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Planetary surface science increasingly depends on quantitative topographic data derived from orbital stereo imagery. I produce regional-scale digital terrain models (DTMs) from Mars Context Camera (CTX) stereo pairs using independently developed processing workflows built on a customized deployment of the NASA Ames Stereo Pipeline.

This capability supports investigations across multiple volcanic provinces on Mars:

Alba Mons — Mapping the coupled evolution of effusive volcanism and fluvial incision on a large low-relief shield. My work documents watershed-scale valley systems and lava tube networks that revise interpretations of Amazonian surface activity in northern Tharsis. (Scheidt, Crown & Berman, 2025, Earth and Space Science; Crown, Scheidt & Berman, 2022, JGR-Planets)

Hellas Basin Southern Rim — Topographic analysis of highland patera volcanoes (Tyrrhenus Mons, Hadriacus Mons, Amphitrites Patera) where volcanic surfaces have been modified by fluvial, cryogenic, and impact processes across billions of years.

Tharsis Lava Flow Fields — CTX DTMs that resolve pahoehoe lava tubes, inflation plateaus, and breakout fields, enabling quantitative assessment of emplacement mechanics and interactions with pre-existing terrain.

Medusae Fossae Formation — Earlier mapping work established Hesperian ages for large portions of this enigmatic formation, substantially revising previous chronologies. (Zimbelman & Scheidt, 2012, Science, 65 citations)

The processing infrastructure behind this work — a local high-performance computing capability running autonomous CTX stereo pipelines — is itself a research product, enabling sustained DTM production without dependence on institutional HPC allocations.

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Terrestrial analog studies provide essential constraints on the relationships between physical processes and surface expression that cannot be resolved from orbital data alone. I treat Earth surfaces as quantitative process laboratories, emphasizing direct measurement and repeat observation to calibrate planetary interpretations.

Volcanic Systems

Field campaigns span multiple basaltic systems across diverse environments:

Iceland — Repeated campaigns at Askja, Holuhraun, Katla, and the Reykjanes Peninsula eruptions (Litli-Hrútur, Meradalir, Geldingadalir). Multi-year UAS surveys document lava flow emplacement, margin evolution, vent degradation, and surface roughness development. These datasets directly inform interpretation of Martian lava flows. (Whelley et al. 2025; Sutton et al., 2024, Bull. Volcanology; Voigt et al., 2021, JVGR; Bonnefoy et al., 2019, JVGR)

Hawaii — My earliest work at Kīlauea was in 2010, but it wasn’t until 2014-2015 that I produced an ultrahigh-resolution DTM for lava flow characterization using a tethered aerospace system (a kite and camera!). In 2022, I designed and built a novel UV-induced fluorescence (UVIF) imaging system for lava tube exploration at Mauna Loa, later featured in NASA’s Our Alien Earth video series.

Bishop Tuff, California — UAS-derived DTMs of welded ignimbrite deposits providing analog data for explosive volcanic stratigraphy on Mars, particularly the Medusae Fossae Formation.

Aeolian Systems

Building on graduate and postdoctoral research on terrestrial dune dynamics, I have conducted field investigations of coarse-grained gravel ripples near Askja, monitoring migration rates over six years using GNSS-controlled repeat UAS surveys. This work quantifies aerodynamic roughness and bedform stability under conditions relevant to Mars. (Zimbelman & Scheidt, 2014, Icarus; Scheidt & Lancaster, 2013, ESPL; Scheidt et al., 2010, JGR-Earth Surface)

Custom Instrumentation

Across these campaigns, I independently design, build, and deploy field instrumentation including UAS-mounted vector magnetometers, specialized camera systems, UV illumination rigs, and field data loggers — enabling end-to-end execution of planetary analog investigations.

Data Stewardship

To date, I have collected approximately 80–100 UAS-derived DTM datasets. Twelve DOI-registered datasets have been archived through the U.S. Geological Survey, University of Arizona, and University of Maryland repositories, with additional datasets under analysis and ready for future release for community use.

→ Public Data Archive

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III. Synthetic Terrain Modeling

Exploration systems — terrain-relative navigation, hazard detection, and landing simulations — require terrain representations with characterized uncertainties at spatial scales that do not yet exist from orbital data. I address this gap by constructing geologically constrained synthetic surfaces from measured field data.

LuNaMaps

Through the Lunar Navigation Maps (LuNaMaps) project at NASA Goddard Space Flight Center, I produce realistic, validated high-resolution synthetic topography for the lunar South Polar region. This work uses:

• Field-derived UAS DTMs of analog crater fields (including Apollo-era and modern simulated craters at Cinder Lake and Mojave Spaceport)
• A library of three-dimensional digital rocks built from terrestrial scanning of natural clasts
• Measured rock and crater size-frequency distributions to populate terrain models

These synthetic terrains are now used in Artemis astronaut training and simulation activities to evaluate terrain-relative navigation, hazard detection and avoidance, and surface interaction. (Scheidt et al., 2025, AAS GN&C Conference; Restrepo et al., 2022, AIAA SCITECH, 8 citations)

Exploration Field Campaigns

Field campaigns conducted under NASA GSFC GIFT, RIS4E, RISE2, ARDVARC, RAVEN, SSW, LuNaMaps and others have produced terrain products used to evaluate navigation robustness and hazard detectability at exploration-relevant spatial scales. Recent work includes GNSS-controlled nighttime UAS orthomosaics acquired under simulated lunar South Pole illumination for ARDVARC rover operations.

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IV. Surface Intelligence

Computational methods extend the reach of surface-process analysis beyond what manual interpretation can achieve.

Autonomous Processing Pipelines

The CTX stereo-processing pipeline operates autonomously on local high-performance hardware, enabling sustained production of science-quality DTMs without manual intervention for each stereo pair. This infrastructure is designed to be documented and reproducible.

Machine Learning for Landform Detection

Earlier collaborative work demonstrated automated detection of geological landforms on Mars using convolutional neural networks, among the first applications of deep learning to planetary geomorphology. (Palafox, Hamilton, Scheidt & Alvarez, 2016, Computers & Geosciences, 171 citations)

Boundary-Layer Physics Visualization

The Surface Response Engine is an interactive tool for exploring how wind interacts with terrain and surface roughness under different planetary atmospheres (Earth, Mars, Venus), designed to communicate boundary-layer physics and geomorphic response.

Multi-System Compute Workflows

I maintain and document practical workflows for planetary geospatial processing across server-class, desktop, and mobile hardware — addressing the reality that independent researchers must assemble and optimize their own computational environments.

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Exploration Context

This research program operates within NASA’s Moon-to-Mars framework. Surface-process science, analog field measurements, and terrain modeling feed directly into:

• Artemis landing site characterization, astronaut training, and terrain-relative navigation
• Mars science through orbital analysis and analog-constrained interpretations
• DAVINCI mission preparation through analog terrain imaging studies
• Open science through public data archives, documented workflows, and community-facing datasets