Large Area High-Resolution 3D Mapping of Oxia Planum: The Landing Site for the ExoMars Rosalind Franklin Rover

Tao, Yu; Müller, Jan-Peter; Conway, Susan J.; Xiong, Siting

doi:10.3390/rs13163270

Cited by 11 publications

(51 citation statements)

References 40 publications

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“…In contrast to the ground-based MDE tasks, single-image DTM estimation tasks using Mars orbital imagery [5][6][7] is different in many aspects. Firstly, the sizes of the target input images are different.…”

Section: Previous Workmentioning

confidence: 99%

“…For example, the publicly available NASA Planetary Data System (PDS) 1-2 m/pixel HiRISE DTMs (https://www.uahirise.org/dtm/, accessed on 15 October 2021) currently have a total surface coverage of 0.0297%. However, deep learning-based techniques have recently been developed that are able to retrieve DTMs using only a single HiRISE observation as input [5][6][7]. Using deep learning-based single-image DTM retrieval methods, ultra-high-resolution (25-50 cm/pixel) 3D information can now be derived "on-demand" for the remaining 3.098% area of the Martian surface, in which case, meaning scientific analysis that is reliant on high-resolution 3D will become feasible in these remaining areas.…”

Section: Introductionmentioning

confidence: 99%

“…A new set of training data is constructed using selected PDS HiRISE DTMs and iMars CTX DTMs [8] which are available through the ESA's Guest Storage Facility [9] (https://www.cosmos.esa.int/web/psa/ucl-mssl_meta-gsf, accessed on 15 October 2021). We demonstrate the new MADNet 2.0 system with single-view HiRISE images over the ExoMars Rosalind Franklin rover's landing site at Oxia Planum [7,10]. The HRSC and CTX DTM mosaics over the same area (described in [7]) that are co-aligned with the Mars Global Surveyor's Mars Orbiter Laser Altimeter (MOLA) [11,12] global areoid DTM (https: //planetarymaps.usgs.gov/mosaic/Mars_MGS_MOLA_DEM_mosaic_global_463m.tif, accessed on 15 October 2021), are used as the baseline reference data.…”

Section: Introductionmentioning

confidence: 99%

“…We demonstrate the new MADNet 2.0 system with single-view HiRISE images over the ExoMars Rosalind Franklin rover's landing site at Oxia Planum [7,10]. The HRSC and CTX DTM mosaics over the same area (described in [7]) that are co-aligned with the Mars Global Surveyor's Mars Orbiter Laser Altimeter (MOLA) [11,12] global areoid DTM (https: //planetarymaps.usgs.gov/mosaic/Mars_MGS_MOLA_DEM_mosaic_global_463m.tif, accessed on 15 October 2021), are used as the baseline reference data. Qualitative assessments using colourised and shaded relief DTMs, and quantitative assessments using semi-automated crater size-counting, and automated slanted-edge image sharpness measurements, are provided for the resultant MADNet 2.0 DTMs, in comparison with the PDS HiRISE DTMs and the MADNet 1.0 HiRISE DTM mosaic product that was produced and described in [7].…”

Section: Introductionmentioning

confidence: 99%

“…The HRSC and CTX DTM mosaics over the same area (described in [7]) that are co-aligned with the Mars Global Surveyor's Mars Orbiter Laser Altimeter (MOLA) [11,12] global areoid DTM (https: //planetarymaps.usgs.gov/mosaic/Mars_MGS_MOLA_DEM_mosaic_global_463m.tif, accessed on 15 October 2021), are used as the baseline reference data. Qualitative assessments using colourised and shaded relief DTMs, and quantitative assessments using semi-automated crater size-counting, and automated slanted-edge image sharpness measurements, are provided for the resultant MADNet 2.0 DTMs, in comparison with the PDS HiRISE DTMs and the MADNet 1.0 HiRISE DTM mosaic product that was produced and described in [7]. In addition, DTM profile and difference measurements are performed for sub-areas to show the height accuracy of the resultant MADNet 2.0 HiRISE DTMs, in comparison with the PDS HiRISE DTMs and the reference CTX DTM.…”

