Planetary Mapping Using Deep Learning: A Method to Evaluate Feature Identification Confidence Applied to Habitats in Mars-Analog Terrain.

The goals of Mars exploration are evolving beyond describing environmental habitability at global and regional scales to targeting specific locations for biosignature detection, sample return, and eventual human exploration. An increase in the specificity of scientific goals-from follow the water to find the biosignatures-requires parallel developments in strategies that translate terrestrial Mars-analog research into confident identification of rover-explorable targets on Mars. Precisely how to integrate terrestrial, ground-based analyses with orbital data sets and transfer those lessons into rover-relevant search strategies for biosignatures on Mars remains an open challenge. Here, leveraging small Unmanned Aerial System (sUAS) technology and state-of-the-art fully convolutional neural networks for pixel-wise classification, we present an end-to-end methodology that applies Deep Learning to map geomorphologic units and quantify feature identification confidence. We used this method to assess the identification confidence of rover-explorable habitats in the Mars-analog Salar de Pajonales over a range of spatial resolutions and found that spatial resolutions two times better than are available from Mars would be necessary to identify habitats in this study at the 1-σ (85%) confidence level. The approach we present could be used to compare the identifiability of habitats across Mars-analog environments and focus Mars exploration from the scale of regional habitability to the scale of specific habitats. Our methods could also be adapted to map dome- and ridge-like features on the surface of Mars to further understand their origin and astrobiological potential.

Corrigendum: Subsurface Microbial Habitats in an Extreme Desert Mars-Analog Environment.

[This corrects the article DOI: 10.3389/fmicb.2019.00069.].

Prokaryotic Community Structure and Metabolisms in Shallow Subsurface of Atacama Desert Playas and Alluvial Fans After Heavy Rains: Repairing and Preparing for Next Dry Period.

The Atacama Desert, the oldest and driest desert on Earth, displays significant rains only once per decade. To investigate how microbial communities take advantage of these sporadic wet events, we carried out a geomicrobiological study a few days after a heavy rain event in 2015. Different physicochemical and microbial community analyses were conducted on samples collected from playas and an alluvial fan from surface, 10, 20, 50, and 80 cm depth. Gravimetric moisture content peaks were measured in 10 and 20 cm depth samples (from 1.65 to 4.1% w/w maximum values) while, in general, main anions such as chloride, nitrate, and sulfate concentrations increased with depth, with maximum values of 13-1,125; 168-10,109; and 9,904-30,952 ppm, respectively. Small organic anions such as formate and acetate had maximum concentrations from 2.61 to 3.44 ppm and 6.73 to 28.75 ppm, respectively. Microbial diversity inferred from DNA analysis showed Actinobacteria and Alphaproteobacteria as the most abundant and widespread bacterial taxa among the samples, followed by Chloroflexi and Firmicutes at specific sites. Archaea were mainly dominated by Nitrososphaerales, Methanobacteria, with the detection of other groups such as Halobacteria. Metaproteomics showed a high and even distribution of proteins involved in primary metabolic processes such as energy production and biosynthetic pathways, and a limited but remarkable presence of proteins related to resistance to environmental stressors such as radiation, oxidation, or desiccation. The results indicated that extra humidity in the system allows the microbial community to repair, and prepare for the upcoming hyperarid period. Additionally, it supplies biomarkers to the medium whose preservation potential could be high under strong desiccation conditions and relevant for planetary exploration.

Subsurface Microbial Habitats in an Extreme Desert Mars-Analog Environment.

Sediments in the hyper-arid core of the Atacama Desert are a terrestrial analog to Mars regolith. Understanding the distribution and drivers of microbial life in the sediment may give critical clues on how to search for biosignatures on Mars. Here, we identify the spatial distribution of highly specialized bacterial communities in previously unexplored depth horizons of subsurface sediments to a depth of 800 mm. We deployed an autonomous rover in a mission-relevant Martian drilling scenario with manual sample validation. Subsurface communities were delineated by depth related to sediment moisture. Geochemical analysis indicated soluble salts and minerology that influenced water bio-availability, particularly in deeper sediments. Colonization was also patchy and uncolonized sediment was associated with indicators of extreme osmotic challenge. The study identifies linkage between biocomplexity, moisture and geochemistry in Mars-like sediments at the limit of habitability and demonstrates feasibility of the rover-mounted drill for future Mars sample recovery.

Spatial Spectroscopic Models for Remote Exploration.

Ancient hydrothermal systems are a high-priority target for a future Mars sample return mission because they contain energy sources for microbes and can preserve organic materials (Farmer, 2000 ; MEPAG Next Decade Science Analysis Group, 2008 ; McLennan et al., 2012 ; Michalski et al., 2017 ). Characterizing these large, heterogeneous systems with a remote explorer is difficult due to communications bandwidth and latency; such a mission will require significant advances in spacecraft autonomy. Science autonomy uses intelligent sensor platforms that analyze data in real-time, setting measurement and downlink priorities to provide the best information toward investigation goals. Such automation must relate abstract science hypotheses to the measurable quantities available to the robot. This study captures these relationships by formalizing traditional "science traceability matrices" into probabilistic models. This permits experimental design techniques to optimize future measurements and maximize information value toward the investigation objectives, directing remote explorers that respond appropriately to new data. Such models are a rich new language for commanding informed robotic decision making in physically grounded terms. We apply these models to quantify the information content of different rover traverses providing profiling spectroscopy of Cuprite Hills, Nevada. We also develop two methods of representing spatial correlations using human-defined maps and remote sensing data. Model unit classifications are broadly consistent with prior maps of the site's alteration mineralogy, indicating that the model has successfully represented critical spatial and mineralogical relationships at Cuprite. Key Words: Autonomous science-Imaging spectroscopy-Alteration mineralogy-Field geology-Cuprite-AVIRIS-NG-Robotic exploration. Astrobiology 18, 934-954.

Novel microbial diversity retrieved by autonomous robotic exploration of the world's deepest vertical phreatic sinkhole.

The deep phreatic thermal explorer (DEPTHX) is an autonomous underwater vehicle designed to navigate an unexplored environment, generate high-resolution three-dimensional (3-D) maps, collect biological samples based on an autonomous sampling decision, and return to its origin. In the spring of 2007, DEPTHX was deployed in Zacatón, a deep (approximately 318 m), limestone, phreatic sinkhole (cenote) in northeastern Mexico. As DEPTHX descended, it generated a 3-D map based on the processing of range data from 54 onboard sonars. The vehicle collected water column samples and wall biomat samples throughout the depth profile of the cenote. Post-expedition sample analysis via comparative analysis of 16S rRNA gene sequences revealed a wealth of microbial diversity. Traditional Sanger gene sequencing combined with a barcoded-amplicon pyrosequencing approach revealed novel, phylum-level lineages from the domains Bacteria and Archaea; in addition, several novel subphylum lineages were also identified. Overall, DEPTHX successfully navigated and mapped Zacatón, and collected biological samples based on an autonomous decision, which revealed novel microbial diversity in a previously unexplored environment.