3d Multi-Resolution Mapping of Mars Using Casp-Go on Hrsc, Crism, CTX and Hirise

Abstract

Abstract. Automated large-scale retrieval of stereo photogrammetric DTMs of Mars fall into three categories: use of COTS software such as BAE-SOCET®; private software such as the DLR-VICAR software suite and open source solutions such as the NASA Ames Stereo Pipeline (ASP). We describe here a novel open source system developed on the back of ASP known as CASP-GO (Tao et al., 2018) which has automated and extended ASP to be able to be applied to all modern single-pass or repeat-pass stereo photogrammetric datasets from 21st century systems such as HRSC, CTX and HiRISE, CASP-GO also includes an automated co-registration system which employs HRSC (itself linked to MOLA) as the base-map upon which all other DTMs are co-registered. We show an example here of this automated co-registration system applied to multi-resolution stacks including CRISM images. Several thousand multi-resolution 3D products, Digital Terrain Models (DTMs) and their corresponding orthorectified images (ORIs) have been generated and used in a wide variety of scientific studies, a few examples of which are shown here. Finally, a new method distributing these products providing long-term archiving and ease of access using DOIs is shown employing the ESA-PSA Guest Storage Facility and their corresponding display within the iMars webGIS system.

DeepLandforms: A Deep Learning Computer Vision Toolset Applied to a Prime Use Case for Mapping Planetary Skylights

“…These surface processesinclude skylight, pits (Barlow et al., 2017; Cushing, 2017; Cushing et al., 2015; Haruyama et al., 2009; Michikami et al., 2014; Sharma & Srivastava, 2021; Wagner & Robinson, 2014; Wyrick, 2004; Xiao et al., 2014), and pit chains (Wyrick, 2004). Further works involve the application of advanced techniques, such as Structure‐from‐Motion, (Le Mouélic et al., 2020; Micheletti et al., 2015; Muller et al., 2021; Y. Wang et al., 2021), Shape‐from‐Shading (SfS) (Alexandrov & Beyer, 2018; Lohse et al., 2006) and Machine Learning for object detection and classification (Barrett et al., 2022; Dundar et al., 2019; Hipperson et al., 2020; Hu et al., 2021; Lee, 2019; Nodjoumi et al., 2021; Palafox et al., 2017; Rashno et al., 2017; Silburt et al., 2019; Wilhelm et al., 2020).…”

DeepLandforms: A Deep Learning Computer Vision toolset applied to a prime use case for mapping planetary skylights

“…These surface processesinclude skylight, pits (Barlow et al, 2017;Cushing, 2017;Cushing et al, 2015;Haruyama et al, 2009;Michikami et al, 2014;Sharma & Srivastava, 2021;Wagner & Robinson, 2014;Wyrick, 2004;Xiao et al, 2014), and pit chains (Wyrick, 2004). Further works involve the application of advanced techniques, such as Structure-from-Motion, (Le Mouélic et al, 2020;Micheletti et al, 2015;Muller et al, 2021;, Shape-from-Shading (SfS) (Alexandrov & Beyer, 2018;Lohse et al, 2006) and Machine Learning for object detection and classification (Barrett et al, 2022;Dundar et al, 2019;Hipperson et al, 2020;Hu et al, 2021;Lee, 2019;Nodjoumi et al, 2021;Palafox et al, 2017;Rashno et al, 2017;Silburt et al, 2019;Wilhelm et al, 2020).…”