Section: Introductionmentioning

confidence: 99%

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MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning

et al. 2021

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The High-Resolution Imaging Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter provides remotely sensed imagery at the highest spatial resolution at 25–50 cm/pixel of the surface of Mars. However, due to the spatial resolution being so high, the total area covered by HiRISE targeted stereo acquisitions is very limited. This results in a lack of the availability of high-resolution digital terrain models (DTMs) which are better than 1 m/pixel. Such high-resolution DTMs have always been considered desirable for the international community of planetary scientists to carry out fine-scale geological analysis of the Martian surface. Recently, new deep learning-based techniques that are able to retrieve DTMs from single optical orbital imagery have been developed and applied to single HiRISE observational data. In this paper, we improve upon a previously developed single-image DTM estimation system called MADNet (1.0). We propose optimisations which we collectively call MADNet 2.0, which is based on a supervised image-to-height estimation network, multi-scale DTM reconstruction, and 3D co-alignment processes. In particular, we employ optimised single-scale inference and multi-scale reconstruction (in MADNet 2.0), instead of multi-scale inference and single-scale reconstruction (in MADNet 1.0), to produce more accurate large-scale topographic retrieval with boosted fine-scale resolution. We demonstrate the improvements of the MADNet 2.0 DTMs produced using HiRISE images, in comparison to the MADNet 1.0 DTMs and the published Planetary Data System (PDS) DTMs over the ExoMars Rosalind Franklin rover’s landing site at Oxia Planum. Qualitative and quantitative assessments suggest the proposed MADNet 2.0 system is capable of producing pixel-scale DTM retrieval at the same spatial resolution (25 cm/pixel) of the input HiRISE images.

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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning

et al. 2021

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A High‐Resolution Digital Terrain Model Mosaic of the Mars 2020 Perseverance Rover Landing Site at Jezero Crater

Tao,

Walter,

Muller

et al. 2023

Earth and Space Science

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We demonstrate the capabilities of a published MADNet monocular height estimation network in producing a refined digital terrain model (DTM) mosaic at 50 cm/pixel resolution for the Mars 2020 Perseverance rover landing site in Jezero crater on Mars. Our approach utilizes the publicly available Mars 2020 Terrain Relative Navigation (TRN) High‐Resolution Imaging Science Experiment (HiRISE) Digital Terrain Model (DTM) mosaic, which was originally created by the United States Geological Survey (USGS) Astrogeology Science Centre. Our resultant HiRISE MADNet DTM mosaic is strictly matched with the original HiRISE TRN DTM and orthoimage mosaics. These mosaics are themselves co‐aligned with the USGS TRN Context Camera (CTX) based DTM and orthoimage mosaics, as well as the ESA/DLR/FUB (European Space Agency/German Aerospace Center/Free University Berlin) High Resolution Stereo Camera (HRSC) level 5 DTM and orthoimage mosaics. In this paper, we provide a brief description of the technical details, and present both visual and quantitative assessments of the refined MADNet HiRISE Jezero DTM mosaic product. This DTM product is now publicly available at http://dx.doi.org/10.17169/refubium-38359.

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Combination of MRO SHARAD and deep-learning-based DTM to search for subsurface features in Oxia Planum, Mars

Xiong¹,

Müller²,

Tao³

et al. 2023

A&A

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Context. Oxia Planum is a mid-latitude region on Mars that attracts a great amount of interest worldwide. An orbiting radar provides an effective way to probe the Martian subsurface and detect buried layers or geomorphological features. The Shallow radar orbital radar system on board the NASA Mars reconnaissance orbiter transmits pulsed signals towards the nadir and receives returned echoes from dielectric boundaries. However, radar clutter can be induced by a higher topography of the off-nadir region than that at the nadir, which is then manifested as subsurface reflectors in the radar image. Aims. This study combines radar observations, terrain models, and surface images to investigate the subsurface features of the ExoMars landing site in Oxia Planum. Methods. Possible subsurface features are observed in radargrams. Radar clutter is simulated using the terrain models, and these are then compared to radar observations to exclude clutter and identify possible subsurface return echoes. Finally, the dielectric constant is estimated with measurements in both radargrams and surface imagery. Results. The resolution and quality of the terrain models greatly influence the clutter simulations. Higher resolution can produce finer cluttergrams, which assists in identifying possible subsurface features. One possible subsurface layering sequence is identified in one radargram. Conclusions. A combination of radar observations, terrain models, and surface images reveals the dielectric constant of the surface deposit in Oxia Planum to be 4.9–8.8, indicating that the surface-covering material is made up of clay-bearing units in this region.

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Large Area High-Resolution 3D Mapping of Oxia Planum: The Landing Site for the ExoMars Rosalind Franklin Rover

Cited by 11 publications

References 40 publications

MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning

MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning

A High‐Resolution Digital Terrain Model Mosaic of the Mars 2020 Perseverance Rover Landing Site at Jezero Crater

Combination of MRO SHARAD and deep-learning-based DTM to search for subsurface features in Oxia Planum, Mars

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