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

We demonstrate an end-to-end application of the in-house deep learning-based surface modelling system, called MADNet, to produce three large area 3D mapping products from single images taken from the ESA Mars Express’s High Resolution Stereo Camera (HRSC), the NASA Mars Reconnaissance Orbiter’s Context Camera (CTX), and the High Resolution Imaging Science Experiment (HiRISE) imaging data over the ExoMars 2022 Rosalind Franklin rover’s landing site at Oxia Planum on Mars. MADNet takes a single orbital optical image as input, provides pixelwise height predictions, and uses a separate coarse Digital Terrain Model (DTM) as reference, to produce a DTM product from the given input image. Initially, we demonstrate the resultant 25 m/pixel HRSC DTM mosaic covering an area of 197 km × 182 km, providing fine-scale details to the 50 m/pixel HRSC MC-11 level-5 DTM mosaic. Secondly, we demonstrate the resultant 12 m/pixel CTX MADNet DTM mosaic covering a 114 km × 117 km area, showing much more detail in comparison to photogrammetric DTMs produced using the open source in-house developed CASP-GO system. Finally, we demonstrate the resultant 50 cm/pixel HiRISE MADNet DTM mosaic, produced for the first time, covering a 74.3 km × 86.3 km area of the 3-sigma landing ellipse and partially the ExoMars team’s geological characterisation area. The resultant MADNet HiRISE DTM mosaic shows fine-scale details superior to existing Planetary Data System (PDS) HiRISE DTMs and covers a larger area that is considered difficult for existing photogrammetry and photoclinometry pipelines to achieve, especially given the current limitations of stereo HiRISE coverage. All of the resultant DTM mosaics are co-aligned with each other, and ultimately with the Mars Global Surveyor’s Mars Orbiter Laser Altimeter (MOLA) DTM, providing high spatial and vertical congruence. In this paper, technical details are presented, issues that arose are discussed, along with a visual evaluation and quantitative assessments of the resultant DTM mosaic products.

Mars3DNet: CNN-Based High-Resolution 3D Reconstruction of the Martian Surface from Single Images

Three-dimensional (3D) surface models, e.g., digital elevation models (DEMs), are important for planetary exploration missions and scientific research. Current DEMs of the Martian surface are mainly generated by laser altimetry or photogrammetry, which have respective limitations. Laser altimetry cannot produce high-resolution DEMs; photogrammetry requires stereo images, but high-resolution stereo images of Mars are rare. An alternative is the convolutional neural network (CNN) technique, which implicitly learns features by assigning corresponding inputs and outputs. In recent years, CNNs have exhibited promising performance in the 3D reconstruction of close-range scenes. In this paper, we present a CNN-based algorithm that is capable of generating DEMs from single images; the DEMs have the same resolutions as the input images. An existing low-resolution DEM is used to provide global information. Synthetic and real data, including context camera (CTX) images and DEMs from stereo High-Resolution Imaging Science Experiment (HiRISE) images, are used as training data. The performance of the proposed method is evaluated using single CTX images of representative landforms on Mars, and the generated DEMs are compared with those obtained from stereo HiRISE images. The experimental results show promising performance of the proposed method. The topographic details are well reconstructed, and the geometric accuracies achieve root-mean-square error (RMSE) values ranging from 2.1 m to 12.2 m (approximately 0.5 to 2 pixels in the image space). The experimental results show that the proposed CNN-based method has great potential for 3D surface reconstruction in planetary applications.