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Efficient DEM Generation from Mars Express Data Without Supercomputers

This study presents a method for generating Digital Elevation Models (DEM) using Mars Express data, avoiding the need for supercomputer resources. The approach utilizes 771,601 samples produced by image matching in Cartesian geocentric coordinates (X, Y, Z). Data preparation included coordinate transformations and segmentation into manageable text files. The prediction and standard error maps were created using Kriging and exported to raster format. Finally, all subsets were combined into a single composite map through weighted averages, ensuring accurate elevation representation on Mars.

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Efficient DEM Generation from Mars Express Data Without Supercomputers

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  1. How to get the job done without a supercomputer DEM generation using Mars Express data By Jasper Van doninck

  2. Used data • 771601 samples produced by image matching • Cartesian geocentric coordinates: X, Y, Z • Standard deviations σX, σY, σZ

  3. Data Preparation • Coordinate transformation: 10 iterations

  4. Data Preparation (2) • Mars Reference spheroid: IAU/IAG 2000 a=3396190 m b=3376200 m e=(a2-b2)/a2 • Study area: λ≈48˚50’ W - 47˚35’ W ψ≈0˚15’ S - 13˚37’ N ≈ 74 km x 822 km

  5. Data Preparation (3) -Partitioning of dataset into 8 tab delimited .txt files λ ψ H (σH) 7 files of 100 000 records 1 file of 71 601 records -Load data in Acces – import as XY-data in ArcGIS, convert to shapefiles

  6. Kriging • Create Prediction Map + Prediction Standard Error Map for every subset • Export prediction maps + standard error maps to raster • 1arcmin grid, block interpolation 12*12 cels (≈82m)

  7. Final Map Generation -Combine subsets to 1 map: Weighted average Vr,c= (Σ Vr,c,i/Er,c,i)/(Σ 1/Er,c,i) Vr,c = Predicted height of cel r,c Vr,c,i = Predicted height of cel in subset i Er,c,i = Prediction standard error in subset i

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