NASA · NASA-708 · record R0393

Global, Seasonal Mars Frost Maps

National Aeronautics and Space Administration · JPL: Jet Propulsion Laboratory

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2024-11-01 (day precision)Source: 11/01/2024 00:00:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Global frost Martian maps derived from five remote sensing datasets and processed with tools like CNNs and other data science techniques. Publicly available on JMARS

Expected benefits

(1) Mars seasonal frost maps that can be used by other researchers, enabling more time on analysis rather than data collection. (2) Demonstration of a deeply collaborative effort between data scientists and physical scientists.

System outputs

Near-global maps showing detections of seasonal frost in visible datasets (HiRISE, CTX) with associated uncertainties.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: Not reported
Training and evaluation data: Visible images acquired from orbit of the martian surface. Some images were human-labeled, then used in training, validation, and testing of the CNN.
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported