Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits

Aye, K.-M.; Schwamb, Megan E.; Portyankina, Ganna; Hansen, C. J.; McMaster, Adam; Miller, Grant; Carstensen, Brian; Snyder, C. T.; Parrish, Michael; Lynn, Stuart; Mai, Chuhong; Miller, David R.; Simpson, Robert J.; Smith, Arfon M.

doi:10.1016/j.icarus.2018.08.018

Cited by 26 publications

(38 citation statements)

References 41 publications

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“…Featuring a deformable search window that can be rotated, scaled and translated as it searches for pixels; this adaptive algorithm is especially suited for accurate matches on steep terrain, where significant foreshortening can cause perspective distortion, which is detrimental to matching. Based on the Lucas-Kanade tem- plate tracking algorithm (Baker and Matthews, 2004;Nefian et al, 2009;Beyer et al, 2018), the ASP Bayes EM treats the Lucas-Kanade variables as random parameters within an expectation maximization (EM) framework. A Gaussian mixture component is also included, which provides a degree of robustness against image noise (Beyer et al, 2018).…”

Section: The Au Methodsmentioning

confidence: 99%

“…However, with an along track resolution of 300 m and an interpolated grid spacing of ∼ 460 m at the Equator (Smith et al, 2001), MOLA has limited potential for the study of features < 10 km in scale. During the last 2 billion years, substantial dynamic landscape evolution has occurred at or below this scale (Day and Kocurek, 2016;Andrieu et al, 2018;Banks et al, 2018;Aye et al, 2019). These features are increasingly well covered by 2-D imagery, but there is great demand for DEMs with horizontal resolution much better than MOLA's that can be combined with imagery to resolve these landscapes.…”

Section: Introductionmentioning

confidence: 99%

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Creating HiRISE digital elevation models for Mars using the open-source Ames Stereo Pipeline

Hepburn

Holt

Hubbard

et al. 2019

Geosci. Instrum. Method. Data Syst.

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Abstract. The present availability of sub-decametre digital elevation models on Mars – crucial for the study of surface processes – is scarce. In contrast to low-resolution global datasets, such models enable the study of landforms <10 km in size, which is the primary scale at which geomorphic processes have been active on Mars over the last 10–20 Myr . Stereogrammetry is a means of producing digital elevation models from stereo pairs of images. The HiRISE camera on board the Mars Reconnaissance Orbiter has captured >3000 stereo pairs at 0.25 m pixel−1 resolution, enabling the creation of high-resolution digital elevation models (1–2 m pixel−1). Hitherto, only ∼500 of these pairs have been processed and made publicly available. Existing pipelines for the production of digital elevation models from stereo pairs, however, are built upon commercial software, rely upon sparsely available intermediate data, or are reliant on proprietary algorithms. In this paper, we present and test the output of a new pipeline for producing digital elevation models from HiRISE stereo pairs that is built entirely upon the open-source NASA Ames Stereo Pipeline photogrammetric software, making use of freely available data for cartographic rectification. This pipeline is designed for simple application by researchers interested in the use of high-resolution digital elevation models. Implemented here on a research computing cluster, this pipeline can also be used on consumer-grade UNIX computers. We produce and evaluate four digital elevation models using the pipeline presented here. Each are globally well registered, with accuracy similar to those of digital elevation models produced elsewhere.

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Section: The Au Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Creating HiRISE digital elevation models for Mars using the open-source Ames Stereo Pipeline

Hepburn

Holt

Hubbard

et al. 2019

Geosci. Instrum. Method. Data Syst.

View full text Add to dashboard Cite

show abstract

“…Later, image data from Mars Reconnaisssance Orbiter's Context Camera (CTX) [3] and from Mars Reconnaisssance's High Resolution Imaging Science Experiment (HiRISE) [23] have been used. CTX images have been used in [15,18] and HiRISE images have been used in [12][13][14][15][16][17]19,21]. Besides the used instruments two groups of approaches emerge.…”

Section: Related Workmentioning

confidence: 99%

“…Besides the used instruments two groups of approaches emerge. The first group focusses on classifying some specific Martian landforms, such as dunes [10,11], rootless cones [15], transverse aeolian ridges [13,15], dark slope streaks [14], or jet deposits [21]. The second group does not focus on a single landform, but rather on a set of different landforms.…”

Section: Related Workmentioning

confidence: 99%

DoMars16k: A Diverse Dataset for Weakly Supervised Geomorphologic Analysis on Mars

et al. 2020

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Mapping planetary surfaces is an intricate task that forms the basis for many geologic, geomorphologic, and geographic studies of planetary bodies. In this work, we present a method to automate a specific type of planetary mapping, geomorphic mapping, taking machine learning as a basis. Additionally, we introduce a novel dataset, termed DoMars16k, which contains 16,150 samples of fifteen different landforms commonly found on the Martian surface. We use a convolutional neural network to establish a relation between Mars Reconnaissance Orbiter Context Camera images and the landforms of the dataset. Afterwards, we employ a sliding-window approach in conjunction with a Markov Random field smoothing to create maps in a weakly supervised fashion. Finally, we provide encouraging results and carry out automated geomorphological analyses of Jezero crater, the Mars2020 landing site, and Oxia Planum, the prospective ExoMars landing site.

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“…However, with an along track resolution of 300 m, and an interpolated grid-spacing of ∼460 m at the equator (Smith et al, 2001), MOLA has limited potential for the study of features <10 km in scale. During the last 2 billion years, substantial dynamic landscape evolution has occurred at or below this scale (Day and Kocurek, 2016;Andrieu et al, 2018;Banks et al, 2018;Aye et al, 2019). These features are increasingly well-covered by 2D imagery, but there is great demand for DEMs with horizontal resolution much better than MOLA's that can be combined with imagery to resolve these landscapes.…”

mentioning

confidence: 99%

Creating HiRISE digital elevation models for Mars using the open-source Ames Stereo Pipeline

Hepburn¹,

Holt²,

Hubbard³

et al. 2019

Preprint

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Abstract. The present availability of sub-decametre digital elevation models on Mars – crucial for the study of surface processes – is scarce. In contrast to the globally-available but low-resolution datasets, such models enable the study of landforms 3000 stereo pairs at 25 cm/pixel resolution, enabling the creation of high-resolution digital elevation models (1–2 m/pixel). However, only ~ 500 of these pairs have been processed and made publicly available to date. Existing pipelines for the production of digital elevation models from stereo-pairs, however, are built upon commercial software, rely upon sparsely-available intermediate data, or are reliant on proprietary algorithms. Here, we present and test the output of a new pipeline for producing digital elevation models from HiRISE stereo pairs that is built entirely upon the open source NASA Ames Stereo Pipeline photogrammetric software, making use of freely available data for cartographic rectification. This pipeline is implemented here on a research computing cluster, but can also be used on consumer-grade UNIX computers. The four output digital elevation models produced using the pipeline presented here are globally well-registered, with accuracy similar to those of multiple digital elevation models produced elsewhere.

show abstract

Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits

Cited by 26 publications

References 41 publications

Creating HiRISE digital elevation models for Mars using the open-source Ames Stereo Pipeline

Creating HiRISE digital elevation models for Mars using the open-source Ames Stereo Pipeline

DoMars16k: A Diverse Dataset for Weakly Supervised Geomorphologic Analysis on Mars

Creating HiRISE digital elevation models for Mars using the open-source Ames Stereo Pipeline

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