MOON DATA: OSSF CERTIFICATION CANDIDATES & APPS

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 We have derived new high-resolution DEMs of several regions (Sites 1, 4, 7, 11) surrounding high-priority lunar south pole landing sites using exclusively laser altimetry data acquired by LRO-LOLA. By iteratively co-adjusting the LOLA tracks in a self-consistent fashion, we reduced the orbital errors by over a factor of 10 such that the new track geolocation uncertainty is ~10 - 20 cm horizontally and ~2 - 4 cm vertically over each region. The new 5 m/pix LDEMs are substantially more realistic than the previous ones with fewer artifacts due to orbital errors and fewer spurious noise points. While the fraction of interpolated 5-m pixels in these polar LDEMs is necessarily large (~90%) due to LOLA's cross-track and inter-spot spacing, these LDEMs have the advantage of having accurate geodetic control and of being unaffected by shadows, and, thus, will be useful constraints on higher-resolution topographic models derived from imagery.

We developed a method to estimate surface height uncertainty in the new LDEMs that accounts for the reduced orbital errors and interpolation errors. This method circumvents the infeasible computation of the full error-covariance matrix of the LDEM. Instead, we use the fractal nature of lunar topography to build a more computationally manageable statistical ensemble of clones with similar error properties as the data. We show how we use this ensemble to study height and slope uncertainty, as well as the uncertainty in illumination conditions. Such an approach can be useful for a range of other studies, not just pure illumination conditions, when examining the feasibility of potential landing sites.

The LDEM height and slope uncertainties within the RoIs are similar across all the sites with a median RMS Z error ~ 0.30 - 0.50 m and a median RMS slope error ~ 1.5 - 2.5°. Interpolation error depends primarily on gap size, or areal density of the LOLA points, with a secondary dependence on terrain slope that becomes more important over highly sloped terrain. Hence, the interpolation error will be larger at greater distances from the pole for the same pixel scale, because of the lower point density and poorer effective resolution. The illumination conditions within each region of interest (RoI) are impacted by height uncertainties and show more variation that can potentially be used as a guide to rank the different sites and RoIs within a site. Between Jan. 1, 2024 and Jan. 1, 2026, Site 1 has the most area with average illumination > 70% at 1 m above the surface and RoIs 4 - 6 at this site generally have the most favorable illumination conditions even considering their uncertainties. At 5 m above the surface, the effect of LDEM uncertainties on illumination conditions are significantly reduced, and Site 1 has longer continuous illumination periods than the other sites. 

https://pgda.gsfc.nasa.gov/products/78  

Audit Criteria Status Notes:

PDS4 Compliance⚠️ Partial Data set originates from LOLA mission (PDS-compliant), but improved DEMs on PGDA may not include formal PDS4 XML labels. Needs direct metadata check.

FAIR Compliance✅ Strong. Publicly accessible, properly referenced, DOI-supported, and hosted by a trusted NASA facility.

Metadata Completeness⚠️ Moderate. Spatial resolution, coordinate system, and illumination modeling described in publication, but unclear if structured metadata is machine-readable or standardized (pending access).

Spatial Reference Frame✅ Confirmed. Lunar South Pole, IAU 2000 Mean Earth/Polar Axis (ME) reference used per publication.

Simulation/AI Readiness⚠️ Conditional. 5 m/pixel DEMs are simulation-grade, but integration readiness depends on format (likely GeoTIFF or similar). No direct semantic/ontological tagging evident.

Open Licensing✅ Confirmed. US government data — public domain.

Citable Dataset with DOI✅ Yes. DOI: 10.1016/j.pss.2020.105119 

1. Obtain and Preprocess Lunar Datasets

🔹 Sub-steps:

• Ingest      topography (e.g., LOLA-derived DEMs)
• Ingest      surface reflectance and thermal IR data (e.g., Diviner)
• Ingest      neutron spectroscopy and epithermal flux data (e.g., LEND)
• Retrieve      shadow and illumination models for PSRs (Permanently Shadowed Regions)
• Harmonize      spatial resolutions and geodetic reference frames (e.g., to Mean      Earth/Polar Axis)

2. Coregister and Georeference Data Layers

🔹 Sub-steps:

• Align      datasets into a unified coordinate system (e.g., IAU Moon 2000)
• Resample      and clip data to region of interest (ROI)
• Correct      orbital distortions or sensor-induced shifts
• Validate      alignment using known lunar landmarks (e.g., Shackleton crater rim)

3. Derive Environmental and Physical Parameters

🔹 Sub-steps:

