiation is sporadic rather than thermally controlled. Furthermore, regions with greater topographic relief exhibit fewer new DSS occurrences compared to flatter terrains, suggesting that complex local topography may inhibit dust mobility, thereby limiting DSS formation. These observations support a dry formation mechanism. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:Dark slope streaks (DSS) represent an intriguing geomorphological landform observed on Martian surface. Studying DSS can provide insights into a range of scientific issues, including climate evolution and geology processes on Mars. However, existing manual identification method is time-consuming and labor-intensive. Automatic methods for extracting DSS in large region have not yet been widely adopted. This study employs Mask R-CNN and manual verification to extract of DSS and to investigate their multi-temporal variations across large-scale Martian regions. Images from the High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter (MRO), taken from 30 images across regions on Mars where DSS distributions are present, were used to construct a DSS dataset for training and validating the deep learning method. Additionally, we conducted a comprehensive analysis of multi-temporal variations in three large experimental regions near the Amazonis Planitia, located in the mid-latitude areas of Mars' northern hemisphere. By examining these extensive regions across multiple time periods, we obtained the change number of DSS. Changes in these regions were assessed by comparing surface temperatures variation, dust deposition, and terrain characteristics. The results reveal that DSS formation is influenced by multiple environmental factors. In particular, the occurrence of new, large-scale DSS events shows no clear monotonic relationship with surface temperature fluctuations but is more closely associated with intensified dust activity during the autumn and winter. This suggests that DSS initiation is sporadic rather than thermally controlled. Furthermore, regions with greater topographic relief exhibit fewer new DSS occurrences compared to flatter terrains, suggesting that complex local topography may inhibit dust mobility, thereby limiting DSS formation. These observations support a dry formation mechanism. [ABSTRACT FROM AUTHOR]
ISSN:02731177
DOI:10.1016/j.asr.2026.02.052