The Martian landscape can now be easily explored thanks to a wealth of data collected by NASA's Mars Reconnaissance Orbiter. A new map has been created using supercomputers and cloud computing, offering a detailed, three-dimensional depiction of Mars' unique terrain.
The map, meticulously processed by USGS Astrogeology Science Center (ASC) experts, encompasses over 4,800 digital terrain models (DTMs) and a staggering collection of more than 155,000 images, enabling a captivating view of the Martian surface.
Easy Access to Mars' 3D Map
The new map enables users to easily explore and experience Mars' landscape in high resolution and 3D, providing a close approximation to an in-person encounter, says ASC team leader Jay Laura in a news release. The availability of these data has been extended to anyone with a smartphone, allowing global access for searching, utilizing, and marveling at Mars data.
The topographic data for generating the DTMs were obtained from the Context Camera on NASA's Mars Reconnaissance Orbiter. Sophisticated computer processing was employed to create a 3D view by aligning and combining overlapping pairs of images.
The USGS Astrogeology team meticulously processed and aligned the individual DTMs to global low-resolution topography, utilizing the powerful USGS Denali supercomputer for efficient data processing over several weeks. The release of these high-quality Mars topographic data aims to democratize accessibility and facilitate scientific contributions from a wider audience.
The current release of 4,800 DTMs represents only a portion of the potential data. The USGS Astrogeology team continues to process thousands of additional Context Camera image pairs, alongside higher resolution pairs from the High-Resolution Imaging Science Experiment (HiRISE) camera, to enhance topographic coverage at a resolution of 20 meters per pixel. The ongoing efforts aim to expand the availability and quality of Mars data for further scientific exploration and discovery.
Processing the Big Data From NASA's Mars Reconnaissance Orbiter
The team utilized data collected by NASA's Mars Reconnaissance Orbiter (MRO) Context Camera, which captures images with a resolution of approximately 6 meters per pixel, covering wide areas of Mars' surface.
Space.com reported that by overlapping and processing pairs of images using a supercomputer, the team created detailed 3D views, similar to how the human brain processes visual data from both eyes for depth perception.
Aligning the image pairs to each other and the topography of Mars proved challenging. The team aligned individual digital terrain models (DTMs) to global low-resolution topography and processed them in batches using the powerful Denali supercomputer. This extensive process required significant computing power and several weeks of processing time, far beyond what a standard personal computer could achieve.
Aside from the 4,800 DTMs, the team also processed over 155,000 high-resolution images from the HiRISE camera. HiRISE images, with a resolution of 25 centimeters per pixel, provide rich detail but also present challenges due to their large data size.
The team successfully processed the entire HiRISE image catalog, totaling around 114 terabytes of data, making it streamable and freely accessible. This advancement allows for analysis in the cloud without the need to download full images, streamlining the accessibility and usability of the data.
The availability of fully processed image datasets like this is crucial for applying artificial intelligence (AI) and machine learning to planetary science.
The data is hosted by Amazon in their Open Data Registry, and the USGS has released search tools to facilitate easier discovery and download for the general public. This data release marks a significant step in the USGS's efforts to develop and release analysis-ready planetary science data, enabling both human and machine-driven exploration and discovery.
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