• Calculate      surface slope and roughness from DEMs
• Derive      thermal inertia and surface temperature cycles
• Estimate      hydrogen abundance from neutron flux data
• Extract      potential water ice indicators (e.g., enhanced hydrogen, low albedo, cold      traps)

4. Apply Data Fusion and Analytical Modeling

🔹 Sub-steps:

• Perform      raster layer arithmetic to score potential ice locations (e.g., fuzzy      logic or weighted overlays)
• Model      stability zones for ice based on thermal and solar data
• Run      probabilistic models for buried ice vs. surface frost
• Integrate      illumination, accessibility, and navigability criteria

5. Generate Prospecting Maps and Risk Layers

🔹 Sub-steps:

• Output      water resource probability zones (e.g., binary or graded)
• Map      safe traverse corridors for robotic missions
• Overlay      engineering constraints (e.g., slope < 15°, illumination > X%,      communication LoS)
• Create      metadata and provenance record for OSSIG certification

6. Validate and Certify Output Products

🔹 Sub-steps:

• Conduct      automated QA checks (e.g., metadata completeness, projection integrity)
• Submit      to OSSIG for AI audit
• Undergo      peer review by lunar science SMEs
• Upon      approval, publish certified map products via https://opensolarsystem.foundation

7. Deploy into Robotic Planning and Simulation Environments

🔹 Sub-steps:

• Import      maps into robotic autonomy software (e.g., for resource targeting and      hazard avoidance)
• Integrate      into Lunaverse for surface simulation and testing
• Generate      mission operation layers (e.g., “go/no-go” zones)
• Use      map-based telemetry replay for field validation (Lunar analogs)

Lunar Water Resource Prospecting Workflow (With Open-Source Tools):

1. Obtain and Preprocess Lunar Datasets

Function

Suggested Open-Source Tools

Access LOLA, LEND, Diviner data

USGS ISIS3, NASA PDS Toolkits, PGDA   Portal

Harmonize spatial projections

GDAL, ISIS3 maplab, QGIS Processing   Toolbox

Manage multi-mission formats

HDF5 tools, NetCDF tools, pds4-tools   (Python)

2. Coregister and Georeference Data Layers

Function

Suggested Tools

Reproject to IAU Moon 2000

GDAL, ISIS3, QGIS

Clip to ROI

GDAL, rasterio (Python), QGIS

Visual alignment checks

QGIS, JMARS, ISIS3 qview

Automate batch transforms

OGR/GDAL Python scripts, Snakemake

3. Derive Environmental and Physical Parameters

Function

Suggested Tools

Surface slope, roughness

GRASS GIS, SAGA GIS, GDAL DEM tools

Temperature modeling

Python (SciPy, NumPy), NASA HEAT code (public)

Neutron data analysis

Matplotlib, Pandas, scikit-learn

Ice stability models

Thermophysical modeling scripts, fuzzy logic in   Python

4. Apply Data Fusion and Analytical Modeling

Function

Suggested Tools

Weighted overlays

QGIS Raster Calculator, GRASS r.mapcalc

Fuzzy logic modeling

scikit-fuzzy, OpenCV, TensorFlow (if ML   used)

Probabilistic fusion

scikit-learn, PyMC, Bayesian modeling   packages

Rule-based geospatial filters

Python with rasterio + geopandas, GRASS

5. Generate Prospecting Maps and Risk Layers

Function

Suggested Tools

Map classification

QGIS, scikit-image, GRASS r.reclass

Engineering overlays

QGIS with terrain filters, Slope tools in GRASS

Metadata generation

pds4-tools, CKAN, Open Metadata Registry   (OMR)

Map packaging

GeoTIFF with sidecar XML, PDS4 bundles

6. Validate and Certify Output Products

Function

Suggested Tools

Metadata validation

pds4-validator, GDAL info tools, QGIS   metadata plug-ins

QA/QC checks

Snakemake test workflows, custom Python scripts,   GeoJSON validators

Peer review

Human process (managed via GitHub repos, Overleaf,   or Zenodo)

7. Deploy into Robotic Planning and Simulation Environments

Function

Suggested Tools

Load into Lunaverse

CesiumJS, 3D Tiles conversion from DEMs, Unreal   Engine (OSSF plugin)

Autonomy layer development

ROS (Robot Operating System), NASA F´ Framework,   OpenCV

Simulation environment testing

Gazebo, Ignition Robotics, Moonbase Sim   environments

Public portal access

GeoServer, Leaflet, OpenLayers, Cesium   ion (open options)